CN110096036A - A kind of determination method, device and equipment of equipment state - Google Patents
A kind of determination method, device and equipment of equipment state Download PDFInfo
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Abstract
The application provides a kind of determination method, device and equipment of equipment state, this method comprises: obtaining the corresponding product data of product of production equipment production；Obtain the sensing data of sensor acquisition relevant to the production equipment；According to the product data and the sensing data, the corresponding characteristic of the production equipment is obtained；The corresponding equipment state of the production equipment is determined according to the characteristic.By the technical solution of the application, the equipment state of production equipment is detected in time, the equipment state of production equipment can be assessed, can predict whether production equipment breaks down, failure is repaired in time, guarantee product quality.
Description
Technical Field
The present application relates to the field of communications technologies, and in particular, to a method, an apparatus, and a device for determining a device status.
Background
In the field of industrial production, the production of a product is a composition of a series of operations and steps, and each production link is a result of the influence of various factors. In the industrial production field, "human, machine, material, method, ring" are five main factors of the production process, human refers to operator, machine refers to production equipment (also referred to as production machine), material refers to production material, method refers to production process (also referred to as production method), and ring refers to production environment.
The production equipment is an important component of the five factors, and has great influence on the production result. If the production equipment is abnormal, the quality problem of the product can be caused, and even the production of the product fails. Therefore, there is a need for a timely detection of the status of the production equipment, and there is currently no effective detection means for the status of the production equipment.
Disclosure of Invention
The application provides a method for determining equipment state, which comprises the following steps:
acquiring product data corresponding to a product produced by production equipment;
acquiring sensor data collected by a sensor associated with the production equipment;
acquiring characteristic data corresponding to the production equipment according to the product data and the sensor data;
and determining the equipment state corresponding to the production equipment according to the characteristic data.
The application provides a method for determining equipment state, which comprises the following steps:
acquiring sensor data collected by a sensor associated with a particular type of device;
acquiring characteristic data corresponding to the specific type of equipment according to the sensor data;
and determining the equipment state corresponding to the equipment of the specific type according to the characteristic data.
The application provides a device for determining the state of equipment, the device comprises:
the acquisition module is used for acquiring product data corresponding to products produced by the production equipment; acquiring sensor data collected by a sensor associated with the production equipment; acquiring characteristic data corresponding to the production equipment according to the product data and the sensor data;
and the determining module is used for determining the equipment state corresponding to the production equipment according to the characteristic data.
The present application provides an analysis device for determining a device state, comprising: the processor is used for acquiring product data corresponding to products produced by the production equipment; acquiring sensor data collected by a sensor associated with the production equipment; acquiring characteristic data corresponding to the production equipment according to the product data and the sensor data; and determining the equipment state corresponding to the production equipment according to the characteristic data.
Based on the technical scheme, in the embodiment of the application, the characteristic data corresponding to the production equipment can be obtained according to the product data and the sensor data, and the equipment state corresponding to the production equipment is determined according to the characteristic data, so that the equipment state of the production equipment can be detected in time, the equipment state of the production equipment can be evaluated, whether the production equipment breaks down or not can be predicted, the fault can be repaired in time, and the product quality is ensured. The mode provides a modeling analysis mode based on sensor data, which is used for evaluating the equipment state of production equipment, improving the analysis capability of industrial production practitioners, quickly and accurately diagnosing the equipment state of the production equipment, even predicting the fault of the production equipment in advance, assisting the maintenance plan of the production equipment, maintaining the production equipment under the condition of not influencing normal production as much as possible, and promoting the benefit of industrial enterprises to be improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments of the present application or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art according to the drawings of the embodiments of the present application.
FIG. 1 is a flow diagram of a method for determining a status of a device in one embodiment of the present application;
FIG. 2 is a flow diagram of an embodiment of the present application;
FIG. 3 is a flow chart of a method of determining a status of a device in another embodiment of the present application;
fig. 4 is a block diagram of an apparatus for determining a device status according to an embodiment of the present application.
Detailed Description
The terminology used in the embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein is meant to encompass any and all possible combinations of one or more of the associated listed items.
It should be understood that although the terms first, second, third, etc. may be used in the embodiments of the present application to describe various information, the information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information can also be referred to as second information, and similarly, second information can also be referred to as first information, depending on the context, without departing from the scope of the present application.
The embodiment of the present application provides a method for determining a device status, which may be applied to an analysis device, which may include, but is not limited to, a PC (Personal Computer), a terminal device, a notebook Computer, a server, and the like, and the type of the analysis device is not limited. Referring to fig. 1, a flowchart of the method for determining the device status is shown, and the method may include the following steps:
step 101, obtaining product data corresponding to a product produced by a production device.
Step 102, sensor data collected by a sensor associated with the production facility is acquired.
And 103, acquiring characteristic data corresponding to the production equipment according to the product data and the sensor data.
And 104, determining the equipment state corresponding to the production equipment according to the characteristic data.
In an example, the execution sequence is only an example given for convenience of description, and in practical applications, the execution sequence between steps may also be changed, and the execution sequence is not limited. Moreover, in other embodiments, the steps of the respective methods do not have to be performed in the order shown and described herein, and the methods may include more or less steps than those described herein. Moreover, a single step described in this specification may be broken down into multiple steps for description in other embodiments; multiple steps described in this specification may be combined into a single step in other embodiments.
With respect to step 103, in an example, the process of "obtaining the corresponding characteristic data of the production equipment according to the product data and the sensor data" may include, but is not limited to, the following ways:
and associating the product data and the sensor data corresponding to the same time information, and acquiring the characteristic data corresponding to the production equipment by using the associated product data and sensor data. That is, the feature data corresponding to the production equipment, that is, the feature data corresponding to the product produced by the production equipment, can be acquired by using the product data and the sensor data corresponding to the same time information.
