Patent Publication Number: US-2023147470-A1

Title: Failure prediction system

Description:
TECHNICAL FIELD 
     This invention relates to a failure prediction system. 
     BACKGROUND ART 
     The hydraulic oil used in equipment oxidizes and gradually deteriorates with equipment use. Therefore, in order to avoid equipment failure and shortened equipment life, the deterioration state of the hydraulic oil must be properly estimated, and the fluid must be replenished or replaced at the appropriate time. 
     In order to meet the above requirements, a technology has been disclosed to reduce the time required to determine the degree of deterioration of the hydraulic oil after the hydraulic oil in use in the equipment is sampled (see, for example, patent document 1). 
     Citation List 
     Non-Patent Document 
      [Patent Document 1] JP 2016-20864 A. 
     SUMMARY OF INVENTION 
     Technical Problem 
     However, the technology described in the patent document 1 assumes that hydraulic oil is collected while the equipment is in use. As a result, equipment operation must be stopped when collecting hydraulic oil, which affects productivity and work efficiency. 
     Related to the above problem, assuming that hydraulic oil is collected while the equipment is in use, the condition of the hydraulic oil cannot be evaluated frequently, making it difficult to perform predictive maintenance accurately even if the determination time is reduced. 
     Furthermore, although the technology described in patent document 1 estimates the degree of deterioration of hydraulic oil, it does not determine equipment failure. 
     The purpose of the present invention, therefore, is to provide a failure prediction system that outputs not only the state of hydraulic oil but also information on failure prediction of equipment by processing in real time. 
     Solution of Problem 
     First Aspect: At least one embodiment of the present invention proposes a failure prediction system of an equipment characterized in comprising: an oil condition sensor attached to the equipment; a parameter calculator to calculate values of the plurality of parameters indicating the state of the hydraulic oil based on a correlation information between a sensor output of the oil condition sensor and the values of the plurality of parameters indicating the state of the hydraulic oil and the sensor output; a failure prediction determiner that predicts a failure of the equipment based on the values of the parameters and identifies and outputs the parameters that are inferred as the cause of the failure prediction. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    shows the configuration of failure prediction system according to the first embodiment. 
         FIG.  2    shows the configuration of the parameter calculator according to the first embodiment. 
         FIG.  3    shows the configuration of the failure prediction determiner according to the first embodiment. 
         FIG.  4    is the flowchart showing the process of the failure prediction system according to the first embodiment. 
         FIG.  5    shows the correlation stored in the correlation function storage of the parameter calculator according to the first embodiment. 
         FIG.  6    is the flowchart showing the process of the failure prediction system according to the second embodiment. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     First Embodiment 
     Hereinafter, a failure prediction system  1  according to a first embodiment of the present invention will be described with reference to  FIGS.  1  to  5   . 
     Configuration of a Failure Prediction System 
     The configuration of a failure prediction system  1  according to the first embodiment of the present invention will be described with reference to  FIGS.  1  to  3   . 
     As shown in  FIG.  1   , the failure prediction system  1  comprises an oil condition sensor  10 , a parameter calculator  20 , and a failure prediction determiner  30 . 
     The oil condition sensor  10  is an oil condition sensor that detects the oil condition of the hydraulic oil, is mounted so that the sensing member is immersed in the hydraulic oil of the equipment  100  to be sensed, and acquires information including, for example, the relative permittivity and conductivity of the hydraulic oil. 
     The oil condition sensor  10  is connected to the parameter calculator  20  (described below) wirelessly or via a network, and outputs the information acquired by the oil condition sensor  10  sequentially or at the request of the parameter calculator  20 . 
     The parameter calculator  20  obtains the values of the plurality of parameters indicating the state of the hydraulic oil based on the correlation information between the sensor output of the oil condition sensor  10  and the values of the plurality of parameters indicating the state of the hydraulic oil and the sensor output. 
     The examples of parameters indicating the condition of the hydraulic oil may be parameters including total acid number, degree of contamination, metallic elements, and moisture. 
     Here, the parameter calculator  20  may be a stand-alone device or, for example, a server in the cloud. 
     The failure prediction determiner  30  outputs failure prediction information of equipment  100  in real time from the values of correlated parameters. 
