JP7664089B2 - Predictive diagnosis device, predictive diagnosis method, and equipment management program - Google Patents

Description

The present invention relates to technology for diagnosing abnormalities in equipment.

Currently, technology has been proposed that performs predictive diagnosis from the operating data of home appliances, facility machinery, and other equipment (particularly electrical equipment) that is expected to operate while undergoing regular maintenance. This type of predictive diagnosis diagnoses whether the equipment is operating normally or if a predictive abnormality is occurring, and if it is determined that a predictive abnormality is occurring, then the predictive abnormality is reported.

An abnormal state here refers to a state in which the equipment is experiencing some kind of breakdown, deterioration, or malfunction, resulting in a decline in the performance or quality of the equipment. On the other hand, a predictive state refers to a state in which, although not an abnormal state, there are signs that the equipment is heading in the direction of becoming abnormal. It is expected that this type of predictive diagnosis will enable maintenance companies to carry out maintenance before breakdowns or deterioration become evident, thereby supporting the stable operation of the equipment.

For example, Patent Document 1 discloses a predictive diagnosis system that provides such predictive diagnosis. Patent Document 1 discloses that when the predictive diagnosis system detects a sign of a malfunction, it notifies a maintenance management device of the information about the sign.

Patent Document 2 also describes predicting an upcoming failure and notifying a service station terminal or a call center terminal, and further describes adjusting the response according to customer attribute information, thereby dealing with the detected symptoms without disrupting the scheduled processing of the equipment.

JP 2015-215690 A JP 2017-16238 A

The timing of when an abnormality occurs is influenced by factors such as the operating environment of the equipment, so there are certain busy and slow periods, and during busy periods, maintenance companies face an issue of staff shortages. In order to alleviate this, predictive diagnosis detects when an abnormality is about to occur. However, due to the nature of detecting abnormality precursors, simply detecting the precursors may lead to an increase in the frequency of detections, making it impossible to resolve the issue of staff shortages at maintenance companies. Furthermore, even if a warning is received, there is a delay in the maintenance company's response, and there is a risk that something that could have been improved by early repairs before the breakdown became apparent may actually end up breaking down.

The present invention has been made in view of this background, and aims to support maintenance providers in taking appropriate action in response to symptoms of detected fault conditions in equipment. Note that a maintenance provider may be an organization or individual that performs maintenance, and is not limited to a specialized company. Furthermore, maintenance providers may be managed on a company or other unit basis, or on a departmental basis within a company.

In order to achieve the above object, the present invention provides a predictive diagnostic device that detects signs of failure in a diagnosis target device that is the subject of maintenance, the predictive diagnostic device having a receiving unit that receives operating information of the diagnosis target device, a predictive diagnostic unit that detects multiple signs of failure in the diagnosis target device based on the operating information, a priority determination unit that determines the priority of maintenance for each of the multiple detected signs, a notification determination unit that determines whether or not to notify for the signs based on the priority and maintenance company attribute information that indicates the status of the maintenance company that maintains the diagnosis target device, and an information transmission unit that issues notifications for signs that are determined to require notification by the notification determination unit.

The present invention also includes a predictive diagnosis method using a predictive diagnosis device, a program for operating the predictive diagnosis device, and a storage medium storing this program. Furthermore, the present invention also includes a predictive diagnosis system including the predictive diagnosis device. Furthermore, the present invention also includes a mobile terminal constituting the predictive diagnosis system and a program for operating the mobile terminal.

The present invention makes it possible to support maintenance companies in taking appropriate measures against symptoms of equipment failure detected by the symptom diagnosis unit.

1 is a diagram showing an overall configuration of a predictive diagnosis system according to a first embodiment. 1 is a functional block diagram of a predictive diagnosis system according to a first embodiment. FIG. 4 is a diagram for explaining the process of the predictive diagnosis server 3 in the first embodiment. FIG. FIG. 2 is a diagram showing an example of a maintenance execution database 321 used in the first embodiment in a table format. 1 is a diagram showing an example of a sign content priority database used in the first embodiment, configured in a table format; FIG. 13 is a diagram for explaining the concept of process 342 in the first embodiment. FIG. 11 is a diagram for explaining the process of the remote diagnosis server 3b in the second embodiment.

A detailed description of a first embodiment of the present invention will be given below with reference to Figs. 1 to 6. Fig. 1 is a diagram showing the overall configuration of a predictive diagnosis system 0 in the first embodiment. The predictive diagnosis system 0 is composed of various devices used in a diagnosis target device installation location 10, a remote monitoring center 30, and a service center 40. Here, the diagnosis target device installation location 10 is, for example, within a home, and a home gateway 5 is installed. The home gateway 5 has a short-range wireless data communication function such as Bluetooth (registered trademark) and can communicate with the diagnosis target device. The diagnosis target device is also a device that is subject to maintenance.

In addition, home appliances, which are examples of the diagnosis target devices, are installed at the diagnosis target device installation location 10. In this embodiment, a drum-type washer/dryer 1, a refrigerator 7, and an air conditioner 8 are installed as home appliances at the diagnosis target device installation location 10. These home appliances can communicate with the home gateway 5 using a short-distance wireless data communication function. Note that since it is only necessary for the home gateway 5 and the home appliances to be able to communicate with each other, wired communication may also be used.