For the process of "associating product data and sensor data corresponding to the same time information", it may include: acquiring first time information from product data and acquiring second time information from sensor data; then, according to the first time information and the second time information, product data and sensor data corresponding to the same time information are determined, and the determined product data and sensor data are associated.
With respect to step 103, in an example, the process of "obtaining the corresponding characteristic data of the production equipment according to the product data and the sensor data" may include, but is not limited to, the following ways:
and selecting a corresponding specific model for the product, and processing the product data and the sensor data by using the specific model to obtain the characteristic data corresponding to the production equipment. Further, the specific model may include, but is not limited to: a time series model; alternatively, a regression model; alternatively, a tree structure model.
With respect to step 104, in an example, the process of "determining the device status corresponding to the production device according to the feature data" may include, but is not limited to, the following manners: judging whether the characteristic data is abnormal data; if the data is abnormal, the equipment state corresponding to the production equipment can be determined to be an abnormal state; if the data is not abnormal data, the equipment state corresponding to the production equipment can be determined to be a normal state.
In one example, the process of "determining whether the feature data is abnormal data" may include, but is not limited to, the following: judging whether the characteristic data is abnormal data or not according to a comparison result of the characteristic data and normal parameters; wherein the normal parameter is parameter information indicating that the production apparatus is normal. Or, in the second mode, whether the characteristic data is abnormal data or not is judged according to the comparison result of the characteristic data and the abnormal parameters; wherein the abnormality parameter is parameter information indicating abnormality of the production apparatus. Or, in a third mode, a plurality of feature data corresponding to a plurality of products produced by the production equipment are obtained, and whether abnormal data exist in the plurality of feature data is analyzed by using the contrast among the plurality of feature data.
For the third mode, in an example, the process of acquiring a plurality of feature data corresponding to a plurality of products produced by the production equipment may include: obtaining a plurality of production batches corresponding to a plurality of products produced by the production equipment; obtaining a plurality of characteristic data corresponding to a plurality of production batches through the mapping relation inquired by the plurality of production batches; wherein the mapping relationship is a mapping relationship between the production lot and the characteristic data.
Further, after "obtaining the feature data corresponding to the production equipment according to the product data and the sensor data", a production lot corresponding to the product may be obtained from the product data, and a mapping relationship between the production lot and the feature data is established. Based on the mapping relationship, the operation of inquiring the mapping relationship through the production batches to obtain the characteristic data corresponding to the production batches can be executed.
Regarding the third way, in an example, the process of "analyzing whether there is abnormal data in the plurality of feature data by using the contrast between the plurality of feature data" may include: if the specified type of feature data exists in the plurality of feature data, it may be determined that the specified type of feature data is abnormal data. Wherein the difference between the specified type of feature data and other feature data is greater than a preset threshold.
Based on the technical scheme, in the embodiment of the application, the characteristic data corresponding to the production equipment can be obtained according to the product data and the sensor data, and the equipment state corresponding to the production equipment is determined according to the characteristic data, so that the equipment state of the production equipment can be detected in time, the equipment state of the production equipment can be evaluated, whether the production equipment breaks down or not can be predicted, the fault can be repaired in time, and the product quality is ensured. The mode provides a modeling analysis mode based on sensor data, which is used for evaluating the equipment state of production equipment, improving the analysis capability of industrial production practitioners, quickly and accurately diagnosing the equipment state of the production equipment, even predicting the fault of the production equipment in advance, assisting the maintenance plan of the production equipment, maintaining the production equipment under the condition of not influencing normal production as much as possible, and promoting the benefit of industrial enterprises to be improved.
The above-mentioned scheme of the embodiment of the present application is described in detail below with reference to specific application scenarios.
In the field of industrial production, production equipment (such as automatic production equipment and the like) can be provided with one or more sensors, the sensors are used for collecting data in the production and processing process of the production equipment to obtain sensor data, the data collected by the sensors can be called as sensor data for distinguishing convenience, automatic control can be realized based on the sensor data, and the production process of the production equipment can be monitored and alarmed.
For example, a temperature sensor may be deployed at a production facility, which may collect temperature information for a production process. Alternatively, a pressure sensor may be deployed at the production facility, which may collect pressure information for the production process. Alternatively, a vibration sensor may be deployed at the production facility, which may collect impact force or acceleration information of the production process. Alternatively, a distance sensor may be deployed at the production facility, which may capture the object movement distance of the production process.
Of course, the sensors are only examples of the present application, and the data collected by the sensors, such as the temperature information, the pressure information, the moving distance of the object, and the like, are sensor data.
The embodiment of the application provides a method for determining the equipment state based on sensor data, which is a method for determining the equipment state based on the sensor data and oriented to the industrial production field, and can determine the characteristic data of production equipment through means such as data mining, mathematical modeling, machine learning and the like, determine the equipment state of the production equipment by using the characteristic data, predict whether the production equipment fails or not, and accordingly evaluate an equipment health Profile (machine health Profile) of the production equipment. For example, the probability of the future production equipment failure is predicted through modeling analysis of the existing production batch data, and the method is used for assisting the maintenance decision of the production equipment, improving the analysis capability of industrial production practitioners, quickly and accurately diagnosing the operation health state of the production equipment, and predicting the failure occurrence of the production equipment in advance, so that the maintenance of the production equipment can be performed under the condition that normal production is not influenced as much as possible, and the benefit of industrial enterprises is promoted to be improved.
In order to more clearly illustrate the method for determining the device state, an application scenario of the embodiment of the present application is introduced, and for convenience of description, a silicon wafer cutting process in the field of industrial production is taken as an example for explanation. Certainly, the silicon wafer cutting process is only an example in the field of industrial production, and is not limited to this, all application scenarios in which a sensor is deployed in production equipment may employ the above method for determining the equipment state, and the implementation flow thereof is similar to that of the silicon wafer cutting process, and in this application scenario, the silicon wafer cutting process is taken as an example for description.
