JPWO2019077679A1 - Data processing apparatus, data processing system, data processing method, data processing program, and storage medium - Google Patents

Abstract

The data processing device (2) includes an algorithm selection unit (26) and a life prediction processing unit. The algorithm selection unit (26) selects, from the algorithm storage unit (30) in which an algorithm for performing the life prediction of the parts constituting the device is stored, among the parts constituting the device, the target parts to be subjected to the life prediction. Select the corresponding algorithm. The preventive maintenance processing unit (25), which is a life prediction processing unit, executes a life prediction process for the target part based on the algorithm selected by the algorithm selection unit (26).

Description

  The present invention relates to a data processing apparatus, a data processing system, a data processing method, a data processing program, and a storage medium that perform data processing for preventive maintenance of the apparatus.

  By monitoring the signs of failure based on the data obtained by actually measuring the status of the equipment, and predicting the remaining life until the level of such signs exceeds a certain reference level, it is possible to identify failures that occur in the equipment in advance. Preventive maintenance methods are known.

  Patent Document 1 discloses a control system that predicts the lifetime of each component based on information from sensors provided in a plurality of components constituting the engine, and predicts the lifetime of the entire engine from the lifetime of each component. ing. The control system of Patent Document 1 predicts a trend of a phenomenon that causes a failure such as crushing or loss of coating, and determines the remaining life of a part from a prediction result of a failure occurrence rate due to the occurrence of such a phenomenon. The control system of Patent Document 1 is programmed to calculate the remaining life of a part according to a life prediction algorithm using data on the operating state of the engine.

Special table 2014-518974 gazette

  At the production site of products, in order to enable planned and stable production, preventive maintenance of a production apparatus operated at the production site is required. By using the algorithm that predicts the life based on the data obtained by actually measuring the state of the production equipment, the life of each part that makes up the production equipment is calculated, so that the user of the production equipment can perform maintenance or replace parts. Can tell when to do.

  In general, the parts constituting the production apparatus are arbitrarily selected by the manufacturer of the production apparatus. When the technology of Patent Document 1 is applied to a production device, the provider of the application for preventive maintenance incorporated in the production device constructs an algorithm specialized for the configuration of the production device for each manufacturer of the production device. Will do. Also, when a part is added or replaced in the production apparatus, the algorithm is reconstructed. Therefore, according to the technique of Patent Document 1, there is a problem that a burden required for building an application used for data processing for preventive maintenance of the apparatus may increase.

  The present invention has been made in view of the above, and an object of the present invention is to provide a data processing apparatus that can reduce the burden required for building an application used for data processing for preventive maintenance of the apparatus.

  In order to solve the above-described problems and achieve the object, a data processing apparatus according to the present invention is a component that constitutes an apparatus from an algorithm storage unit that stores an algorithm for predicting the lifetime of the parts that constitute the apparatus. An algorithm selection unit that selects an algorithm corresponding to a target part that is a target of life prediction, and a life prediction processing unit that executes a process of life prediction of the target part based on the algorithm selected by the algorithm selection unit; It is characterized by providing.

  The data processing apparatus according to the present invention has an effect of reducing the burden required for building an application used for data processing for preventive maintenance of the apparatus.

1 is a block diagram of a data processing system according to a first embodiment of the present invention. Configuration diagram of a preventive maintenance application installed in the data processing apparatus shown in FIG. The block diagram which shows the function structure of the data processor shown in FIG. The block diagram which shows the hardware constitutions of the data processor shown in FIG. The flowchart which shows the procedure of the process by the task handler shown in FIG. The block diagram which shows the function structure of the preventive maintenance process part shown in FIG. The figure which shows the representative life curve and failure threshold value which are selected by the representative life curve selection part shown in FIG. 1st figure explaining the production | generation of the lifetime prediction curve by the lifetime prediction curve production | generation part shown in FIG. 2nd figure explaining the production | generation of the lifetime prediction curve by the lifetime prediction curve production | generation part shown in FIG. 3rd figure explaining the production | generation of the life prediction curve by the life prediction curve production | generation part shown in FIG. FIG. 4 is a fourth diagram for explaining the generation of a life prediction curve by the life prediction curve generation unit shown in FIG. The flowchart which shows the procedure of the process by the data processor after the preventive maintenance algorithm shown in FIG. 2 is selected. The flowchart which shows the procedure of the process which produces | generates a lifetime prediction curve by the lifetime prediction curve production | generation part shown in FIG.

  Hereinafter, a data processing device, a data processing system, a data processing method, a data processing program, and a storage medium according to embodiments of the present invention will be described in detail with reference to the drawings. Note that the present invention is not limited to the embodiments.

Embodiment 1 FIG.
FIG. 1 is a block diagram of a data processing system according to the first exemplary embodiment of the present invention. A data processing system 1 shown in FIG. 1 includes a data processing device 2, a device 4A connected to the data processing device 2, and devices 4B and 4C connected to the device 4A. The devices 4A, 4B, and 4C are devices that acquire industrial data. The industrial data is data such as temperature, voltage, current, distance, speed, or position information, and is any data regarding the state of the production apparatus or the production site.

  The device 4B is a production apparatus, and is a driving device such as a numerical control (NC) device, a servo motor, and an inverter. The device 4A is a controller that controls the device 4B and is a programmable logic controller (PLC). The device 4C is a sensor attached to the device 4B which is a production apparatus, and is a vibration sensor, a sound collecting microphone, a current clamp meter, a temperature sensor, or the like. It is assumed that the number of devices 4A, 4B, 4C provided in the data processing system 1 is arbitrary. The data processing system 1 shown in FIG. 1 includes one device 4A, two devices 4B, and one device 4C. The devices 4A, 4B, and 4C are not limited to the specific examples described above, and may be any device that acquires industrial data.

  The data processing device 2 is a computer in which a preventive maintenance application 10 that is a data processing program is installed. The data processing apparatus 2 collects industrial data transmitted from the devices 4A, 4B, and 4C and performs a series of functional processes on the industrial data. The functional processing performed in the data processing device 2 includes processing for predicting the lifetime of the components that constitute the device 4B. The data processing device 2 is connected to a cloud server 3 that is an external server. The display device 5 is connected to the data processing device 2. The display device 5 displays the life prediction result obtained by the data processing device 2.

