Classifications

• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
  • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
  • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
  • G05B13/028—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using expert systems only
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B2219/00—Program-control systems
  • G05B2219/20—Pc systems
  • G05B2219/24—Pc safety
  • G05B2219/24074—Probability of defect, seriosity or severity of defect, fault
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B2219/00—Program-control systems
  • G05B2219/30—Nc systems
  • G05B2219/33—Director till display
  • G05B2219/33303—Expert system for diagnostic, monitoring use of tree and probability
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B23/00—Testing or monitoring of control systems or parts thereof
  • G05B23/02—Electric testing or monitoring
  • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
  • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
  • G05B23/0283—Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL]
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B23/00—Testing or monitoring of control systems or parts thereof
  • G05B23/02—Electric testing or monitoring
  • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
  • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
  • G05B23/0286—Modifications to the monitored process, e.g. stopping operation or adapting control

Definitions

• the invention relates generally to electrical machines, in particular built-in electrical machines monitoring devices. Furthermore, the invention relates to the field of monitoring of electrical machines for their probability of failure.
• Electrical machines such as field devices, are technical devices that are used in automation technology in production processes. These include actuators, such as actuators and valves, as well as sensors, such as transmitters in factory and process automation.
• the electric machine should be as fail-safe as possible, since in the worst case, the entire production process comes to a standstill in the event of a failure, which can lead to high economic damage.
• a safety monitoring module which monitors the behavior of several electrical machines in a system executing a production process and compares them with an expected behavior. If the safety monitoring module detects a deviation between the monitored behavior and the expected behavior, the safety monitoring module transfers the entire system to a safe state so that greater damage is avoided. In this way, a faster resumption of the production process can be achieved.
• an electrical machine comprises a monitoring device, which is set up to monitor an operating state and / or an ambient state of the electrical machine and to derive a failure probability of the electrical machine based on the monitored operating state and / or ambient state.
• the electric machine may be any electrical device such as a motor, a generator or a controller.
• the specified electrical machine is based on the consideration that it is indeed possible with the safety monitoring module mentioned above to find faults in a system carrying out a production process and to protect the system correspondingly from negative effects due to this fault, however, the known safety device responds.
• Monitoring module exclusively on existing errors which always results in a deviation from a planned production process and thus inevitably leads to economic damage.
• an idea of the specified electrical machine is to determine its own probability of failure and thus the probability that errors occur, for example, to give the technical staff information about its expected life and its expected reliability. This can be done for example by a comparison with collected statistical data via the electric machine.
• the failure probability of a technical system also referred to as the failure rate, shall be understood below to mean a statistical quantity whose value indicates the probability of failure or the probability of a malfunction of the technical system.
• the probability of failure is the probability that an electric machine will fail within a given period of time.
• the error rate ie the percentage of electrical machines from a fleet that fail within a defined period of time as well as the average service life of the technical system.
• the probability of failure can also be the number of failures of a technical system over a defined time unit, so that it is apparent from the inverse of the probability of failure, the average life of the technical system.
• the probability of failure can be determined in any way.
• known models of the electric machine can be used, which make it possible to derive the reliability and / or the health of the electric machine based on the past and current operating conditions of the electric machine.
• An electronic circuit of the electrical machine implemented on a printed circuit board could be monitored, for example, for its temperature, from which conclusions about the probability of failure can then be drawn about the known model.
• the monitoring device can be set up to output the probability of failure to a user.
• the user may be the technical person who oversees the production process in which the electric machine is integrated.
• the technical staff in the case of critical probability of failure of the specified electric machine still in the current production process on the timing and type of further safeguards for the relevant electric machine decide without directly the entire production process would have to be stopped.
• the monitoring device can be set up to initiate a measure for securing a functional state of the electrical machine based on the derived probability of failure.
• measures could be, for example, in a reduction of the power of the specified electric machine to the maintenance of the field device, whereby the entire production process is limited in its performance, because the production process, for example, only with a reduced throughput is feasible, but it is not completely interrupted ,
• the indicated electrical machine comprises a memory in which data for use and / or maintenance history, environmental conditions and / or test results for the field device are stored.
• the monitoring device is preferably set up to derive the probability of failure of the electrical machine based on at least part of the data in the memory. Due to the data stored in the memory, predictive methods can be used to determine the probability of failure, whereby the probability of default can be determined more reliably.
• the specified electrical machine comprises a sensor for detecting the operating state and / or a sensor for detecting the ambient state. Due to the operating states in the specified field device as well as the environmental conditions around the specified field device, the accuracy of the probability of failure can be further increased, since These states directly derive the load of the individual components of the field device.
• the specified electrical machine comprises a data interface that is set up to receive information from another electrical machine.
• the monitoring device of the specified field device is set up to derive the failure probability of the electrical machine based on the received information.
• Statistical information such as mean values and deviations for the data flowing into the probability of failure or the probability of failure itself can be determined from the received information, which further increases the information content of the probability of default.
• the data interface of the specified electrical machine is set up to send information about the monitored operating state and / or the monitored ambient state.
• the specified electrical machine comprises an input interface for setting and / or programming the monitoring device.
• the functionality monitoring device can be adapted or updated to adapt the monitoring of the specified electric machine to local and / or temporal changes.
• FIG. 1 shows an exemplary schematic view of a fieldbus system
• FIG. 2 shows an exemplary schematic view of the field device in the fieldbus system of FIG. 1.