In the silicon wafer cutting process, production equipment is used for cutting a silicon ingot into silicon wafers, and the silicon wafers are equivalent to products produced by the production equipment. During the cutting process, the production equipment can collect product data of the silicon wafer, and the product data can include but is not limited to one or any combination of the following: the acquisition time of the product data, the production batch of the silicon wafer, and the production schedule of the silicon wafer may also include other contents, which is not limited to this.
For example, the production facility collects product data 1-6, which may be as shown in Table 1, and then the production facility may store the product data in a database. Of course, in practical applications, the product data collected by the production equipment is much more than the product data in table 1, and table 1 is taken as an example for description. Every time a silicon wafer is cut by the production equipment, which is equivalent to the production of a product (namely, a silicon wafer), when a product is produced, a plurality of product data can be collected, for example, product data 1-product data 3 are product data for product 1, and product data 4-product data 6 are product data for product 2.
TABLE 1
| Product data identification | Time of acquisition | Production batch | Progress of production |
| Product data 1 | 2017.12.17-10:32:28 | 20171217001 | 10％ |
| Product data 2 | 2017.12.17-12:32:30 | 20171217001 | 60％ |
| Product data 3 | 2017.12.17-14:20:28 | 20171217001 | 90％ |
| Product data 4 | 2017.12.17-15:06:28 | 20171217002 | 5％ |
| Product data 5 | 2017.12.17-16:32:30 | 20171217002 | 40％ |
| Product data 6 | 2017.12.17-17:40:28 | 20171217002 | 95％ |
In one example, a sensor (e.g., a distance sensor) may be deployed at the production equipment, the distance sensor may collect the movement distance of the object during the production process, e.g., the sensor may be deployed at the cutting unit of the production equipment, and the sensor is used to collect the movement distance of the conical shaft (i.e., the conical shaft of the silicon wafer cutting machine), i.e., the movement distance of the conical shaft during the process of cutting the silicon ingot into silicon wafers by the cutting unit. For example, in the initial state, the moving distance of the taper shaft is 0, and the silicon ingot is not cut by the cutting means; with the cutting of the silicon ingot by the cutting component, the moving distance of the tapered shaft is larger and larger, and the sensor can acquire the moving distance of the tapered shaft, for example, the moving distance of the tapered shaft is 2 cm, 4 cm, and the like, which is not limited to this.
During the cutting process, the sensor may acquire sensor data, which may include, but is not limited to, one or any combination of the following: the acquisition time of the sensor data and the moving distance of the conical shaft. Of course, the sensor data may also include other content, and is not limited to this sensor data.
For example, the sensor collects sensor data 1-6, which may be as shown in Table 2, and then stores the sensor data in a database. Of course, in practical applications, the sensor data collected by the sensor will be much more than the sensor data in table 2, and table 2 will be taken as an example in the following. Every time a silicon wafer is cut by the production equipment, which is equivalent to the production of a product (namely, a silicon wafer), when a product is produced, a plurality of sensor data can be collected, for example, sensor data 1-sensor data 3 are sensor data for the product 1, and sensor data 4-sensor data 6 are sensor data for the product 2.
TABLE 2
Based on the product data (as shown in table 1) and the sensor data (as shown in table 2) stored in the database, the device status of the production device, such as the device status of the production device itself or the device status of a component (e.g., a cutting component) on the production device, can be determined. This process is explained in detail below.
Referring to fig. 2, a schematic diagram of a flow architecture in the embodiment of the present application is shown, where the architecture may include, but is not limited to: the system comprises a data layer, a preprocessing layer, a model characteristic layer, a configuration layer, a mining layer and an application layer. Wherein:
1. and (6) a data layer. The database may store sensor data, product data, MES (manufacturing execution System) data, quality verification data, process setting data, and the like, which are data widely used in the industrial production process, and the type of data in the database is not limited as long as the data includes the sensor data and the product data. At the data layer, sensor data and product data may be retrieved from a database and provided to the preprocessing layer.
The product data is corresponding to the product produced by the production equipment, and may be shown in table 1. The sensor data is sensor data collected by sensors associated with the production facility and may be as shown in table 2.
2. And (3) pretreating the layer. After the sensor data and the product data are obtained, the sensor data and the product data may be preprocessed (e.g., removing outliers, etc.) and associated.
Since the sensor data may include an abnormal value, it is determined whether or not the abnormal value exists in the sensor data after the sensor data is obtained. If so, the outlier in the sensor data is deleted, and if not, the outlier in the sensor data does not need to be deleted. For example, if the length of the final product (i.e., the silicon wafer) is 10 cm, the moving distance of the cone axis does not exceed 10 cm, and thus if the sensor data includes a moving distance greater than 10 cm, the sensor data is an abnormal value and needs to be deleted.
Since the product data may include an abnormal value, it is determined whether the abnormal value exists in the product data after the product data is obtained. If so, deleting the outlier in the product data, and if not, deleting the outlier in the product data. For example, the production schedule may not exceed 100%, and if the product data includes more than 100% production schedule, the product data is an abnormal value and needs to be deleted.
Here, since the product data includes time information (for the convenience of distinction, the time information in the product data is referred to as first time information, i.e., the collection time in table 1), and the sensor data includes time information (for the convenience of distinction, the time information in the sensor data is referred to as second time information, i.e., the collection time in table 2), the product data and the sensor data corresponding to the same time information may be associated with each other. Specifically, first time information is obtained from product data, second time information is obtained from sensor data, product data and sensor data corresponding to the same time information are determined according to the first time information and the second time information, and the determined product data and the determined sensor data are associated.
For example, the first time information of the product data 1 is 2017.12.17-10:32:28, and the second time information of the sensor data 1 is 2017.12.17-10:32:28, so that the product data 1 and the sensor data 1 correspond to the same time information "2017.12.17-10: 32: 28", that is, the product data 1 and the sensor data 1 may be associated. Similarly, product data 2 may be associated with sensor data 2, product data 3 may be associated with sensor data 3, product data 4 may be associated with sensor data 4, product data 5 may be associated with sensor data 5, and product data 6 may be associated with sensor data 6.