  The device 4B is provided with a mechanism for transmitting the driving force of the motor. One of the main causes of the failure of the device 4B is an abnormality in the rotating mechanism that rotates by receiving the driving force of the motor. In the first embodiment, the data processing device 2 performs preventive maintenance of the device 4B by performing life prediction for at least one of a bearing, a ball screw, a gear, and a belt that are main components constituting the rotation mechanism. . In FIG. 1, the motor, the operation mechanism, the rotation mechanism, and the components are not shown.

  FIG. 2 is a configuration diagram of the preventive maintenance application 10 installed in the data processing apparatus 2 shown in FIG. The preventive maintenance application 10 that is a data processing program includes a task handler 11 and a preventive maintenance algorithm 12. The preventive maintenance algorithm 12 is a program in which an algorithm for preventive maintenance is implemented and the implemented algorithm is realized. Further, the preventive maintenance algorithm 12 may be information describing a calculation procedure for preventive maintenance instead of the program. The information describing the calculation procedure may include information indicating a calculation formula. In this case, the preventive maintenance algorithm is realized by the preventive maintenance application 10 referring to the stored preventive maintenance algorithm 12. In the following description, it is assumed that the preventive maintenance algorithm 12 is a program.

  The preventive maintenance algorithm 12 is prepared for each type of part. In the first embodiment, at least four preventive maintenance algorithms 12 for bearings, ball screws, gears, and belts are used. A more detailed preventive maintenance algorithm 12 for each type of component, such as for large bearings, medium bearings, and small bearings, may be used. The data processing apparatus 2 can predict the lifetime in which the influence of the product specifications such as the difference in dimensions for each part is incorporated by referring to the specification parameter 15 described later. For example, when performing life prediction for bearings having different dimensions, a common algorithm corresponding to both bearings may be used, and the specification parameter 15 may be varied.

  The user of the device 4B downloads the preventive maintenance application 10 in which the preventive maintenance algorithm 12 is incorporated from a store or the like that sells the application on the website, and installs it in the data processing apparatus 2. The user of the device 4B can change the preventive maintenance algorithm 12 incorporated in the preventive maintenance application 10. The user of the device 4B additionally acquires the preventive maintenance algorithm 12 by downloading from a store or the like that sells applications on the website. The user of the device 4B may acquire the preventive maintenance algorithm 12 by reading the preventive maintenance algorithm 12 from a storage medium in which the preventive maintenance algorithm 12 is stored. The user of the device 4B may acquire the preventive maintenance application 10 by reading the preventive maintenance application 10 from the storage medium in which the preventive maintenance application 10 is stored. The user of the device 4B can configure the preventive maintenance application 10 by arbitrarily combining the preventive maintenance algorithms 12.

  The provider of the preventive maintenance application 10 provides the user of the device 4B with the preventive maintenance application 10 to which the preventive maintenance algorithm 12 can be added and replaced. The task handler 11 is provided as a standard in the preventive maintenance application 10 provided by the provider of the preventive maintenance application 10. The provider of the preventive maintenance algorithm 12 is assumed to be a component manufacturer, a manufacturer of the device 4B, or a provider of the preventive maintenance application 10, but may be another person.

  The task handler 11 reads the setting information 14 and the specification parameter 15. The setting information 14 includes identification information for identifying the preventive maintenance algorithm 12 for each part, identification information for identifying the specification parameter 15 for each part, information for identifying the part for which the life prediction is performed, It is a file including information on the execution cycle of life prediction for each part for which prediction is executed. The user of the device 4B can set which part of the parts whose corresponding preventive maintenance algorithm 12 is included in the preventive maintenance application 10 is to perform the life prediction. The preventive maintenance algorithm 12 to be used among the plurality of preventive maintenance algorithms 12 included in the preventive maintenance application 10 can be arbitrarily selected by the user.

  The specification parameter 15 is a file that defines information unique to a part. The specification parameter 15 is referred to when specifying a failure mode specific to the part. The specification parameter 15 includes information such as part dimensions. As a specific example, the specification parameter 15 for the bearing includes the numerical values of the diameter of the rolling element, the pitch circle diameter of the rolling element, the number of rolling elements, and the contact angle. It is assumed that the user of the device 4B can acquire the specification parameters 15 created for each part manufacturer and for each part type via the web or a storage medium. The specification parameter 15 is created by the manufacturer of the part, but may be created by other persons.

  The identification information of the preventive maintenance algorithm 12 is a file name given to the file of the preventive maintenance algorithm 12. The identification information of the specification parameter 15 is a file name given to the file of the specification parameter 15. The identification information of the preventive maintenance algorithm 12 may be information that can identify the preventive maintenance algorithm 12 for each part, and may be information other than the file name. The identification information of the specification parameter 15 may be information that can identify the specification parameter 15 for each part, and may be information other than the file name.

  The task handler 11 manages preventive maintenance processing in the preventive maintenance application 10. Management of preventive maintenance processing by the task handler 11 includes management of the life prediction execution cycle for each component. The task handler 11 recognizes the execution cycle of each component based on the execution cycle information included in the setting information 14. The data processing device 2 can execute the life prediction at an independent timing for each component constituting the device 4B by managing the execution cycle in the task handler 11.

  When there is a part whose execution cycle has arrived, the task handler 11 performs life prediction for the part. Note that information indicating that the life prediction is not performed may be set in the execution cycle information. When information indicating that the life prediction is not performed is set, the task handler 11 may not perform the life prediction for the part corresponding to the setting. The data processing apparatus 2 executes the preventive maintenance algorithm 12 corresponding to a part of the parts in which the corresponding preventive maintenance algorithm 12 is included in the preventive maintenance application 10, and the preventive maintenance algorithm corresponding to other parts. 12 may not be executed. The task handler 11 activates a thread 13 for executing processing for a target component that is a target of life prediction. The thread 13 selects the preventive maintenance algorithm 12 corresponding to the target part based on the identification information included in the setting information 14 for the target part.