The home gateway 5 can also connect to a predictive diagnosis server 3 provided in a remote monitoring center 30 via the Internet 6, which is an example of a network. The predictive diagnosis server 3 is an example of a predictive diagnosis device, and receives operation information indicating the operating status of the drum-type washer-dryer 1, refrigerator 7, and air conditioner 8 via the home gateway 5. The predictive diagnosis server 3 has a function of performing predictive diagnosis using the operation information.

The predictive diagnosis server 3 can also be connected to a user mobile terminal 2, such as a mobile phone used by a user (consumer), and a mobile terminal 4 of a maintenance company at a service center 40 for home appliances, via the Internet 6. The above-mentioned operating information may be transmitted from each home appliance to the predictive diagnosis server 3 via the user mobile terminal 2. In this case, the operating information may be communicated via the home appliance - user mobile terminal 2 - home gateway 5 - Internet 6, or the home gateway 5 may be omitted. Furthermore, the user mobile terminal 2 and mobile terminal 4 can be realized by an information processing device (computer) such as a smartphone, tablet, or PC.

The processing of this embodiment will be described below using a drum-type washer/dryer 1 as an example, but the same applies to refrigerators 7 and air conditioners 8. Furthermore, the diagnostic targets of the predictive diagnosis system 0 are not limited to home appliances. The diagnostic target device may be, for example, any device that is assumed to be maintained by the user at regular intervals (cleaning, etc.), and may be a commercial air conditioner, elevator, etc.

Next, FIG. 2 is a functional block diagram for explaining the functions of the predictive diagnosis system 0 in the first embodiment.

First, in addition to the normal washing and drying function, the washer-dryer 1 is equipped with an information acquisition unit 11, an information transmission unit 12, an information receiving unit 13, and a display unit 14. The information acquisition unit 11 acquires specific operation information of the washer-dryer 1. The information acquisition unit 11 then uses the information transmission unit 12 to transmit the operation information to the predictive diagnosis server 3 via the Internet 6 periodically, such as at regular intervals. In this way, the information transmission unit 12 has the wireless communication function described above. The information acquisition unit 11 also acquires the location information and device identification number of the washer-dryer 1, and uses these to identify the responsible maintenance company.

The user mobile terminal 2 also includes an information transmission unit 22, an information reception unit 23, a display unit 24, and an equipment management unit 25. It is preferable that the user mobile terminal 2 also includes an input unit for performing various operations. Here, the display unit 24 may be configured to double as an input unit, such as a touch panel. The equipment management unit 25 also has a function of managing the operation of the home appliances. For example, it creates operation instructions for the washer/dryer 1, and causes the display unit 24 to display a notification of the end of an operation that has been received. Furthermore, the equipment management unit 25 receives from the user instructions for dealing with the symptoms detected by the predictive diagnosis server 3 and notified to the user mobile terminal 2. The equipment management unit 25 then notifies the mobile terminal 4 and the predictive diagnosis server 3 of a request to deal with the symptoms using the information transmission unit 22.

It is preferable that the device management unit 25 is a processor such as a CPU that executes the above-mentioned processes in accordance with a device management program. The device management unit 25 may also be realized by dedicated hardware.

The predictive diagnosis server 3 includes a predictive diagnosis unit 31, a maintenance execution classification unit 32, a priority order determination unit 33, a notification determination unit 34, an information transmission unit 35, an information reception unit 36, and a storage unit 300. When the predictive diagnosis unit 31 detects a sign that requires notification, the information transmission unit 35 is used to send a notification via the Internet 6. The notification destination is at least one of the user mobile terminal 2, the mobile terminal 4, and the washer/dryer 1. As a result, information on the detected sign is displayed on at least one of the display unit 14, the display unit 24, and the display unit 44. The predictive diagnosis server 3 can be realized by an information processing device (computer). In this case, it is preferable that the predictive diagnosis unit 31, the maintenance execution classification unit 32, the priority order determination unit 33, and the notification determination unit 34 are each implemented by a processor such as a CPU that executes various processes described below according to each program. Each unit may also be realized by dedicated hardware.

The storage unit 300 has a function of storing various information and databases shown in FIG. 3. Therefore, the storage unit 300 can be realized by a storage medium such as an HDD, and can store each program. The various information and databases may be configured separately from the predictive diagnosis server 3.

The mobile terminal 4 also includes an information transmission unit 42, an information reception unit 43, a display unit 44, and a predictive diagnosis unit 45. The mobile terminal 4 can be realized with the same hardware as the user mobile terminal 2. That is, the mobile terminal 4 is assumed to be a commonly available information processing device (terminal) such as a smartphone or tablet, and has an application program for predictive diagnosis installed in advance. In other words, it is desirable for the predictive diagnosis unit 45 to be a processor such as a CPU that performs information processing to respond to the predictive diagnosis, that is, to perform maintenance, in accordance with the application program for predictive diagnosis. The predictive diagnosis unit 45 may also be realized with dedicated hardware.