Further, the preprocessing layer can also provide the associated product data and sensor data (such as the association relationship between the product data 1 and the sensor data 1, the association relationship between the product data 2 and the sensor data 2, the association relationship between the product data 3 and the sensor data 3, and the like) to the model feature layer.
3. And (5) a model feature layer. After the associated product data and sensor data are obtained, the associated product data and sensor data can be utilized to obtain characteristic data corresponding to the production equipment. That is, the characteristic data corresponding to the production equipment can be acquired using the product data and the sensor data corresponding to the same time information.
Specifically, a corresponding specific model may be selected for the product, and the specific model may be used to process the associated product data and sensor data to obtain characteristic data corresponding to the production equipment. The specific model may include, but is not limited to: a time series model; alternatively, a regression model; alternatively, a tree structure model.
For example, the model feature layer may obtain an association relationship between product data 1 and sensor data 1, an association relationship between product data 2 and sensor data 2, an association relationship between product data 3 and sensor data 3, an association relationship between product data 4 and sensor data 4, an association relationship between product data 5 and sensor data 5, and an association relationship between product data 6 and sensor data 6. Then, since the product data 1, the product data 2, and the product data 3 correspond to the same production lot "20171217001", that is, the product data 1, the product data 2, and the product data 3 correspond to the same product (i.e., product 1), the product data 1 and the sensor data 1, the product data 2 and the sensor data 2, the product data 3, and the sensor data 3 may be used as the same set of input data, which is used to train the product data of the product 1. Similarly, product data 4 and sensor data 4, product data 5 and sensor data 5, product data 6 and sensor data 6 may be used as the same set of input data that is used to train product data for product 2.
Assuming that the specific model is a time series model, the product data 1 and the sensor data 1, the product data 2 and the sensor data 2, and the product data 3 and the sensor data 3 can be used as input data and output to the time series model, and the time series model performs modeling analysis and feature extraction according to the input data to obtain feature data a. Similarly, the product data 4 and the sensor data 4, the product data 5 and the sensor data 5, and the product data 6 and the sensor data 6 can also be output to the time series model, and the time series model performs modeling analysis and feature extraction according to the data to obtain feature data B.
Further, the characteristic data a and the characteristic data B are characteristic data corresponding to the production equipment.
The relevant definition of the time series model can be as follows: in production and scientific research, where a certain variable or group of variables is measured visually, and is arranged in time order at a series of time instants and used to interpret mathematical expressions of the variables and interrelationships, the resulting set of sequences of discrete numbers may be referred to as a time sequence, and such time-meaningful sequences are also referred to as dynamic data. Time series analysis is a theory and method for establishing a mathematical model through curve fitting and parameter estimation according to time series data obtained by system observation, and is generally performed by adopting a curve fitting and parameter estimation method (such as a nonlinear least square method).
Assuming that the specific model is a regression model, the product data 1 and the sensor data 1, the product data 2 and the sensor data 2, and the product data 3 and the sensor data 3 may be output to the regression model as input data, and the regression model performs modeling analysis and feature extraction based on the input data to obtain feature data a. Similarly, the product data 4 and the sensor data 4, the product data 5 and the sensor data 5, and the product data 6 and the sensor data 6 may be output to a regression model, and the regression model may perform modeling analysis and feature extraction according to these data, thereby obtaining feature data B.
Further, the characteristic data a and the characteristic data B are characteristic data corresponding to the production equipment.
The correlation definition of the regression model (also referred to as regression analysis model) can be as follows: regression analysis is used to study the specific dependencies of one variable (the explained variable) on another variable (the explaining variable), determine the degree of confidence of the mathematical relationships between the variables on these relationships, and find out which variables affect a particular variable significantly and which do not significantly, starting from a set of sample data. In addition, the value of one or more variables can be used to predict or control the value of another particular variable based on the value of that variable, and the accuracy of such prediction or control can be given.
Assuming that the specific model is a tree structure model, the product data 1 and the sensor data 1, the product data 2 and the sensor data 2, and the product data 3 and the sensor data 3 may be output to the tree structure model as input data, and the tree structure model performs modeling analysis and feature extraction according to the input data to obtain feature data a. Similarly, the product data 4 and the sensor data 4, the product data 5 and the sensor data 5, and the product data 6 and the sensor data 6 can be output to the tree structure model, and the tree structure model performs modeling analysis and feature extraction according to the data, so as to obtain feature data B.
Further, the characteristic data a and the characteristic data B are characteristic data corresponding to the production equipment.
Wherein, the related definition of the tree structure model can be as follows: a tree structure is an important non-linear data structure, which is a structure in which data elements (called nodes in the tree) are organized in a branching relationship.
In the above process, a processing manner of the time series model, the regression model, and the tree structure model is introduced, and as for the process of performing modeling analysis and feature extraction on the time series model, the regression model, and the tree structure model according to the input data, the present document is not limited, as long as the feature data can be obtained according to the input data.
For example, based on the time series model, assuming that the product data is taken as the abscissa and the sensor data is taken as the ordinate, the product data 1 and the sensor data 1 correspond to one coordinate point 1, the product data 2 and the sensor data 2 correspond to one coordinate point 2, and the product data 3 and the sensor data 3 correspond to one coordinate point 3. If the coordinate point 1, the coordinate point 2 and the coordinate point 3 form a straight line, the characteristic data may be a slope of the straight line, and after the time series model performs modeling analysis and characteristic extraction according to the input data, the obtained characteristic data is the slope of the straight line. If the coordinate point 1, the coordinate point 2 and the coordinate point 3 form a curve, the characteristic data may be a change rate of the curve, and the characteristic data obtained after the time series model performs modeling analysis and characteristic extraction according to the input data is the change rate of the curve. Of course, the feature data may be other contents, and the feature data of different models may be the same or different, which is not limited to this.