  The thread 13 executes processing according to the selected preventive maintenance algorithm 12 using the specification parameter 15 of the target part. The task handler 11 performs parallel processing by a plurality of threads 13 when there are a plurality of components that have reached the execution cycle. The thread 13 executes the process of life prediction by executing the process according to the preventive maintenance algorithm 12 using the specification parameter 15. Functions of the preventive maintenance algorithm 12 include functions of failure frequency calculation, actual measurement value calculation, failure mode specification, representative life curve selection, life prediction curve selection, and predicted life calculation. Each function, failure frequency, and failure mode of the preventive maintenance algorithm 12 will be described later.

  FIG. 3 is a block diagram showing a functional configuration of the data processing apparatus 2 shown in FIG. Each functional unit shown in FIG. 3 is realized by executing the preventive maintenance application 10 on a computer that is hardware.

  The data processing device 2 includes a control unit 20 that is a functional unit that controls the data processing device 2, a storage unit 21 that stores information, a communication unit 22 that is a functional unit that communicates information, and a function that inputs information. And an input unit 23.

  The control unit 20 includes a preventive maintenance management unit 24 that is a functional unit that manages preventive maintenance processing. The preventive maintenance management unit 24, which is an execution cycle management unit, manages the execution cycle of life prediction. In addition, the control unit 20 includes a preventive maintenance processing unit 25 that is a functional unit that executes a life prediction process, and a preventive maintenance algorithm 12 for each part. And an algorithm selection unit 26 which is a functional unit for selecting the preventive maintenance algorithm 12 corresponding to the above. The preventive maintenance processing unit 25 is a life prediction processing unit that executes a process for predicting the life of the target part based on the algorithm selected by the algorithm selection unit 26. The functions of the preventive maintenance management unit 24 and the algorithm selection unit 26 are realized by processing of the task handler 11. The function of the preventive maintenance processing unit 25 is realized by the process of the preventive maintenance algorithm 12 that is executed using the specification parameter 15 of the target part.

  The storage unit 21 stores an algorithm storage unit 30 that stores the preventive maintenance algorithm 12, an industrial data storage unit 31 that stores industrial data about all parts acquired from the devices 4A, 4B, and 4C, and a specification parameter 15 of the part. Is provided with a specification parameter storage unit 32.

  The preventive maintenance algorithm 12 incorporated in the preventive maintenance application 10 is stored in the algorithm storage unit 30. The industrial data storage unit 31 stores industrial data acquired every second with time information. The industrial data storage unit 31 may store industrial data acquired at intervals of millisecond order or microsecond order, or may store industrial data acquired at other intervals. As a specific example, the industrial data acquired by the device 4B for the bearing includes values of motor current, encoder position, motor speed, and temperature. The preventive maintenance processing unit 25 calculates the vibration frequency of the bearing based on the industrial data about the bearing. The calculation of the vibration frequency will be described later. The industrial data acquired by the device 4C, which is a sensor provided in the bearing, includes values of vibration acceleration and sound pressure level. The specification parameter storage unit 32 stores specification parameters 15 for each component of the device 4B.

  In addition, the storage unit 21 stores a life data storage unit 33 that stores a result of life prediction by the preventive maintenance processing unit 25, a representative life curve storage unit 34 that stores a representative life curve and a failure threshold, and setting information 14. A setting information storage unit 35. Specifically, the lifetime data storage unit 33 stores a failure mode for each component, a remaining lifetime of the component, and an identification score. The setting information 14 is input to the data processing device 2 by the manufacturer of the device 4B. The representative life curve, failure threshold, failure mode, and identification score will be described later.

  The communication unit 22 performs communication between the data processing device 2 and the devices 4A, 4B, and 4C, the display device 5, and the cloud server 3 that are devices outside the data processing device 2. The input unit 23 inputs the setting information 14 to the data processing device 2.

  FIG. 4 is a block diagram showing a hardware configuration of the data processing apparatus 2 shown in FIG. The data processing device 2 includes a central processing unit (CPU) 40 that executes various processes, a random access memory (RAM) 41 including a program storage area and a data storage area, and an external storage device. A hard disk drive (HDD) 42 is provided. The data processing device 2 also includes a communication circuit 43 that is a connection interface with an external device of the data processing device 2 and an input device 44 that receives an input operation to the data processing device 2. Each unit of the data processing device 2 illustrated in FIG. 4 is connected to each other via a bus 45. Note that the external storage device may be a semiconductor memory.

  The HDD 42 stores the preventive maintenance application 10, industrial data, component specification parameters 15, life data as a result of life prediction, a representative life curve, and setting information 14. The functions of the storage unit 21 illustrated in FIG. 3 are realized using the HDD 42.

  The preventive maintenance application 10 is loaded into the RAM 41. The CPU 40 expands the preventive maintenance application 10 in the program storage area in the RAM 41 and executes various processes. The data storage area in the RAM 41 is a work area for executing various processes. The functions of the control unit 20 illustrated in FIG. 3 are realized using the CPU 40. The function of the communication unit 22 is realized using the communication circuit 43. The input device 44 includes a keyboard or a pointing device. The function of the input unit 23 illustrated in FIG. 3 is realized using the input device 44.

  The preventive maintenance application 10 may be stored in a storage medium that can be read by a computer. The data processing device 2 may store the preventive maintenance application 10 stored in the storage medium in the HDD 42. The storage medium may be a portable storage medium that is a flexible disk or a flash memory that is a semiconductor memory. The preventive maintenance application 10 may be installed in the data processing device 2 from another computer or server device via a communication network.

  FIG. 5 is a flowchart showing a processing procedure by the task handler 11 shown in FIG. The task handler 11 is activated when the computer that is the data processing device 2 is activated, and maintains the activated state until the computer is shut down.

  In step S <b> 1, the task handler 11 determines whether there is a component that has reached the life prediction execution cycle, based on the execution cycle information included in the setting information 14 read from the setting information storage unit 35. To do. If there is no component that has reached the execution cycle (step S1, No), in step S2, the task handler 11 waits until the next determination of the presence or absence of the component that has reached the execution cycle is made. The task handler 11 returns the process to step S1 after waiting in step S2.

  If there is a part that has reached the execution cycle (step S1, Yes), the task handler 11 activates the thread 13 for the target part that is the part that has reached the execution cycle in step S3. In step S4, the thread 13 selects the preventive maintenance algorithm 12 for the target part based on the identification information included in the setting information 14 for the target part. As a result, the task handler 11 selects the preventive maintenance algorithm 12 for the target part from the preventive maintenance algorithms 12 stored in the algorithm storage unit 30. Note that the task handler 11 is not limited to executing the process of step S3 when there is a component whose life prediction execution cycle has arrived in step S1. The task handler 11 may execute the process of step S3 when an instruction to execute the preventive maintenance process is input from the input unit 23 by the user.