Next, the main processing of this embodiment will be described with reference to FIG. 3. FIG. 3 is a block diagram for explaining the processing of the predictive diagnosis server 3 in this embodiment. First, the information receiving unit 36 receives operation information from the washer/dryer 1 (process 360).

Then, the predictive diagnostic unit 31 judges whether or not a warning sign of a failure of the washer-dryer 1 has occurred based on the operation information. For this purpose, the predictive diagnostic unit 31 uses the composite predictive model 311 stored in the storage unit 300. The composite predictive model 311 is a function of the data and calculated values that change for each degree of abnormality at each location when multiple warning signs of abnormal conditions occur for each location that is assumed to have the possibility of abnormality. The predictive diagnostic unit 31 calculates the degree of abnormality for each assumed abnormality location from the received operation information based on the composite predictive model 311 (process 312). Here, the degree of abnormality can be assumed to be, for example, large, medium, small, level 1, level 2, etc. It is preferable to perform process 312, that is, in this embodiment, at a predetermined interval.

The cause of an abnormal condition occurring in the washer/dryer 1 is not necessarily one. In this embodiment, by assuming multiple abnormal conditions and determining the degree of the signs, it is possible to detect multiple signs of an abnormal condition that has occurred. This makes it possible to prevent abnormalities from being overlooked and improve the efficiency of maintenance work.

Then, the sign diagnosis unit 31 compares the calculated sign level with the detection threshold 313 stored in the memory unit 300. As a result, if the sign diagnosis unit 31 determines that there is a sign beyond the threshold range, it outputs the sign content (processing 314). On the other hand, if it is within the threshold range, it determines that there is no sign, and returns to information reception (processing 360). Here, the sign content is information that indicates the sign identified from the driving information or the content of an abnormality related to the sign.

Next, the maintenance execution classification unit 32 uses the maintenance execution database 321 (maintenance execution DB) stored in the storage unit 300 to select a maintenance person for the home appliance (washer/dryer 1) related to the symptom content (process 322). Here, FIG. 4 is a diagram showing an example of the maintenance execution database 321 in a table format. The maintenance execution database 321 is information in which the symptom content and the maintenance performer are associated. Here, the symptom content, that is, the expected abnormality, is divided into two types depending on the maintenance performer. One type is a malfunction caused by a prerequisite user not performing maintenance or a user using the product beyond the range of the product specifications. Examples include a clogged dryer filter and a clogged lint filter. These can be solved by the user as long as the cause of the malfunction is known. In other words, the symptom content is recorded as the "user" as the maintenance performer in the maintenance execution database 321.

The other type is a malfunction caused by an anomaly in the structure or control of the equipment. Examples include sensor anomalies and fan motor anomalies. In these cases, it is difficult for the user to solve the problem even if they understand the cause of the malfunction, and repairs must be made by a maintenance company with specialized knowledge. In other words, this is a symptom content in which the maintenance performer in the maintenance execution database 321 is recorded as "maintenance staff." Here, it is desirable for "maintenance staff" to refer to the maintenance staff themselves or the department to which they belong who will carry out maintenance for the symptoms at the maintenance company.

Using the maintenance execution database 321, the maintenance execution classification unit 32 determines the maintenance executor according to the symptom content output in process 314 from the maintenance execution database 321 (process 322). Here, if the maintenance executor is the user, the maintenance execution classification unit 32 notifies the user mobile terminal 2 and the washer-dryer 1 of the symptom content from the information transmission unit 35 (process 350). That is, a notification is performed to the user. As an example of a notification method, a notification email can be sent to the user mobile terminal 2, and the symptom content can be displayed on the screen when the email is opened. Alternatively, a notification can be made using the notification function of the equipment management unit 25 (equipment management application program). In addition, for the washer-dryer 1, the symptom content can be displayed on the display unit 14 after the information is received by the information receiving unit 23. At this time, the operation panel can be used as the display unit 14. Here, the display content on the user mobile terminal 2 and the washer-dryer 1 includes at least the symptom content. In addition to this, the maintenance content for the symptom content may also be displayed. In this case, the maintenance content may be transmitted from the predictive diagnosis server 3, or the user mobile terminal 2 or the washer/dryer 1 may identify the maintenance content corresponding to the notified predictive content. Note that the user mobile terminal 2 uses the device management unit 25 to identify the maintenance content. The device management unit 25 identifies the maintenance content corresponding to the notified predictive content by using the correspondence between the predictive content and the maintenance content stored in a memory unit (not shown) of the user mobile terminal 2.

On the other hand, if the maintenance performer is a maintenance person, the process transitions to process 333 in the priority determination unit 33. In this way, the maintenance execution classification unit 32 classifies the outputted indications into indications that the maintenance company will perform maintenance and indications that the user of the washer-dryer 1 will perform maintenance.

Then, the priority determination unit 33 determines the priority of the signs detected by the washer-dryer 1 based on the sign content priority database 331 stored in the storage unit 300 and the detection information 332 of other devices (process 333). The details of this process 333 are described below.