After the model characteristic layer obtains the characteristic data corresponding to the production equipment, the characteristic data corresponding to the production equipment can be provided to the mining layer. For example, through the above-described processing, the model feature layer may provide feature data a to the mining layer and may provide feature data B to the mining layer.
4. And configuring the layer. The user is provided with configuration options according to which the user enters relevant information.
For example, the configuration option may be a normal parameter, based on which the user may input a normal parameter of the product at the configuration layer according to the configuration option, and the normal parameter is parameter information indicating that the production equipment is normal, for example, the feature data X, and the like. Based on this, if the difference between the feature data a/feature data B and the feature data X is smaller than the threshold, the feature data a/feature data B is normal, that is, the device state of the production device is a normal state. If the difference between the feature data a/feature data B and the feature data X is not less than the threshold, the feature data a/feature data B is abnormal, that is, the equipment state of the production equipment is an abnormal state.
For example, the configuration option may be an abnormality parameter, based on which the user may input an abnormality parameter of the product at the configuration layer according to the configuration option, and the abnormality parameter is parameter information indicating an abnormality of the production equipment, for example, the feature data Y, or the like. Based on this, if the difference between the feature data a/feature data B and the feature data Y is smaller than the threshold, the feature data a/feature data B is abnormal, that is, the apparatus state of the production apparatus is an abnormal state. If the difference between the feature data a/feature data B and the feature data Y is not less than the threshold, the feature data a/feature data B is normal, that is, the equipment state of the production equipment is normal.
For example, the configuration option may be production lot information, based on which, the user can input the device information and the production lot at the configuration layer according to the configuration option, such as the device information a, the production lot "20171217001", and the production lot "20171217002", which indicate that the product corresponding to the production lot "20171217001" and the product corresponding to the production lot "20171217002" are all produced by the production device corresponding to the device information a.
Wherein, the device information may include, but is not limited to, one or any combination of the following: the device information is not limited to the device type, the device platform, and the device component, and only one production device can be uniquely represented based on the device information, that is, the corresponding production device can be known through the device information.
In summary, after obtaining the normal parameters, the configuration layer may provide the normal parameters to the mining layer. Or, after obtaining the abnormal parameters, the configuration layer may provide the abnormal parameters to the mining layer. Alternatively, the configuration layer may provide the corresponding relationship between the device information and the production lot to the mining layer after obtaining the corresponding relationship between the device information and the production lot. Of course, the above are just a few examples of configuration layers, and no limitation is made to this.
5. And excavating the layer. After obtaining the feature data corresponding to the production equipment (provided by the model feature layer) and the relevant information input by the user (provided by the configuration layer), the equipment state corresponding to the production equipment can be determined according to the feature data. Specifically, whether the characteristic data is abnormal data is judged; if so, determining that the equipment state corresponding to the production equipment is an abnormal state; if not, determining that the equipment state corresponding to the production equipment is a normal state.
For example, if the model feature layer provides feature data a/feature data B and the configuration layer provides normal parameters (e.g., feature data X), it may be determined whether the feature data a/feature data B is abnormal data according to a comparison result between the feature data a/feature data B and the normal parameters (e.g., feature data X). For example, if the difference between the feature data a/feature data B and the feature data X is smaller than the threshold, the feature data a/feature data B is determined to be normal data, and thus, the device status corresponding to the production device is determined to be a normal status. And if the difference value between the characteristic data A/the characteristic data B and the characteristic data X is not less than the threshold value, determining that the characteristic data A/the characteristic data B are abnormal data, and therefore determining that the equipment state corresponding to the production equipment is an abnormal state.
For example, if the model feature layer provides feature data a/feature data B and the configuration layer provides an abnormal parameter (e.g., feature data Y), it may be determined whether the feature data a/feature data B is abnormal data according to a comparison result between the feature data a/feature data B and the abnormal parameter (e.g., feature data Y). For example, if the difference between the feature data a/feature data B and the feature data Y is smaller than the threshold, the feature data a/feature data B is determined to be abnormal data, and thus, the device state corresponding to the production device is determined to be an abnormal state. And if the difference value between the characteristic data A/the characteristic data B and the characteristic data X is not less than the threshold value, determining that the characteristic data A/the characteristic data B are normal data, and therefore determining that the equipment state corresponding to the production equipment is a normal state.
For example, if the model feature layer provides feature data a/feature data B, and the configuration layer provides a correspondence between the device information and the production lot, a plurality of feature data corresponding to a plurality of products produced by the production device are obtained, and whether abnormal data exists in the plurality of feature data is analyzed by using a contrast between the plurality of feature data.
Because the product data may include a production lot, as shown in table 1, after the model feature layer acquires the feature data, the model feature layer may further acquire the production lot from the product data, and establish a mapping relationship between the production lot and the feature data. For example, the model feature layer may map feature data A to production lot 20171217001 and feature data B to production lot 20171217002. Based on this, the model feature layer provides the mapping relationship between the feature data A and the production lot 20171217001 and the mapping relationship between the feature data B and the production lot 20171217002 to the mining layer.
After receiving the related information input by the user, the configuration layer may provide the device information a, the production lot "20171217001", and the production lot "20171217002" input by the user to the mining layer.
In summary, the mining layer may obtain a mapping relationship between the feature data a and the production lot 20171217001; mapping relation between the characteristic data B and the production batch 20171217002; based on the correspondence relationship between the equipment information a, the production lot "20171217001" and the production lot "20171217002", the mining layer determines that the equipment information a corresponds to the production lot "20171217001" and the production lot "20171217002", and determines that the equipment information a corresponds to the feature data a and the feature data B, that is, the feature data a and the feature data B. Of course, the above is only an example, and in practical applications, the plurality of feature data corresponding to the device information a may be more, and the corresponding feature data a-feature data D are taken as an example.