  Based on the identification information of the specification parameter 15, the thread 13 selects the specification parameter 15 for the part whose life is to be predicted from the specification parameter 15 read from the specification parameter storage unit 32. In step S <b> 5, the thread 13 inputs the specification parameter 15 of the part to the preventive maintenance algorithm 12. In step S6, in the thread 13, the preventive maintenance algorithm 12 executes a life prediction process that is a preventive maintenance process. When the process in step S6 ends, the task handler 11 ends the process shown in FIG.

  FIG. 6 is a block diagram showing a functional configuration of the preventive maintenance processing unit 25 shown in FIG. The preventive maintenance processing unit 25 selects a measured value calculation unit 51 that is a functional unit that calculates a measured value, a failure mode calculation unit 52 that is a functional unit that calculates a failure mode value for each failure mode, and a representative life curve. A representative life curve selection unit 53 that is a function unit, a failure mode specification unit 54 that is a function unit that specifies a failure mode, and a life prediction curve generation unit 55 that is a function unit that generates a life prediction curve. In addition, the preventive maintenance processing unit 25 includes a life prediction unit 56 that is a functional unit that calculates the predicted life of the target part based on the preventive maintenance algorithm 12 selected by the algorithm selection unit 26.

  The failure mode indicates the cause of the component failure. The failure mode value is a numerical value used for specifying the failure mode. The preventive maintenance algorithm 12 stored in the algorithm storage unit 30 includes a failure model that is a calculation formula of a failure mode value for each failure mode of a component. In the first embodiment, the failure mode is a failure cause that can monitor a failure sign by observing the frequency of vibration generated in the component. The failure mode calculation unit 52 calculates a failure frequency that is a failure mode value. The failure frequency is a vibration frequency that is a sign of failure, and is a unique frequency for each failure mode. The failure mode calculation unit 52 calculates a failure frequency for each failure mode.

  Here, the calculation of the failure frequency by the failure mode calculation unit 52 will be described using a bearing that is one of the components as an example. The failure of the bearing can occur due to abnormalities in the inner ring, the outer ring, the cage, and the rolling element. The bearing failure modes include first to fifth failure modes described below.

In the following formulas (1) to (5), “d” is the diameter of the rolling element, “D” is the diameter of the pitch circle of the rolling element, “Z” is the number of rolling elements, and “α” is Contact angle. The units of “d” and “D” are millimeters, and the unit of “α” is radians. The failure mode calculation unit 52 acquires each value of “d”, “D”, “Z”, and “α” from the specification parameter 15 read from the specification parameter storage unit 32. “F ₀ ” is the rotational frequency of the inner ring. The unit of “f ₀ ” is hertz. The failure mode calculation unit 52 calculates the value of “f ₀ ” based on the industrial data 16 read from the industrial data storage unit 31.

The first failure mode is a defect of the cage may monitor for signs of failure by observing the rotational frequency f _m of the cage. Failure mode calculator 52, by the following equation (1), and calculates the rotation frequency f _m is the failure frequency of the first failure mode.

The second failure mode is a cage failure, which can be monitored for signs of failure by observing the cage relative rotational frequency fm _-i with respect to the inner ring. The failure mode calculation unit 52 calculates the relative rotation frequency f _m−i that is the failure frequency of the second failure mode by the following equation (2).

The third failure mode is scratching or peeling of the race surface of the inner ring, and the sign of the failure can be monitored by observing the passing frequency f _i of the rolling element with respect to the inner ring. The failure mode calculation unit 52 calculates the pass frequency f _i that is the failure frequency of the third failure mode by the following equation (3).

The fourth failure mode is scratching or peeling of the race surface of the outer ring, and the sign of failure can be monitored by observing the passing frequency f _O of the rolling element with respect to the outer ring. The failure mode calculation unit 52 calculates the pass frequency f _O that is the failure frequency of the fourth failure mode by the following equation (4).

Fifth failure mode is a flaw or peeling of the rolling elements, it may monitor for signs of failure by observing the rotation frequency f _b of the rolling elements. Failure mode calculator 52, by the following equation (5), and calculates the rotation frequency f _b is the failure frequency of the fifth failure mode.

When the target component is a servo motor bearing, the failure mode calculation unit 52 may calculate the rotation frequency f _{0 of the} inner ring based on a speed monitor value that is industrial data acquired from the servo motor. Alternatively, the failure mode calculation unit 52 may calculate the rotation frequency f _{0 of the} inner ring based on the number of pulses that is industrial data acquired from the device 4C that is a sensor provided in the external pulse encoder. The rotation frequency f ₀ is a value that fluctuates in a specific cycle. The failure mode calculation unit 52 acquires the rotation frequency f ₀ at a constant timing in the cycle.

By acquiring the rotation frequency f ₀ at a certain timing, the value of the rotation frequency f ₀ obtained by the failure mode calculation unit 52 at each timing becomes constant. For this reason, the value of the rotation frequency f ₀ may be a value preset in the specification parameter 15 instead of the value calculated based on the industrial data. Even when the rotational frequency f ₀ is acquired at a fixed timing, the rotational frequency f ₀ may slightly vary depending on the situation of the device 4B. Therefore, the rotational frequency f ₀ is calculated based on industrial data. By calculating, it is possible to obtain a value in which a change in the status of the device 4B is reflected more than a preset value. For this reason, the failure mode calculation unit 52 can calculate the failure frequency with high accuracy by acquiring the rotation frequency f ₀ by calculation based on the industrial data.

  Note that the failure mode may be a cause of failure that can be monitored for signs of failure by observing phenomena other than vibration. The failure mode calculation unit 52 may calculate a failure mode value other than the failure frequency. If the failure mode is a failure cause that can be monitored for signs of failure by observing the gearbox temperature, the failure mode value is the failure temperature, which is the gearbox temperature. The failure mode calculation unit 52 acquires the failure temperature.