Figure 5 is a diagram showing an example of the symptom content priority database of this embodiment in a table format. In the symptom content priority database 331, a priority is set for each symptom content. The priority is indicated by a numerical value indicating the necessity of maintenance, and in the example of Figure 5, the lower the priority, the higher the value. This priority may be determined, for example, based on the length of time until an abnormality occurs after the symptom is detected, the cost of parts required when an abnormality occurs, the length of delivery time for replacement parts, etc. Alternatively, it may be determined by taking these factors into consideration in combination. Note that, although the priority is shown here as being determined only based on the symptom content, it may also be a value that reflects the degree of the symptom.

Detection information 332 for other devices stores predictive detection information for home appliances maintained by the maintenance company for washer-dryer 1. Here, home appliances maintained by the maintenance company are home appliances installed within the maintenance company's area of responsibility. This predictive detection information will be explained later. Note that home appliances are not limited to washer-dryer 1, but include all other home appliances if the maintenance company in charge is also responsible for maintaining them. The predictive detection information is information on home appliances maintained by the same maintenance company as washer-dryer 1.

This symptom detection information is collected or updated periodically (for example, at regular intervals). Therefore, the other device detection information 332 stores, as symptom information, data on the number of home appliances being maintained by the maintenance company at the time when a symptom of the washer-dryer 1 was detected, the symptom content for each appliance, and the priority level. The symptom detection information may also include the maintenance worker who will be performing the maintenance.

Then, the priority determination unit 33 uses the symptom detection information and the symptom contents of the washer-dryer 1 to determine the maintenance priority of the washer-dryer 1 among all home appliances (process 333). For this purpose, the priority determination unit 33 sorts the priority order of each home appliance to determine the priority order. At this time, home appliances with the same priority may be assigned the same priority order, or may be sorted using other criteria such as the order in which the symptoms were detected.

Next, the notification determination unit 34 determines whether or not to notify based on the priority and the maintenance company attribute information 341 (process 342). Here, FIG. 6 is a diagram for explaining the concept of process 342, that is, the process of determining whether or not to notify. Hereinafter, the process of determining whether or not to notify will be explained with reference to FIG. 6. The notification determination unit 34 calculates the number of visits that can be handled (number of possible responses) using the maintenance company attribute information 341. In addition, the notification determination unit 34 calculates the accumulated value of the number of detections by accumulating the number of detections in order from the top of the priority determined in process 333. As a result, the notification determination unit 34 identifies the predictive contents of the priority that are within the range of the number of possible responses. It is possible. Such predictive contents are notified preferentially. Here, the maintenance company attribute information 341 is assumed to be operation information such as the busy season of the maintenance company, information on the operating equipment in the area in charge, work shifts, maintenance work time for each predictive content, etc. However, it is not limited to these, and any information indicating the maintenance status of the maintenance company may be used.

In this way, when the notification decision unit 34 decides whether or not to notify the washer-dryer 1 of the warning signs, the information transmission unit 35 notifies the user of the warning signs (processing 350). The notification method is the same as when the maintenance performer is a user, and is notified to the user mobile terminal 2 and the washer-dryer 1 from the information transmission unit 12. As a result, the user mobile terminal 2 and the washer-dryer 1 can display the warning signs and the countermeasures. Therefore, the notified information includes at least the warning signs. The notification contents may include the countermeasures, or the user mobile terminal 2 and the washer-dryer 1 may identify the countermeasures from the warning signs. Here, the countermeasures include information for requesting maintenance from the maintenance company. The information for requesting maintenance includes the recommended repair request period. The display may also include information recommending that the washer-dryer 1 be stopped or stopped from operating.

The user mobile terminal 2 uses the device management unit 25 to identify the countermeasure method. The device management unit 25 identifies the maintenance content according to the notified predictive content by using the correspondence between the predictive content and the countermeasure method stored in a memory unit (not shown) of the user mobile terminal 2.

In response to this display, the user mobile terminal 2 or the washer/dryer 1 accepts input of a desired date for the recommended repair request period from the user. The user mobile terminal 2 or the washer/dryer 1 then transmits a maintenance request, including the installation location of the device to be diagnosed and the desired date, to the predictive diagnosis server 3 or the mobile terminal 4. In other words, the maintenance request is transmitted to the maintenance company.

As a result, the maintenance company can use the mobile terminal 4 to check the maintenance request that is received by the mobile terminal 4 itself or by the predictive diagnosis server 3.

The maintenance company then checks the desired schedule against the available dates and times to determine the maintenance schedule. The maintenance schedule is then notified to the user via mobile terminal 4. In this case, it is desirable to execute this notification from mobile terminal 4 to the user mobile terminal 2.

When approval of the maintenance schedule from the user is received, the user mobile terminal 2 notifies the mobile terminal 4 or the predictive diagnosis server 3 of this. As a result, when the scheduled schedule, that is, the approved schedule, arrives, a maintenance worker is dispatched from the service center 40 and performs appropriate maintenance. Note that other devices, such as the maintenance company's maintenance worker management device, may be used to handle maintenance requests, maintenance schedules, and approval thereof.