The mining layer may then analyze the plurality of feature data for the presence of anomalous data using the contrast between the plurality of feature data. Specifically, if there is a specific type of feature data in the plurality of feature data, it may be determined that the specific type of feature data is abnormal data. Wherein the difference between the specified type of feature data and other feature data is greater than a preset threshold. For example, if the difference between the feature data a and the feature data B is greater than a preset threshold, the difference between the feature data a and the feature data C is greater than a preset threshold, and the difference between the feature data a and the feature data D is greater than a preset threshold, and the feature data B, the feature data C, and the feature data D are the same or similar, the feature data a is the specified type of feature data, that is, the abnormal data.
Of course, the above analysis method is only one example of analyzing whether there is abnormal data in the plurality of feature data, and the analysis method is not limited. For example, whether abnormal data exists in a plurality of characteristic data can be analyzed according to a correlation analysis method, and in general, the characteristic data of most products produced by a production device can be normal, and the characteristic data of a small part of products can be abnormal. Further, the feature data at the time front has a high probability of being normal, and the feature data at the time rear has a high probability of being abnormal.
6. And (5) an application layer. The method can predict whether the production equipment fails in the future by utilizing a plurality of characteristic data corresponding to the production equipment. Specifically, a plurality of characteristic data corresponding to the production equipment can be utilized to analyze index trends of the plurality of characteristic data, and the index trends are utilized to predict the health state of the production equipment, that is, whether the production equipment will fail at a future time is predicted, so that a health portrait for the production equipment is formed.
For example, the sequencing result of the feature data a-the feature data D according to the chronological order is: the characteristic data A, the characteristic data B, the characteristic data C and the characteristic data D are obtained, namely the time of the characteristic data A is the largest from the current time, and the time of the characteristic data D is the smallest from the current time. Although the feature data a, the feature data B, the feature data C and the feature data D are not abnormal data, the feature data a, the feature data B, the feature data C and the feature data D are closer to the abnormal condition, for example, if the abnormal standard is assumed to be 0.5, and the feature data a is 0.8, the feature data B is 0.7, the feature data C is 0.6 and the feature data D is 0.55, it is indicated that the feature data of the product produced by the production equipment is closer to the abnormal condition as the time advances, so that the probability of failure occurrence of the production equipment is predicted to be high at a certain time in the future, and the health image of the production equipment is likely to fail in the future. If the characteristic data A, the characteristic data B, the characteristic data C and the characteristic data D do not have the phenomenon that the distance between the characteristic data A, the characteristic data B, the characteristic data C and the characteristic data D is closer to the abnormal condition, the probability of failure of the production equipment at a certain time in the future is predicted to be small, and the probability of failure of the production equipment in the future is predicted to be small.
In one example, new process parameters can be given according to the state of the production equipment and recommended to a user, the user can use the new process parameters to produce, data formed in the production process is collected and used for later analysis, and the like, an online closed loop and an offline closed loop are formed, and virtuous circle is promoted.
Based on the same application concept as the method described above, an embodiment of the present application further provides a method for determining a device status, which is shown in fig. 3 and is a flowchart of the method, and the method may include the following steps:
step 301, sensor data collected by sensors associated with a particular type of device is acquired.
Step 302, obtaining characteristic data corresponding to the specific type of device according to the sensor data.
Step 303, determining the device status corresponding to the specific type of device according to the feature data.
The specific type of equipment may include, but is not limited to, production equipment (e.g., automated production equipment, etc.).
The process of acquiring the feature data corresponding to the specific type of device according to the sensor data may include, but is not limited to: and acquiring characteristic data corresponding to the production equipment according to the product data corresponding to the product produced by the production equipment and the sensor data acquired by the sensor related to the production equipment.
The implementation of steps 301 to 303 may be shown in fig. 1 or fig. 2, and is not described again.
Based on the same application concept as the method, an embodiment of the present application further provides an apparatus for determining a device status, as shown in fig. 4, which is a structural diagram of the apparatus for determining a device status, where the apparatus includes:
an obtaining module 401, configured to obtain product data corresponding to a product produced by a production device; acquiring sensor data collected by a sensor associated with the production equipment; acquiring characteristic data corresponding to the production equipment according to the product data and the sensor data;
a determining module 402, configured to determine a device state corresponding to the production device according to the feature data.
The obtaining module 401 is specifically configured to associate product data and sensor data corresponding to the same time information in a process of obtaining feature data corresponding to production equipment according to the product data and the sensor data; and acquiring characteristic data corresponding to the production equipment by using the associated product data and sensor data.
The obtaining module 401 is specifically configured to select a corresponding specific model for the product in the process of obtaining the feature data corresponding to the production equipment according to the product data and the sensor data, and process the product data and the sensor data by using the specific model to obtain the feature data corresponding to the production equipment.
The determining module 402 is specifically configured to determine whether the feature data is abnormal data in a process of determining an equipment state corresponding to the production equipment according to the feature data; if so, determining that the equipment state corresponding to the production equipment is an abnormal state; if not, determining that the equipment state corresponding to the production equipment is a normal state.
The determining module 402 is specifically configured to, in the process of determining whether the feature data is abnormal data, determine whether the feature data is abnormal data according to a comparison result between the feature data and a normal parameter; wherein the normal parameter is parameter information indicating that the production equipment is normal; or,
judging whether the characteristic data is abnormal data or not according to the comparison result of the characteristic data and the abnormal parameters; wherein the abnormality parameter is parameter information indicating abnormality of the production apparatus; or,
acquiring a plurality of characteristic data corresponding to a plurality of products produced by the production equipment; and analyzing whether abnormal data exist in the plurality of characteristic data or not by utilizing the contrast among the plurality of characteristic data.