  The actual measurement value calculation unit 51 reads the industrial data 16 stored in the industrial data storage unit 31 and calculates an actual measurement value corresponding to the failure mode value based on the read industrial data 16. When the failure mode value is a failure frequency, the actual value calculation unit 51 calculates an actual frequency that is an actual value. The actually measured frequency is a frequency of vibration generated in the component, and is calculated based on the industrial data 16 acquired by the devices 4B and 4C. When the target component is a bearing, the actual measurement value calculation unit 51 calculates the actual measurement frequency based on the motor current value acquired by the device 4B. The actual measurement value calculation unit 51 calculates an actual measurement frequency by extracting frequency components by fast Fourier transform (FFT) of the motor current value. The industrial data storage unit 31 stores data obtained by FFT as industrial data 16.

  When the vibration phenomenon is not observed in the device 4B, the actual measurement value calculation unit 51 may calculate the actual measurement frequency based on the industrial data 16 acquired by the device 4C. For calculation of the actually measured frequency, vibration acceleration acquired by a vibration sensor that is the device 4C attached to the bearing may be used. For calculation of the actually measured frequency, the sound pressure level acquired by the sound pressure sensor that is the device 4C attached to the bearing may be used. The actual measurement value calculation unit 51 may calculate the actual measurement frequency by extracting frequency components by FFT of vibration acceleration or sound pressure level.

The failure mode specification unit 54 compares the failure mode value calculated by the failure mode calculation unit 52 with the actual measurement value calculated by the actual measurement value calculation unit 51, and specifies the failure mode of the target component. When the target part is a bearing, the failure mode specifying unit 54 determines a failure frequency that matches the actually measured frequency among the failure frequencies of the first to fifth failure modes. If the measured frequency is equal to the rotational frequency f _m of the above, the failure mode identifying unit 54 identifies the failure mode of the bearing is a target part in the first failure mode.

  The failure mode specifying unit 54 can apply various methods as a method for determining whether or not the failure mode value matches the actual measurement value. The failure mode specifying unit 54 may determine whether or not the failure mode value matches the actual measurement value based on a predetermined error range. The failure mode specifying unit 54 determines that the failure mode value matches the actual measurement value when the difference between the failure mode value and the actual measurement value is within the error range. The failure mode specification unit 54 sends information indicating the specified failure mode to the representative life curve selection unit 53 and the life prediction curve generation unit 55.

  The failure mode specifying unit 54 may calculate an identification score representing the probability that the phenomenon observed as the actual measurement value corresponding to the failure mode value is a phenomenon due to the failure in the specified failure mode. The failure mode specifying unit 54 calculates an identification score based on the difference between the failure mode value and the actually measured value. The identification score is sent to the life prediction unit 56 through the life prediction curve generation unit 55. The user of the device 4B can determine the reliability of the failure determination of the target part by referring to the identification score.

  The representative life curve selection unit 53 selects a representative life curve and a failure threshold based on the failure mode specified by the failure mode specification unit 54. The representative life curve, which is the first curve, is a curve obtained by approximating the data obtained by the accelerated life test of the part, and is a measurement value corresponding to the failure mode value and the time for the phenomenon that occurred in the test. Represents a relationship. The life acceleration test is a test in which the life of a part is verified by intentionally deteriorating the part to be tested. The failure threshold is an actually measured value when a component has failed in the test.

  FIG. 7 is a diagram showing the representative life curve C1 and the failure threshold T selected by the representative life curve selection unit 53 shown in FIG. When the failure mode value is a failure frequency, the representative life curve C1 represents the relationship between the amplitude of vibration generated in the test and time. The failure threshold T is a vibration amplitude when a component has failed in the test. That is, when the failure mode value is the failure frequency, the vibration amplitude corresponding to the failure frequency is plotted in time series. In FIG. 7, the vertical axis represents vibration amplitude, and the horizontal axis represents time. In the following description, the vertical axis representing vibration amplitude may be referred to as the Y axis, and the horizontal axis representing time may be referred to as the X axis. Note that even if the parts are manufactured by the same manufacturer and have the same type, the data obtained by the life acceleration test may vary. The representative life curve C1 is a life curve that is representative of a life curve that is manufactured by the same manufacturer and obtained by the same type of part.

  The representative life curve storage unit 34, which is a curve storage unit, stores a representative life curve and a failure threshold value for each failure mode for each component of the device 4B. The representative life curve selection unit 53 determines the representative life curve C1 and the failure threshold T corresponding to the target part and the specified failure mode from the representative life curve and the failure threshold stored in the representative life curve storage unit 34. select. The time L1 is a time when the vibration amplitude reaches the failure threshold T in the representative life curve C1.

  The representative life curve selection unit 53 sends the selection result of the representative life curve C1 and the failure threshold T to the life prediction curve generation unit 55. In addition, the vertical axis when the representative life curve C1 is represented may represent temperature or frictional force, which is a parameter corresponding to the failure mode value, in addition to the vibration amplitude. In addition to the time, the horizontal axis may represent an integrated temperature, which is a parameter indicating the progress of the deterioration of the component.

  The life prediction curve generation unit 55 generates a life prediction curve based on the representative life curve C1 selected by the representative life curve selection unit 53. The life prediction curve represents the prediction of the time series change of the actual measurement value after the execution of the life prediction. When the failure mode value is a failure frequency, the life prediction curve represents the relationship between the vibration amplitude and time after the execution of the life prediction.

  FIG. 8 is a first diagram illustrating the generation of a life prediction curve by the life prediction curve generation unit 55 shown in FIG. FIG. 9 is a second diagram illustrating the generation of the life prediction curve by the life prediction curve generation unit 55 shown in FIG. The life prediction curve generation unit 55 reads the representative life curve C1 and the failure threshold T from the representative life curve storage unit 34 in accordance with the selection result by the representative life curve selection unit 53.

  The length of the time axis is different between the representative life curve C1 obtained by the life acceleration test and the actual value plot. The life prediction curve generation unit 55 represents the time axis up to the time L1 of the representative life curve C1 in the horizontal axis direction in accordance with the time axis up to the time L2 that is the rated life according to the actual usage status of the target part. The life curve C1 is extended. The rated life is the life in use of a standard product.