The history of notifications to the user and the history of contact between the user and the maintenance company may be stored as a maintenance history (not shown) in the memory unit 300 of the predictive diagnosis server 3 via the Internet 6. In addition, in the predictive diagnosis system 0 of this embodiment, the predictive diagnosis server 3 is located in the remote monitoring center 30, and a configuration for performing online diagnosis has been described, but depending on the diagnosis content, the function of the predictive diagnosis server 3 may be provided in the washer/dryer 1, which is the machine to be diagnosed. In this case, the detection information 332 of other devices is omitted, and the priority order is determined using the predictive content priority database 331.

In addition, updates to the maintenance company attribute information 341 will be suppressed, and the information will be stored in advance in the storage unit 300. In addition, it is expected that the maintenance company attribute information 341 will store information regarding busy and slow seasons based on past experience, for example. As a result, although the real-time nature of the maintenance company attribute information 341 will decrease, predictive diagnosis will be possible even for devices that are not configured to connect to the Internet 6, and stable operation can be supported at a lower cost.

In this embodiment, in process 342, it is determined whether to notify based on the priority order, but in process 322, it may be determined that notification should be made for all of the symptoms for which the maintenance company will perform maintenance. Here, such a determination can also be made when the cumulative value of the number of detections exceeds the number of cases that can be handled. In this case, it is desirable to shift the notification timing in process 350 between symptoms that are outside the range of the number of cases that can be handled and symptoms that are within the range. In other words, it is assumed that symptoms that are outside the range will be notified at a later time or in a different cycle than symptoms that are within the range. In the case of a different cycle, it is desirable to periodically perform the process shown in FIG. 3, and to raise the priority or priority order of symptoms that are outside the range in the next cycle so that they can be notified preferentially. Note that this cycle is desirably set in consideration of the shifts of the maintenance staff of the maintenance company, but it can be adjusted as appropriate.

Furthermore, when the cumulative number of detections exceeds the number that can be handled, the notification determination unit 34 may notify the mobile terminal 4 of the location of the diagnosis target device, which is the visit destination, and the symptom content that satisfies a specified condition, for symptom content that is outside the range. For example, by notifying of the symptom content in the vicinity, such as within a specified distance from the visit destination, it becomes possible to perform maintenance "while visiting."

As described above, according to this embodiment, predictive diagnosis can be performed periodically based on the operation information from the washer/dryer 1. Here, the operation information can be acquired from sensors and boards used to control and manage the operation of the washer/dryer 1. Therefore, predictive diagnosis is possible even if there is no special measuring device or other configuration for predictive diagnosis.

This makes it easier to detect signs of abnormal conditions and carry out maintenance and repairs before they become apparent to the user, such as a stoppage of operation or functionality. This reduces the possibility of home appliances reaching an abnormal state. It also prevents the need to add special measuring instruments or other new components to home appliances in order to achieve predictive diagnosis, which helps prevent increases in the manufacturing costs of the home appliances themselves.

In addition, if the symptom is something that the user can improve, the user is immediately notified by process 322. This allows the user to avoid the trouble of contacting the maintenance company or being present at their visit if the symptom is something that the user can improve.

Furthermore, in this embodiment, the number of notifications is determined based on the maintenance company attribute information 341. This makes it possible to respond appropriately to requests from users who have been notified of the contents of the predictive symptoms. This allows for measures to be taken before an abnormal condition becomes apparent, and makes it possible to effectively utilize the results of predictive diagnosis.

In addition, in this embodiment, the operator can communicate the contents of the warning signs to the maintenance personnel before dispatching them to the location 10 of the equipment to be diagnosed. This allows the maintenance personnel to prepare in advance and respond efficiently when they visit the location 10 of the equipment to be diagnosed.

In addition, in this embodiment, the maintenance company prioritizes the signs of problems to be improved, and the system responds to them starting with the highest priority. This allows the maintenance company to increase the year-round operating rate, thereby improving the efficiency of the maintenance business.

In addition, in this embodiment, the user who receives the notification about the warning signs contacts the maintenance company to make a maintenance request (call for dispatch). This makes it easier for the maintenance company to dispatch at the user's convenience, reducing the mental burden on the user.

In this embodiment, the washer/dryer 1 has been mainly described as the device to be diagnosed, but the same effect can be obtained with refrigerators 7, air conditioners 8, and other devices. In particular, heat pump devices such as refrigerators 7 and air conditioners 8 are configured such that the operating load is significantly affected by the ambient temperature, and therefore the difference between busy and slow seasons for maintenance companies is generally as large as or even larger than that of the washer/dryer 1. Therefore, this embodiment can achieve a greater effect on these devices to be diagnosed.

Furthermore, in this embodiment, the washer/dryer 1 has been described as being owned by the user for his/her own use, but the present invention can also be applied to cases where a large number of devices are owned for the purpose of being used by others, such as a coin laundry. In such a case, the user mobile terminal 2 is the mobile terminal of a manager, such as the owner of the coin laundry.

The second embodiment of the present invention will be described in detail below with reference to FIG. 7. FIG. 7 is a diagram for explaining the processing of the predictive diagnosis server 3b in the second embodiment of the present invention.