• the same elements of identical or comparable function are provided with the same reference numerals.
• a plurality of electrical machines in the form of field devices 8 are controlled by a control room 4 via a fieldbus 6.
• the field devices 8 can be actuators in the form of actuators or valves as well as sensors in the form of measuring elements within a production process.
• High demands are placed on the field devices 8 in terms of robustness and availability. For example, they must operate faultlessly in ambient temperatures between -20 ° C and +85 ° C. If one of the field devices 8 fails in the production process, a production downtime caused thereby can cause a high level of economic damage.
• a control device 10 shown in FIG. 2 is installed in each field device 8, which determine the probability of failure of their corresponding field device 8 and thus the reliability and required maintenance of the respective field device 8 and, for example via a Display means 12 can spend.
• This information can be used, for example, by the personnel monitoring the field devices 8 in order to reduce the probability of failure of the corresponding field device 8 in good time by means of suitable maintenance measures. It is also possible to initiate appropriate maintenance measures automatically.
• the control device 10 comprises as its core a reliability and maintenance controller 14, hereinafter referred to as R & M controller 14 (reliability and maintenance controller).
• the R & M controller 14 is connected to a reliability and maintenance database 16, hereinafter called R & M database 16 (reliability and maintenance database).
• R & M database 16 reliability and maintenance database
• HMI connection 24 HMI: human machine called interface
• Information about the reliability and the maintenance of the field device 8 in which the control device 10 is stored is stored in the R & M database 16.
• This information can be, for example, data that is associated with the first use of the field device 8, which are connected to a possible failure of the field device 8, and / or by means of which the wear of the field device 8 can be seen.
• Data associated with the initial use of the field device may include, for example, its production date, delivery date, or date of commissioning.
• Data associated with a possible failure of the field device can include, for example, the reporting date of a failure or the return date of the field device 8 to the manufacturer.
• Data from which the wear of the field device can be determined may include, for example, the history of the use of the field device 8, environmental conditions, past maintenance activities or test results. For example, data that includes the environmental conditions may include maximum, average, or minimum temperature or humidity, or may include periods of time during which the field device has been exposed to certain temperature or humidity thresholds.
• the R & M controller 14 is the analytic part of the controller 10. It implements analytical and predictive techniques such as data mining, statistical methods, learning algorithms, and so forth. Further, in the R & M controller 14, techniques are implemented which, based on currently available reliability and probability of failure, can determine the maintenance actions that are best suited to increase reliability and reduce the probability of failure. If possible or necessary, the R & M Controller 14 can initiate these maintenance measures automatically or change the operation of the field device appropriately in order to minimize the failure probability of the field device.
• the fieldbus 6 is connected to the sensor connection 18, the service connection 20 and the communication connection 22. Optionally, the fieldbus 6 can also be connected to the HMI connection 24.
• internal sensors 26 arranged in the field device 8 are connected to the sensor connection 18. These can detect internal operating states in the field device 8, such as, for example, temperatures and moisture, whose measurement results allow conclusions to be drawn about the failure probability of the field device 8. Additionally or alternatively, information about the use of the field device 8 can also be detected by the internal sensors 26, from which information about the probability of failure of the field device 8 can also be drawn. Measured data of an external sensor 28 can also be received via the fieldbus 6 at the sensor connection 18, providing information about ambient conditions such as ambient temperatures around the field device 8, which can likewise be taken into account when determining the failure probability of the field device 8. The data received via the sensor connection 18 can be stored in the R & M database 16, for example for storing the usage history of the field device, or used by the R & M controller 14 to determine the failure probability of the field device 8.
• the service connection 20 allows the service personnel on the field device to carry out instant maintenance measures.
• 16 data can be stored or downloaded from the service connection 20 in the R & M database in order, for example, to read out result log files.
• the individual components of the control unit 10, such as the R & M controller 14, the sensor connection 18 or the HMI connection 24 can be configured or reprogrammed via the service connection 20.
• the service terminal 20 is explicitly intended for the service technician who can use the service terminal 20 to download information from the field device 8, such as environment data from the R & M database 16. At the same time, the service technician can provide statistical data of similar products and / or upload new statistical methods or mathematical algorithms to the field device 8 via the service port 20.
• a visualization of the data may be required, for example via a display with operating option.
• IT ports may be required to exchange the data between the field device 8 and a technician.
• Information about the probability of failure and / or the reliability of the field device 8 can be exchanged with other field devices 8 connected to the fieldbus 6 via the communication connection 22.
• this data may include any or all of the data stored in the aforementioned R & M database 16.
• the individual field devices 8 can exchange data with each other directly bypassing the fieldbus 6.
• the communication port 22 can be designed wireless or wired. The aim of the data communication is to have the data of a fleet of field devices of the same or similar production available.
• information can be output to the user on the display 12.
• This information can be the probability of failure, suggestions for maintenance or even problematic internal operating conditions in the field device 8 or external operating states around the field device 8, which the user could possibly change to increase the probability of failure.
• the user can make inputs via the HMI connection 24, for example to specify warning thresholds for the probability of default, from which the user wants to be informed of an impending failure of the field device 8, for example by issuing a warning signal.
• the HMI port 24 is thus intended to exchange information with an operator, such as a customer or an end user.
• Examples of data exchanged over the HMI port 24 are, for example, MTBF data (Mean Time Between Failures - data showing a mean time between failures), health status data, residual life data, or pending required service data.

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• Engineering & Computer Science (AREA)
• Artificial Intelligence (AREA)
• Health & Medical Sciences (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Evolutionary Computation (AREA)
• Medical Informatics (AREA)
• Software Systems (AREA)
• Physics & Mathematics (AREA)
• General Physics & Mathematics (AREA)
• Automation & Control Theory (AREA)
• Testing And Monitoring For Control Systems (AREA)

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• 2012
  • 2012-08-17 DE DE102012016269.3A patent/DE102012016269A1/de not_active Ceased
• 2013
  • 2013-08-12 WO PCT/EP2013/002416 patent/WO2014026759A1/fr not_active Ceased

Patent Citations (3)

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