When a specific example is described, the rated life when the ball bearing as the target part is a ball bearing is expressed as (C / P) ³ × 16667 / n. The rated life when the ball bearing as the target part is a roller bearing is expressed as (C / P) ^10/3 × 16667 / n. Here, “C” is a basic dynamic load rating, “P” is a dynamic equivalent load, and “n” is a rotational speed. The unit of “C” and “P” is Newton, and the unit of “n” is revolution per minute (rpm). The unit of rated life is hour.

For “P” that is a dynamic equivalent load, P = X _r × Fr + Y _a × Fa holds. Here, “X _r ” is a radial coefficient, “Fr” is a radial load, “Y _a ” is an axial coefficient, and “Fa” is an axial load. The unit of “Fr” and “Fa” is Newton. The life prediction curve generation unit 55 acquires each value of “C”, “n”, “X _r ”, and “Y _a ” from the specification parameter 15 read from the specification parameter storage unit 32. The life prediction curve generation unit 55 acquires each value of “Fr” and “Fa” from the setting profile. The setting profile is a file that defines information unique to the device 4B and a usage environment of the device 4B. Further, the life prediction curve generation unit 55 determines whether the ball bearing is a ball bearing or a bearing based on the set profile. The life prediction curve generation unit 55 may calculate a time L2 that is a rated life based on the specification parameter 15 and the setting profile.

The life prediction curve generation unit 55 generates the rated curve C2 that is the second curve by deforming the representative life curve C1 based on the failure threshold T and the time L2 that is the rated life. Constants “a”, “b”, and “c” of the exponential function Y = a × b ^x + c represented by the rated curve C2 are representative lifetimes in the X-axis direction until the vibration amplitude at the time L2 matches the failure threshold T. It is obtained by stretching the curve C1. The life prediction curve generation unit 55 extends the representative life curve C1 by scaling the X axis between the X axis and the Y axis.

FIG. 10 is a third diagram for explaining the generation of the life prediction curve by the life prediction curve generation unit 55 shown in FIG. The life prediction curve generation unit 55 reads out the actual measured value of vibration amplitude from the industrial data 16 from the industrial data storage unit 31 and plots the read actual value on the time axis up to the present. When the target component is a bearing, the vibration amplitude can be extracted from data obtained by performing FFT on the motor current value acquired by the device 4B by the actual measurement value calculation unit 51. The life prediction curve generation unit 55 generates an actual measurement curve C3 representing an exponential function Y = a ′ × b ^x + c ′ by approximation of an actual measurement value. The life prediction curve generation unit 55 generates an actual measurement curve C3 that is a third curve representing the relationship between the actual measurement value of vibration amplitude, which is an actual measurement value corresponding to the failure mode value, and time. The constant “b” of the actual measurement curve C3 is matched with the constant “b” of the rated curve C2. The time L3 is a time when the vibration amplitude reaches the failure threshold T in the actual measurement curve C3.

  The constant “a ′” may be a negative value when the most recent measured value from the current time is smaller than the most recently measured value. In this case, the life prediction curve generation unit 55 may use the constant “a ′” calculated in the previous life prediction to generate the actual measurement curve C3. Alternatively, when the previous constant “a ′” does not exist, the life prediction curve generation unit 55 may use the constant “a” of the representative life curve C1 for generation of the actual measurement curve C3.

  FIG. 11 is a fourth diagram illustrating the generation of the life prediction curve C4 by the life prediction curve generation unit 55 shown in FIG. The life prediction curve generation unit 55 generates a life prediction curve C4 by mixing the rated curve C2 and the actual measurement curve C3. Thereby, the preventive maintenance processing unit 25 obtains a life prediction curve C4 generated based on the rated curve C2 and the actual measurement curve C3. The life prediction curve generation unit 55 generates a life prediction curve C4 by applying a weight representing the degree of dominance of the actual measurement curve C3 in the life prediction curve C4 to the actual measurement curve C3. Accordingly, the life prediction curve generation unit 55 changes the ratio of the actual measurement curve C3 included in the life prediction curve C4.

  The life prediction curve generation unit 55 changes the weighting ratio p used for generation of the life prediction curve C4 with 0% as the lower limit and 100% as the upper limit. When the weighting ratio p is 0%, the life prediction curve C4 matches the rated curve C2. When the weighting ratio p is 100%, the life prediction curve C4 matches the actual measurement curve C3. The time L4 is a time when the vibration amplitude reaches the failure threshold T in the life prediction curve C4.

  Here, a setting example of the weighting ratio p will be described. The weighting ratio p is determined based on the vibration amplitude condition on the vertical axis and the time condition on the horizontal axis shown in FIG. The vibration amplitude condition is the Y-axis condition, and the time condition is the X-axis condition.

  In the initial life prediction after starting the operation of the device 4B, the weighting ratio p is set to 0% based on the X-axis condition that it is the first time. Further, when the time from the start of the operation of the device 4B to the current time exceeds the time L2 that is the rated life of the target component, the weighting ratio p is 100% based on the X-axis condition that the time L2 has been exceeded. And

  When the actual measured value of the vibration amplitude is constant with the actual measured value in the previous life prediction, the weighting ratio p is determined in the previous life prediction based on the Y-axis condition that the vibration amplitude is constant. The same as the weighting ratio p. Note that the fact that two actually measured values are constant means that the difference between the two actually measured values is within a preset percentage range.

  Based on the Y-axis condition that the vibration amplitude has increased when the actual measured value of the vibration amplitude is not constant with the actually measured value at the time of the previous life prediction and the current measured value has increased from the previous measured value. Thus, the weighting ratio p is increased from the previous time. In addition to this condition, when the X-axis condition that the time from the start of operation of the device 4B to the present time is less than 70% of the time L2 is satisfied, the weighting ratio p is increased by 10% from the previous time. If the X-axis condition that the time from the start of operation of the device 4B to the present time is 70% or more and less than 80% of the time L2 is satisfied, the weighting ratio p is increased by 20% from the previous time. If the X-axis condition that the time from the start of operation of the device 4B to the present time is 80% or more and less than 90% of the time L2 is satisfied, the weighting ratio p is increased by 30% from the previous time. Further, when the X-axis condition that the time from the start of operation of the device 4B to the present time is 90% or more and less than 100% of the time L2 is satisfied, the weighting ratio p is increased by 40% from the previous time.