The predictive diagnosis server 3b differs from the predictive diagnosis server 3 of Example 1 in that a fault diagnosis function has been added. That is, the predictive diagnosis unit 31 of the predictive diagnosis server 3b performs fault diagnosis at a predetermined interval (process 310-2). Note that in this example, the predictive diagnosis unit 31 is configured to execute process 310-2, but a separate fault diagnosis unit may also be provided. In this way, since it performs predictive diagnosis and fault diagnosis, the predictive diagnosis server 3b of this example can also be called a remote diagnosis server.

As described above, FIG. 7 corresponds to FIG. 3 in Example 1. FIG. 1, FIG. 2, and FIG. 4 to FIG. 6 in Example 1 are also common to Example 2. Also, unlike FIG. 3, the processing subjects (31 to 34) are omitted in FIG. 7.

The process is explained below, with the diagnosis target device being a washer/dryer 1, as in Example 1, but this is not limited to this.

Note that in this embodiment, a malfunction does not necessarily mean that a function has stopped or that operation has stopped, but also includes a decrease in function due to aging or other reasons. A request for a malfunction diagnosis may be initiated by a user who senses a malfunction, or the malfunction diagnosis may be performed automatically periodically. Furthermore, in this embodiment, a case will be described in which a user of the washer/dryer 1 senses some kind of fault in the device and requests a remote malfunction diagnosis from a maintenance company.

The processing of the predictive diagnosis server 3b in this embodiment will be described below. Note that since the predictive diagnosis (process 310-1) is similar to the processing (process 312, process 314) of the predictive diagnosis unit 31 in the first embodiment, details will be omitted. Also, each unit constituting the predictive diagnosis server 3b is similar to the predictive diagnosis server 3 in the first embodiment. However, as described above, the predictive diagnosis unit 31 is capable of fault diagnosis.

When a maintenance company is requested by a user to perform a fault diagnosis, the maintenance company notifies the predictive diagnosis server 3b from the mobile terminal 4 of an instruction to start fault diagnosis. In response to the instruction to start diagnosis, the information receiving unit 36 of the predictive diagnosis server 3b receives operation information from the information acquiring unit 11 of the washer-dryer 1 (process 360). This reception may be in response to a request from the information sending unit 35 of the predictive diagnosis server 3b, or may be achieved by the information acquiring unit 11 of the washer-dryer 1 actively sending the information. In other words, it may be of either a pull type or a push type.

Next, the predictive diagnosis unit 31 uses the received operating information to determine whether a fault or a predictive error has occurred (process 310-1, process 310-2). Here, in this embodiment, process 310-1 is the same as process 312 and process 314 in embodiment 1 as described above, but a different processing algorithm may be used.

The predictive diagnosis unit 31 also uses a composite fault model to determine the presence or absence of a fault stored in the memory unit 300, and calculates the degree of fault in the washer-dryer 1 indicated by the operation information. At this time, the predictive diagnosis unit 31 performs calculations for each assumed abnormality location in the washer-dryer 1. Here, the predictive diagnosis unit 31 determines whether or not there is a fault depending on whether the degree of fault satisfies a preset condition. As a result, if it is determined that there is a fault, the predictive diagnosis unit 31 identifies the type of fault based on the operation information. On the other hand, if there is no fault, the process transitions to process 350.

The maintenance execution classification unit 32 also uses the fault content to select a maintenance performer based on the maintenance execution database 321 (process 322). In other words, a maintenance classification is performed to identify a maintenance performer based on the fault content by the same process as in Example 1.

In the maintenance execution database 321 shown in FIG. 4, the contents of the signs are associated with the maintenance practitioners, but instead of or in addition to the contents of the signs, the contents of the failures can be associated with the maintenance practitioners. In addition, the maintenance practitioners for the failures or signs in the maintenance execution database 321 are the same for both predictive diagnosis and failure diagnosis, and can be used in common.

If, as a result of process 322, there is a sign or a fault and the maintenance executor is a user, or if there is a fault and the maintenance executor is a maintenance technician, the process transitions to process 350. Also, if there is a sign and the maintenance executor is a maintenance technician, the process transitions to process 333. The process from process 333 onwards is the same as in Example 1.

However, steps 322, 333, and 342 may be executed as follows: (1) or (2).

(1) First, in process 322, the maintenance execution classification unit 32 transitions to process 333 if the maintenance executor is a maintenance worker, regardless of whether the executor is a symptom or a failure. Also, if the maintenance executor is a user, the process transitions to process 350.

Next, in process 333, the priority determination unit 33 determines the priority of the corresponding fault or symptom. To this end, if there is a fault, the priority determination unit 33 gives the priority to the corresponding fault. That is, maintenance is given top priority. To this end, the highest priority (0 in this example) is given as the priority of the fault. Next, similar to the first embodiment, the priority determination unit 33 uses the detection information 332 of other devices to sort the faults and symptoms of the home appliances handled by the maintenance company in order of priority. Then, the priority determination unit 33 applies the same process as in the first embodiment to the sorted results to determine the priority. As a result, in process 342, the notification determination unit 34 determines whether or not a notification is required by the same process as in the first embodiment.