Based on the same application concept as the method, the embodiment of the application also provides an analysis device for determining the device state, and the analysis device can comprise a processor; wherein: the processor is used for acquiring product data corresponding to products produced by the production equipment; acquiring sensor data collected by a sensor associated with the production equipment; acquiring characteristic data corresponding to the production equipment according to the product data and the sensor data; and determining the equipment state corresponding to the production equipment according to the characteristic data.
Based on the same application concept as the method, the embodiment of the present application further provides a machine-readable storage medium, where a plurality of computer instructions are stored on the machine-readable storage medium, and when executed, the computer instructions perform the following processes: acquiring product data corresponding to a product produced by production equipment; acquiring sensor data acquired by a sensor associated with production equipment; acquiring characteristic data corresponding to the production equipment according to the product data and the sensor data; and determining the equipment state corresponding to the production equipment according to the characteristic data.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. A typical implementation device is a computer, which may take the form of a personal computer, laptop computer, cellular telephone, camera phone, smart phone, personal digital assistant, media player, navigation device, email messaging device, game console, tablet computer, wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Furthermore, these computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (17)
1. A method for determining a device status, the method comprising:
acquiring product data corresponding to a product produced by production equipment;
acquiring sensor data collected by a sensor associated with the production equipment;
acquiring characteristic data corresponding to the production equipment according to the product data and the sensor data;
and determining the equipment state corresponding to the production equipment according to the characteristic data.
2. The method according to claim 1, wherein the process of obtaining the characteristic data corresponding to the production equipment according to the product data and the sensor data specifically comprises:
associating product data and sensor data corresponding to the same time information;
and acquiring characteristic data corresponding to the production equipment by using the associated product data and sensor data.
3. The method according to claim 2, wherein the process of associating the product data and the sensor data corresponding to the same time information specifically comprises:
acquiring first time information from product data and acquiring second time information from sensor data;
and determining product data and sensor data corresponding to the same time information according to the first time information and the second time information, and associating the determined product data and the sensor data.
4. The method according to claim 1 or 2, wherein the process of obtaining the characteristic data corresponding to the production equipment according to the product data and the sensor data specifically comprises:
and selecting a corresponding specific model for the product, and processing the product data and the sensor data by using the specific model to obtain characteristic data corresponding to the production equipment.
5. The method of claim 4, wherein the particular model comprises:
a time series model; alternatively, a regression model; alternatively, a tree structure model.
6. The method of claim 1,
the process of determining the equipment state corresponding to the production equipment according to the characteristic data comprises the following steps:
judging whether the characteristic data are abnormal data or not;
if so, determining that the equipment state corresponding to the production equipment is an abnormal state;
if not, determining that the equipment state corresponding to the production equipment is a normal state.
7. The method of claim 6,
the process of judging whether the feature data is abnormal data specifically includes:
judging whether the characteristic data is abnormal data or not according to the comparison result of the characteristic data and normal parameters; wherein the normal parameter is parameter information indicating that the production equipment is normal; or,
judging whether the characteristic data is abnormal data or not according to the comparison result of the characteristic data and the abnormal parameters; wherein the abnormality parameter is parameter information indicating abnormality of the production apparatus; or,
acquiring a plurality of characteristic data corresponding to a plurality of products produced by the production equipment; and analyzing whether abnormal data exist in the plurality of characteristic data or not by utilizing the contrast among the plurality of characteristic data.
8. The method of claim 7,
the process of obtaining a plurality of characteristic data corresponding to a plurality of products produced by the production equipment includes:
obtaining a plurality of production batches corresponding to a plurality of products produced by the production equipment;
obtaining a plurality of characteristic data corresponding to the plurality of production batches through the mapping relation inquired by the plurality of production batches; wherein the mapping relation is the mapping relation between the production batch and the characteristic data.
9. The method of claim 8, wherein after obtaining the corresponding characteristic data of the production equipment based on the product data and the sensor data, the method further comprises:
obtaining a production batch corresponding to the product from the product data;
and establishing a mapping relation between the production batch and the characteristic data.
10. The method of claim 7, wherein analyzing the plurality of feature data for the presence of abnormal data by using the contrast between the plurality of feature data comprises:
if the specified type of feature data exists in the plurality of feature data, determining that the specified type of feature data is abnormal data, wherein the difference between the specified type of feature data and other feature data is larger than a preset threshold value.
11. A method for determining a device status, the method comprising:
acquiring sensor data collected by a sensor associated with a particular type of device;
acquiring characteristic data corresponding to the specific type of equipment according to the sensor data;
and determining the equipment state corresponding to the equipment of the specific type according to the characteristic data.
12. An apparatus for determining a device status, the apparatus comprising:
the acquisition module is used for acquiring product data corresponding to products produced by the production equipment; acquiring sensor data collected by a sensor associated with the production equipment; acquiring characteristic data corresponding to the production equipment according to the product data and the sensor data;
and the determining module is used for determining the equipment state corresponding to the production equipment according to the characteristic data.
13. The apparatus according to claim 12, wherein the obtaining module is specifically configured to associate product data and sensor data corresponding to the same time information during the process of obtaining the characteristic data corresponding to the production equipment according to the product data and the sensor data; and acquiring characteristic data corresponding to the production equipment by using the associated product data and sensor data.
14. The apparatus according to claim 12 or 13, wherein the obtaining module is specifically configured to select a corresponding specific model for the product in a process of obtaining the feature data corresponding to the production equipment according to the product data and the sensor data, and process the product data and the sensor data by using the specific model to obtain the feature data corresponding to the production equipment.
15. The apparatus according to claim 12, wherein the determining module is specifically configured to determine whether the feature data is abnormal data in a process of determining a device state corresponding to the production device according to the feature data; if so, determining that the equipment state corresponding to the production equipment is an abnormal state; if not, determining that the equipment state corresponding to the production equipment is a normal state.