  Based on the Y-axis condition that the vibration amplitude has decreased when the actual measured value of the vibration amplitude is not constant with the actually measured value at the time of the previous life prediction and the current measured value has decreased from the previous measured value. Thus, the weighting ratio p is decreased from the previous time or the same as the previous time. In addition to this condition, when the X-axis condition that the time from the start of operation of the device 4B to the present time is less than 70% of the time L2 is satisfied, the weighting ratio p is decreased by 10% from the previous time. Further, when the X-axis condition that the time from the start of operation of the device 4B to the present time is 70% or more and less than 100% of the time L2 is satisfied, the weighting ratio p is the same as the previous time.

  In this way, by setting the weighting ratio p based on the X-axis condition, the life prediction curve generation unit 55 is in an initial stage after the operation of the device 4B is started, and when the accumulation of actual measurement values is small. Then, the life prediction curve C4 weighted so that the rated curve C2 becomes dominant as compared with the actual measurement curve C3 is generated. As a result, the preventive maintenance processing unit 25 can perform life prediction that places a weight on the rated life at a time when the accumulation of actual measurement values is small. Moreover, the lifetime prediction curve generation unit 55 performs weighting that increases the degree of control of the actual measurement curve C3 as time elapses. The life prediction curve generation unit 55 changes the life prediction curve C4 so as to approach the actual measurement curve C3 as the accumulation of actual measurement values increases as time passes. As a result, the preventive maintenance processing unit 25 can perform life prediction by placing a weight on the accumulated measured values as the accumulated measured values increase. In addition, by setting the weighting ratio p based on the Y-axis condition, the life prediction curve generation unit 55 performs weighting that increases the degree of dominance of the actual curve C3 as the actual value of the vibration amplitude increases. The life prediction curve generation unit 55 changes the life prediction curve C4 so as to approach the actual measurement curve C3 as the vibration amplitude increases. Thereby, the preventive maintenance processing unit 25 can perform life prediction in accordance with the situation where the actual measurement value is increasing.

  The life prediction unit 56 obtains the time L4 by substituting the failure threshold T into the exponential function represented by the life prediction curve C4 generated by the life prediction curve generation unit 55. The life prediction unit 56 calculates the remaining life that is the time from the present to the time L4. The life prediction unit 56 includes the failure mode specified by the failure mode specification unit 54, the remaining life that is the result 17 of the life prediction calculated by the life prediction unit 56, and the identification score calculated by the failure mode specification unit 54. Is sent to the lifetime data storage unit 33. The life data storage unit 33 stores the failure mode, the remaining life, and the identification score. The display device 5 displays the failure mode read from the life data storage unit 33, the remaining life, and the identification score.

  FIG. 12 is a flowchart showing a processing procedure by the data processing apparatus 2 after the preventive maintenance algorithm 12 shown in FIG. 2 is selected. In step S11, the failure mode calculation unit 52 calculates the failure frequency of each failure mode of the target part. In step S12, the actual measurement value calculation unit 51 calculates an actual measurement value of the frequency of vibration generated in the component.

  In step S <b> 13, the failure mode specifying unit 54 compares the actually measured frequency, which is an actually measured value, with the failure frequency, and specifies the failure mode of the target component. In step S14, the failure mode specifying unit 54 calculates an identification score of the specified failure mode. The representative life curve selection unit 53 selects a representative life curve and a failure threshold based on the specified failure mode.

  In step S <b> 15, the life prediction curve generation unit 55 reads the representative life curve C <b> 1 and the failure threshold T selected by the representative life curve selection unit 53 from the representative life curve storage unit 34. In step S16, the life prediction curve generation unit 55 generates a life prediction curve C4 based on the read representative life curve C1.

  FIG. 13 is a flowchart showing a procedure of processing for generating a life prediction curve C4 by the life prediction curve generation unit 55 shown in FIG. In step S21, the life prediction curve generation unit 55 obtains the rated curve C2 by extending the time axis of the representative life curve C1 based on the rated life and the failure threshold T.

  In step S <b> 22, the life prediction curve generation unit 55 obtains an actual measurement curve C <b> 3 based on the actual measurement values of vibration amplitude up to the present. In step S23, the life prediction curve generation unit 55 obtains a life prediction curve C4 according to the weighting by applying a weight to the rating curve C2 so as to approach the actual measurement curve C3. Thereby, the process which produces | generates the lifetime prediction curve C4 by the lifetime prediction curve generation part 55 is complete | finished.

  In step S <b> 17 shown in FIG. 12, the life prediction unit 56 calculates the remaining life of the target part based on the life prediction curve C <b> 4 generated by the life prediction curve generation unit 55. In step S18, the life data storage unit 33 stores the failure mode specified in step S13, the remaining life calculated in step S17, and the identification score calculated in step S14. In step S19, the display device 5 displays the failure mode read from the lifetime data storage unit 33, the remaining lifetime, and the identification score. Thereby, the data processing device 2 ends the processing shown in FIG.

  Part or all of the processing by the function of the data processing device 2 of the first embodiment may be performed in the cloud server 3. The cloud server 3 may hold a failure model that is a calculation formula for the failure mode value, and calculate the failure mode value and specify the failure mode.

  According to the first embodiment, the data processing device 2 includes the algorithm selection unit 26 that selects the preventive maintenance algorithm 12 corresponding to the target part. Compared with the case where an algorithm specially constructed for the configuration of the production apparatus is installed in the preventive maintenance application 10, the burden required for constructing the preventive maintenance application 10 can be reduced. Thereby, there is an effect that it is possible to reduce a burden required for constructing an application used for data processing for preventive maintenance of the apparatus.

Embodiment 2. FIG.
The data processing apparatus 2 according to the second exemplary embodiment of the present invention changes the execution cycle of the life prediction for each part according to the elapsed time since the use of the part is started for each part constituting the device 4B. . The data processing device 2 according to the second embodiment has the same configuration as the data processing device 2 according to the first embodiment. The preventive maintenance management unit 24, which is an execution cycle management unit, changes the execution cycle in accordance with the elapsed time since the use of the component is started.