(2) Process 322 is performed in the same manner as (1). Next, in process 333, the priority determination unit 33 identifies the priority of the sign, and determines the priority of the sign based on this. In other words, the same process as in Example 1 is executed.

Then, in process 342, the notification determination unit 34 determines that the failure is to be notified. Next, the notification determination unit 34 calculates the number of visits that can be handled (number of possible responses) using the maintenance company attribute information 341. Then, the notification determination unit 34 subtracts the number of failures from the calculated number of possible responses to calculate the number of possible symptoms that can be handled. Next, the notification determination unit 34 compares the number of possible symptoms that can be handled with the accumulated value of the number of detected symptoms, and identifies the symptom contents of the priority order that fall within the range of the number of possible symptoms that can be handled. As a result, it is determined that the failure is to be notified, and it is determined whether the symptom is to be notified according to its priority order. This concludes the explanation of alternatives (1) and (2), and as a result, the user mobile terminal 2 receives notifications regarding failures preferentially.

Finally, in process 350, the destination of information transmission unit 35 is determined by the information of the maintenance performer. That is, if the maintenance performer is a user, the details and severity of the malfunction are sent to the user mobile terminal 2 or washer/dryer 1, and if the maintenance performer is a maintenance company, the details and severity of the malfunction are sent to mobile terminal 4. In addition, after the notification is received by information receiving unit 13 in washer/dryer 1, the notification details are displayed on the operation panel (not shown) as display unit 14.

The display unit 14 of the washer-dryer 1 of this embodiment is intended to be used even when a sign is detected in the predictive diagnosis. For this reason, it is desirable to display differently when a sign is detected and when a malfunction is detected, in order to make it easier to understand whether the detected content is a sign or a malfunction. A different display, for example, is intended to change the color of the backlight of the operation panel depending on whether there is a malfunction or a sign of a malfunction. In the case of equipment other than the washer-dryer 1 that does not have an operation panel, the color of the backlight or operation lamp on the remote control of that equipment may be changed.

As a result, if a fault is detected, a maintenance company will be dispatched immediately or the problem will be fixed on the user's own. On the other hand, if a warning sign is detected, the user will have ample time to contact the maintenance company or fix the problem on their own.

In this way, by changing the display depending on whether there is a warning sign or a malfunction, the user can easily visually understand the urgency of the need for maintenance of the washer-dryer 1 without complicating the device configuration, improving convenience.

This concludes the explanation of each embodiment, but the present invention is not limited to each embodiment and various modified examples are envisioned. For example, the service center 40 and the remote monitoring center 30 may be a common system, or multiple service centers 40 may be provided. When multiple service centers 40 are provided, a remote monitoring center 30 may be provided for each service center 40, or multiple service centers 40 may be managed by a single remote monitoring center 30. In the latter case, the predictive diagnosis server 3 and the predictive diagnosis server 3b may be constructed using a so-called cloud system.

The present invention also includes the following aspects. The configuration includes a receiving unit that receives operation information of the equipment, a predictive diagnosis unit that detects malfunctions from the operation information before the equipment breaks down, and a priority determination unit that prioritizes notifications of the predictive diagnosis results. The configuration further includes a means for acquiring attribute information of the equipment's maintenance company, and a notification determination unit that determines whether or not to notify the predictive diagnosis results based on the results of the priority determination unit and the attribute information of the maintenance company.

The present invention is further configured to have a means for acquiring information related to the location information of the device. Here, the priority determination unit may assign a priority to the malfunction that the maintenance company handles. Also, the means for acquiring attribute information of the maintenance company of the device refers to a database in which attribute information of the maintenance company is stored. And the database in which attribute information of the maintenance company is stored is updatable.

The system may further include a transmission unit that transmits the diagnosis result of the predictive diagnosis unit based on the result of the notification determination unit. This transmission unit transmits the diagnosis result of the predictive diagnosis unit to multiple devices.

In addition, either or both of the device and the mobile terminal owned by the device administrator may have a receiving unit that receives information from the transmitting unit, and may be configured to function as a display unit that displays the detection details based on the information received from the receiving unit. Note that the information received by the receiving unit from the transmitting unit preferably includes the recommended repair request period along with the diagnosis results of the early symptom diagnosis unit.

The system may also be configured to have a fault diagnosis unit that detects equipment failure from operation information. In this case, the contents detected by the fault diagnosis unit and the contents detected by the predictive diagnosis unit are displayed differently. The equipment to be diagnosed by the predictive diagnosis system that supports stable operation of equipment may be a home appliance, a heat pump device, or a commercial device.