16. The apparatus of claim 15,
the determining module is specifically configured to, in a process of determining whether the feature data is abnormal data, determine whether the feature data is abnormal data according to a comparison result between the feature data and a normal parameter; wherein the normal parameter is parameter information indicating that the production equipment is normal; or,
judging whether the characteristic data is abnormal data or not according to the comparison result of the characteristic data and the abnormal parameters; wherein the abnormality parameter is parameter information indicating abnormality of the production apparatus; or,
acquiring a plurality of characteristic data corresponding to a plurality of products produced by the production equipment; and analyzing whether abnormal data exist in the plurality of characteristic data or not by utilizing the contrast among the plurality of characteristic data.
17. An analysis device for determining a state of a device, comprising: the processor is used for acquiring product data corresponding to products produced by the production equipment; acquiring sensor data collected by a sensor associated with the production equipment; acquiring characteristic data corresponding to the production equipment according to the product data and the sensor data; and determining the equipment state corresponding to the production equipment according to the characteristic data.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111090266A (en) * | 2019-12-04 | 2020-05-01 | 广州市高士实业有限公司 | Sealant preparation monitoring method and device, computer equipment and storage medium |
| CN111752251A (en) * | 2020-07-07 | 2020-10-09 | 临沂启阳电缆有限公司 | Intelligent control management system for factory workshop |
| CN112002403A (en) * | 2020-08-25 | 2020-11-27 | 上海至数企业发展有限公司 | Quantitative evaluation method, device and equipment for medical equipment and storage medium |
| CN112525247A (en) * | 2019-09-19 | 2021-03-19 | 山东东华水泥有限公司 | Method, device and equipment for detecting saturated wear state |
| CN113539909A (en) * | 2021-09-15 | 2021-10-22 | 深圳市信润富联数字科技有限公司 | Fault detection method and device, terminal equipment and storage medium |
| CN115993366A (en) * | 2023-03-24 | 2023-04-21 | 枣庄市大猫电子科技有限公司 | Workpiece surface detection method and system based on sensing equipment |
| CN119784256A (en) * | 2025-01-03 | 2025-04-08 | 南通迪顺衬布有限公司 | Abnormality monitoring method and device for textile equipment and storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030200060A1 (en) * | 2002-04-22 | 2003-10-23 | Evren Eryurek | On-line rotating equipment monitoring device |
| US20030229469A1 (en) * | 2002-06-07 | 2003-12-11 | Limin Song | Virtual RPM sensor |
| CN1476051A (en) * | 2002-07-15 | 2004-02-18 | ���µ�����ҵ��ʽ���� | Monitoring system and monitoring method for semiconductor manufacturing equipment |
| CN101118437A (en) * | 2007-09-03 | 2008-02-06 | 石毅 | New style numerically controlled machine remote condition monitoring and failure diagnosis system realizing method |
| CN102361014A (en) * | 2011-10-20 | 2012-02-22 | 上海大学 | State monitoring and fault diagnosis method for large-scale semiconductor manufacture process |
| CN102798534A (en) * | 2011-05-23 | 2012-11-28 | 松下电器产业株式会社 | Apparatus working state diagnostic method and apparatus working state diagnostic device |
| US20130218522A1 (en) * | 2010-10-28 | 2013-08-22 | Hideaki Suzuki | Abnormality diagnostic system and industrial machinery |
| CN103604622A (en) * | 2013-11-29 | 2014-02-26 | 北京普拉斯科技发展有限公司 | On-line monitoring and instant warning and fault diagnosis system of wind generating set |
-
2018
- 2018-01-29 CN CN201810082723.9A patent/CN110096036A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030200060A1 (en) * | 2002-04-22 | 2003-10-23 | Evren Eryurek | On-line rotating equipment monitoring device |
| US20030229469A1 (en) * | 2002-06-07 | 2003-12-11 | Limin Song | Virtual RPM sensor |
| CN1476051A (en) * | 2002-07-15 | 2004-02-18 | ���µ�����ҵ��ʽ���� | Monitoring system and monitoring method for semiconductor manufacturing equipment |
| CN101118437A (en) * | 2007-09-03 | 2008-02-06 | 石毅 | New style numerically controlled machine remote condition monitoring and failure diagnosis system realizing method |
| US20130218522A1 (en) * | 2010-10-28 | 2013-08-22 | Hideaki Suzuki | Abnormality diagnostic system and industrial machinery |
| CN102798534A (en) * | 2011-05-23 | 2012-11-28 | 松下电器产业株式会社 | Apparatus working state diagnostic method and apparatus working state diagnostic device |
| CN102361014A (en) * | 2011-10-20 | 2012-02-22 | 上海大学 | State monitoring and fault diagnosis method for large-scale semiconductor manufacture process |
| CN103604622A (en) * | 2013-11-29 | 2014-02-26 | 北京普拉斯科技发展有限公司 | On-line monitoring and instant warning and fault diagnosis system of wind generating set |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112525247A (en) * | 2019-09-19 | 2021-03-19 | 山东东华水泥有限公司 | Method, device and equipment for detecting saturated wear state |
| CN111090266A (en) * | 2019-12-04 | 2020-05-01 | 广州市高士实业有限公司 | Sealant preparation monitoring method and device, computer equipment and storage medium |
| CN111752251A (en) * | 2020-07-07 | 2020-10-09 | 临沂启阳电缆有限公司 | Intelligent control management system for factory workshop |
| CN112002403A (en) * | 2020-08-25 | 2020-11-27 | 上海至数企业发展有限公司 | Quantitative evaluation method, device and equipment for medical equipment and storage medium |
| CN113539909A (en) * | 2021-09-15 | 2021-10-22 | 深圳市信润富联数字科技有限公司 | Fault detection method and device, terminal equipment and storage medium |
| CN115993366A (en) * | 2023-03-24 | 2023-04-21 | 枣庄市大猫电子科技有限公司 | Workpiece surface detection method and system based on sensing equipment |
| CN119784256A (en) * | 2025-01-03 | 2025-04-08 | 南通迪顺衬布有限公司 | Abnormality monitoring method and device for textile equipment and storage medium |
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