     Here, the failure prediction determiner  30  inputs the values of correlated parameters, performs machine learning, and outputs failure prediction information of the equipment  100 . 
     For example, as the failure prediction information, the information including the timing of failure and the contribution of parameters indicating the condition of the hydraulic oil can be exemplified. 
     The failure prediction determiner  30  may be a stand-alone device or, for example, a server in the cloud. 
     It may be a stand-alone device that combines the failure prediction determiner  30  and the parameter calculator  20 , or it may be a server in the cloud, for example. 
     Configuration of Parameter Calculator  20   
     The parameter calculator  20  consists of a calculator  21 , a correlation function storage  22 , and a controller  23 , as shown in  FIG.  2   . 
     The calculator  21  calculates values of parameters indicating the oil condition of the hydraulic oil based on information obtained from the oil condition sensor  10 , for example, sensor information including relative permittivity and conductivity, and correlation functions stored in the correlation function storage  22  described below. 
     The correlation function storage  22  stores correlation functions that indicate the correlation between sensor information obtained from the oil condition sensor  10  and parameters indicating the oil condition of the hydraulic oil. 
     Correlation functions are calculated by mathematical regression from the vast amount of data collected and include, for example, correlation functions between relative permittivity and total acid number, relative permittivity and degree of contamination, relative permittivity and moisture, conductivity and degree of contamination, conductivity and metallic elements, and conductivity and moisture. 
     The controller  23  controls the operation of the calculator  21  according to a control program stored in ROM (Read Only Memory) etc. 
     Configuration of the Failure Prediction Determiner  30   
     As shown in  FIG.  3   , the failure prediction determiner  30  comprises a failure prediction determination algorithm  31 , a learning model storage  32 , a controller  33 , and an information memory  34 . 
     The failure prediction determination algorithm  31  is an algorithm for performing machine learning in the failure prediction determiner  30 , and it takes as input the values of the parameters indicating the oil condition of the hydraulic oil calculated in the parameter calculator  20 , performs machine learning using the learning model described below, and outputs failure prediction information including failure timing and contribution of the parameters indicating the oil condition of the hydraulic oil, for example, and corresponding messages to prevent failure. 
     As an example of the failure prediction determination algorithm  31 , a boosting that forms a decision tree structure can be exemplified. 
     The learning model storage  32  stores learning models generated in advance. Here, the learning model is data from which rules and patterns (outputs) are learned based on input data. 
     The controller  33  controls the operation of the failure prediction determiner  30  according to a control program stored in ROM (Read Only Memory) etc. 
     In addition, the ROM and other memory elements of the controller  33  store corresponding messages etc. in response to failure prediction information to prevent failures before they occur. 
     The controller  33  causes all of the equipment  100  to perform failure prediction determination. In other words, the controller  33  causes the same model and the same period of use of the equipment  100  to perform the failure prediction determination. 
     The information memory  34  stores a database linking the information input from the parameter calculator  20  and the failure prediction information output from the failure prediction determination algorithm  31 . 
     Processing of Failure Prediction System 
     Processing of the failure prediction system according to the first embodiment of the present invention will be described with reference to  FIGS.  4  and  5   . 
     The controller  23  of the parameter calculator  20  monitors, for example, the output from the oil condition sensor  10  at predetermined time intervals, and outputs sensor information obtained from the oil condition sensor  10 , including, for example, relative permittivity and conductivity, to the calculator  21  (step S 101 ). 
     The calculator  21  of the parameter calculator  20  calculates the value of the parameter indicating the oil condition of the hydraulic oil based on the information input from the controller  23 , for example, sensor information including relative permittivity and conductivity, and the correlation function stored in the correlation function storage  22  described below (step S 102 ). 
     For example, the correlation function storage  22  stores correlation functions between relative permittivity and degree of contamination, correlation functions between relative permittivity and moisture, correlation functions between conductivity and degree of contamination, correlation functions between conductivity and metallic elements, correlation functions between conductivity and moisture, etc., including the correlation function between permittivity and total acid number as shown in  FIG.  5   . 
     The calculator  21  calculates the values of parameters indicating the oil condition of the hydraulic oil, for example, total acid number, degree of contamination, metallic elements, and moisture, based on sensor information including relative permittivity and conductivity and correlation functions stored in the correlation function storage  22 , and outputs them to the failure prediction determiner  30 . 