  The increase in the actual measurement value of the vibration amplitude becomes earlier as the period approaches the life of the component. In the second embodiment, the preventive maintenance management unit 24 shortens the execution cycle of the life prediction and increases the execution frequency of the life prediction processing as the elapsed time from the start of the use of the parts becomes longer. The preventive maintenance management unit 24 may change the execution cycle of the life prediction based on the weighting ratio p in the first embodiment. As a result, the preventive maintenance management unit 24 increases the execution frequency of the life prediction process as the measured value of the vibration amplitude increases and as time elapses.

  According to the second embodiment, the data processing device 2 changes the life prediction execution cycle for each part according to the elapsed time since the use of the part is started, and thus according to the degree of increase in the actual measurement value. The execution frequency of the life prediction process can be changed. Thereby, the data processing device 2 can increase the prediction accuracy of the remaining life.

  The configuration described in the above embodiment shows an example of the contents of the present invention, and can be combined with another known technique, and can be combined with other configurations without departing from the gist of the present invention. It is also possible to omit or change the part.

  1 data processing system, 2 data processing device, 3 cloud server, 4A, 4B, 4C device, 5 display device, 10 preventive maintenance application, 11 task handler, 12 preventive maintenance algorithm, 13 threads, 14 setting information, 15 specification parameters , 16 Industrial data, 20 Control unit, 21 Storage unit, 22 Communication unit, 23 Input unit, 24 Preventive maintenance management unit, 25 Preventive maintenance processing unit, 26 Algorithm selection unit, 30 Algorithm storage unit, 31 Industrial data storage unit, 32 Specification parameter storage unit, 33 Life data storage unit, 34 Representative life curve storage unit, 35 Setting information storage unit, 40 CPU, 41 RAM, 42 HDD, 43 Communication circuit, 44 Input device, 45 bus, 51 Actual value calculation unit , 52 Failure mode calculation unit, 53 Bed selecting unit, 54 failure mode identification unit 55 life prediction curve generation unit, 56 life prediction unit.

In order to solve the above-described problems and achieve the object, the data processing apparatus according to the present invention stores an algorithm for predicting the lifetime of the parts constituting the apparatus and can be selected for each type of part. The algorithm selection unit that selects the algorithm corresponding to the target part that is the target of life prediction from the parts that constitute the device, the algorithm selected by the algorithm selection part, and the part that is unique to each part And a life prediction processing unit that executes a process for predicting the life of the target part based on the specification parameters as information .

Claims (16)

An algorithm selection unit that selects an algorithm corresponding to a target component that is a target of lifetime prediction among the components that configure the device, from an algorithm storage unit that stores an algorithm for performing lifetime prediction of the components that configure the device; ,
Based on the algorithm selected by the algorithm selection unit, a life prediction processing unit that executes a life prediction process of the target part;
A data processing apparatus comprising:   The data processing apparatus according to claim 1, wherein the algorithm selection unit selects an algorithm based on identification information for identifying an algorithm for each component.   3. The algorithm selection unit according to claim 1, wherein the algorithm selection unit selects an algorithm corresponding to the target component that has reached the execution cycle based on information on an execution cycle of the life prediction for each component. Data processing equipment. An execution cycle management unit for managing the execution cycle for each component;
The data processing apparatus according to claim 3, wherein the execution cycle management unit changes the execution cycle in accordance with an elapsed time after the use of the component is started. The algorithm selection unit inputs specification parameters, which are information unique to the target part, to the life prediction processing unit,
The life prediction processing unit executes the life prediction processing using the algorithm selected by the algorithm selection unit and the specification parameters, according to any one of claims 1 to 4. The data processing apparatus described. The life prediction processing unit
A failure mode calculation unit that calculates a failure mode value that is a numerical value used to specify a failure mode indicating a failure cause of the component;
Based on the failure mode value, a failure mode identifying unit that identifies the failure mode of the target part;
The data processing apparatus according to claim 1, further comprising: A first curve representing a relationship between an actual measurement value obtained in a test for verifying the life of the component and time, and a failure threshold which is the actual measurement value when the component fails in the test. A storage unit for storing,
The life prediction processing unit reads the first curve and the failure threshold for the target part from the storage unit, and deforms the first curve based on the failure threshold and the rated life of the target part. A life prediction curve generation unit that generates a life prediction curve that is used to calculate a life expectancy curve used to calculate the life expectancy of the target part based on the second curve. The data processing apparatus according to claim 6.   The life prediction curve generation unit generates a third curve representing a relationship between an actual measurement value corresponding to the failure mode value and time, and the life prediction is performed by mixing the second curve and the third curve. The data processing apparatus according to claim 7, wherein a curve is generated.   9. The life prediction curve generation unit generates the life prediction curve by applying a weight to the third curve that represents the degree of dominance of the third curve in the life prediction curve. The data processing apparatus described.   The data processing apparatus according to claim 9, wherein the life prediction curve generation unit performs the weighting that increases the degree of dominance of the third curve as time elapses.   The data processing apparatus according to claim 9, wherein the life prediction curve generation unit performs the weighting that increases the degree of dominance of the third curve as the actual measurement value increases.   The failure mode specifying unit calculates an identification score that represents a probability that a phenomenon observed as an actual measurement value corresponding to the failure mode value is a phenomenon caused by a failure in the specified failure mode. The data processing apparatus according to claim 6. An algorithm selection unit that selects an algorithm corresponding to a target component that is a target of lifetime prediction among the components that configure the device, from an algorithm storage unit that stores an algorithm for performing lifetime prediction of the components that configure the device; ,
Based on the algorithm selected by the algorithm selection unit, a life prediction processing unit that executes a life prediction process of the target part;
A data processing system comprising: Data processing device
A step of selecting an algorithm corresponding to a target part that is a target of life prediction among parts constituting the apparatus, from an algorithm for performing life prediction of parts constituting the apparatus;
Executing a process of predicting the life of the target part based on the selected algorithm;
A data processing method comprising: A data processing program that causes a computer to function as a data processing device that performs a process of predicting the lifetime of components that constitute the device,
Selecting an algorithm corresponding to a target part that is a target of life prediction from among the parts that constitute the device, from an algorithm for performing life prediction of the part;
Performing a process of predicting the life of the target part based on the selected algorithm;
A data processing program for causing a computer to execute.   A storage medium storing the data processing program according to claim 15 and capable of being read by a computer.

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