0: Predictive diagnosis system 1: Washer/dryer 2: User's mobile terminal 3, 3b: Predictive diagnosis server 4: Mobile terminal 5: Home gateway 6: Internet 7: Refrigerator 8: Air conditioner 10: Installation location of device to be diagnosed 30: Remote monitoring center 40: Service center

Claims (14)

A predictive diagnostic device for detecting a failure sign of a diagnosis target device that is a maintenance target,
A receiving unit that receives operation information of the device to be diagnosed;
a symptom diagnosis unit that detects a plurality of symptoms related to a failure in the diagnosis target device based on the operation information;
a priority order determination unit that determines a priority order of maintenance for each of the plurality of detected signs;
a notification determination unit that determines whether or not a notification is required for the symptom based on the priority order and maintenance company attribute information indicating a status of a maintenance company that performs maintenance of the diagnosis target device;
The predictive diagnosis device further comprises an information transmission unit that executes notification regarding the predictive symptom determined by the notification determination unit to require notification. The predictive diagnosis device according to claim 1,
The predictive diagnosis device further includes a maintenance execution classification unit that classifies the predictive signs into predictive signs that the maintenance company will perform maintenance and predictive signs that a user of the equipment to be diagnosed will perform maintenance. The predictive diagnosis device according to claim 2,
The information sending unit is a predictive diagnosis device that, when the maintenance execution classification unit classifies the predictive sign as a sign that the user will perform maintenance, notifies a user who uses the equipment to be diagnosed of the user of the predictive sign by providing information indicating the content of the predictive sign as a notification regarding the predictive sign. The predictive diagnosis device according to claim 2,
If the maintenance execution classification unit classifies the indication as one that the maintenance company will perform maintenance, the notification determination unit calculates the number of cases that can be handled by the maintenance company from the maintenance company attribute information, and determines which of the multiple indications require notification in accordance with the priority order and the number of cases. The predictive diagnosis device according to claim 4,
The predictive diagnosis unit further detects a failure of the diagnosis target device,
The priority order determination unit is a predictive diagnosis device that determines a priority order such that maintenance for the failure is given top priority. A method for predictive diagnosis using a predictive diagnosis device for detecting a failure sign of a diagnosis target device that is a maintenance target, comprising:
A receiving unit receives operation information of the diagnosis target device,
A sign diagnosing unit detects a plurality of signs of failure in the diagnosis target device based on the operation information;
A priority order determination unit determines a priority order of maintenance for each of the plurality of detected signs;
a notification determination unit determines whether or not a notification is required for the symptom based on the priority order and maintenance company attribute information indicating a status of a maintenance company performing maintenance of the diagnosis target device;
A sign diagnosis method comprising: an information transmitting unit that executes a notification regarding the sign determined by the notification determining unit to require notification. The method for predictive diagnosis according to claim 6,
Furthermore, the predictive diagnostic method further comprises a maintenance execution classification unit classifying the predictive signs into predictive signs that the maintenance company will perform maintenance and predictive signs that the user of the device to be diagnosed will perform maintenance. The method for predictive diagnosis according to claim 7,
A predictive diagnosis method in which, when the information sending unit classifies the predictive sign as a sign that the user will perform maintenance by the maintenance execution classification unit, the information sending unit notifies a user who uses the device to be diagnosed of the user of the predictive sign of information indicating the content of the predictive sign as a notification regarding the predictive sign. The method for predictive diagnosis according to claim 7,
When the maintenance execution classification unit classifies the indication as one that the maintenance company should perform maintenance, the notification determination unit calculates the number of cases that can be handled by the maintenance company from the maintenance company attribute information, and determines the indication that requires notification from the multiple indications according to the priority order and the number of cases. The method for predictive diagnosis according to claim 9,
The predictive diagnosis unit further detects a failure of the diagnosis target device,
The predictive diagnosis method, wherein the priority order determination unit determines a priority order in which maintenance for the fault is given top priority. A device management program that causes a terminal device, which is a computer for managing the operation of a device to be diagnosed, to execute the following steps:
storing a correspondence between a symptom content related to a failure of the diagnosis target device and a countermeasure method for the symptom content, the countermeasure method including information for requesting maintenance;
outputting an operation instruction to the diagnosis target device;
receiving a notification of a sign from a sign diagnosis device that detects a sign of a failure of the diagnosis target device, the notification including a priority order for each of a plurality of signs of a failure in the diagnosis target device calculated based on operation information of the diagnosis target device and a sign content of the sign determined based on maintenance company attribute information indicating a status of a maintenance company that maintains the diagnosis target device;
specifying a countermeasure method according to the notified symptom content by using the correspondence relationship;
displaying the notified content of the predictive sign and the identified method of dealing with the problem;
a step of notifying the maintenance company of a maintenance request for the symptom in accordance with an instruction from a user of the terminal device;
The indication is a device management program indicating that maintenance by the maintenance company is required. 12. The device management program according to claim 11,
The sign includes a failure that requires maintenance by the maintenance company,
The step of receiving a notification regarding the symptom is a step of preferentially receiving a notification regarding the failure. 12. The device management program according to claim 11,
moreover,
receiving operation information of the diagnosis target device itself from the diagnosis target device;
a device management program that causes the predictive diagnosis device to execute a step of transmitting the operating information; 14. The device management program according to claim 11,
The notification regarding the sign is determined by an equipment management program according to the number of issues that can be handled by the maintenance company, which is calculated based on the maintenance company attribute information, and the priority level.

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