     The controller  33  of the failure prediction determiner  30  incorporates the calculation results of the parameter calculator  20  into the failure prediction determination algorithm  31 . 
     The failure prediction determination algorithm  31  takes as input the values of parameters indicating the oil condition of the hydraulic oil calculated in the parameter calculator  20  and executes machine learning using a learning model to display on a display (not shown) or output to a user terminal or the like in real time, for example, failure prediction information including failure timing, contribution of parameters indicating the oil condition, and corresponding messages etc. for preventing failures (step S 103 ) . 
     The information output from the parameter calculator  20  and the failure prediction information output from the failure prediction determination algorithm  31  are stored in the information memory  34  in the form of a database. 
     Effect 
     As explained above, in the failure prediction system  1  according to this embodiment, based on the sensor output of the oil condition sensor  10  attached to the equipment  100  and the correlation information between the sensor output and the values of multiple parameters indicating the state of the hydraulic oil, the parameter calculator  20  obtains the values of multiple parameters indicating the state of the hydraulic oil, and the failure prediction determiner  30  predicts a failure of the equipment  100  from the parameter values, identifies parameters that are speculated as the cause of failure prediction, and outputs failure prediction information in real time. 
     Therefore, the oil condition of the hydraulic oil and the failure of the equipment  100  can be monitored in real time based on the sensor outputs of the oil condition sensor  10  attached to the equipment  100  and the correlation information between the values of multiple parameters indicating the condition of the hydraulic oil and the sensor outputs, which enables early determination of when to replace the hydraulic oil appropriately and the failure of the equipment  100 . 
     In the failure prediction system  1  according to this embodiment, the parameter calculator  20  obtains the values of multiple parameters indicating the state of the hydraulic oil, and the failure prediction determiner  30  predicts failure of the equipment  100  from the values of the parameters, identifies the parameters that are inferred as the cause of the failure prediction, and outputs failure prediction information in real time. 
     In other words, since the failure prediction system  1  notifies users of failure prediction information after predicting the condition of hydraulic oil, the basis of failure prediction information can be presented to users based on the prediction results of hydraulic oil conditions. 
     In addition, since the failure prediction system  1  according to this embodiment displays on a display (not shown) or outputs to a user terminal or the like in real time information including the time of failure, the contribution of parameters indicating the state of the hydraulic oil, and a corresponding message to prevent failure, it can prompt the user to act quickly. 
     The parameter calculator in the failure prediction system  1  according to this embodiment obtains the values of the parameters from the sensor outputs of the oil condition sensors using the first learning data generated by machine learning with the previously measured parameter values as teacher data. 
     In other words, the parameter calculator  20  in the failure prediction system  1  according to this embodiment stores, for example, a correlation function between sensor outputs mathematically calculated based on collected data and parameters indicating the state of hydraulic oil, and uses first learning data generated by machine learning with previously measured parameter values as teacher data to obtain the values of parameters from the sensor outputs of the oil condition sensors. 
     Therefore, it is possible to predict how the values of parameters indicating the state of the hydraulic oil will change relative to changes in sensor output values due to the cumulative usage time of the equipment  100  and other factors. 
     The failure prediction determiner in the failure prediction system  1  according to this embodiment identifies and outputs said parameters that are inferred as causes of failure prediction of equipment from the values of the parameters calculated by the parameter calculator using a second learning model generated by machine learning, with the objective variable being whether the equipment is in a failed state or not, or in a state that is predictive of failure, and the parameter being an explanatory variable. 
     That is, for example, the failure prediction determiner in the failure prediction system  1  according to this embodiment identifies and outputs said parameters that are inferred as causes of failure prediction of equipment from the values of parameters calculated by the parameter calculator based on a second learning model generated by machine learning, using as objective variables whether the equipment is in a failed state or not, or whether it is in a state that is predicted to fail, and parameters as explanatory variables. 
     Therefore, it is possible to output highly accurate information on failure prediction of equipment in a short period of time. 
     The controller  33  in the failure prediction system  1  according to this embodiment will have the failure prediction determination performed for all equipment  100 , i.e., for devices of the same model and with the same period of use. 
     In general, it is expected that similar failure prediction trends will appear for the same model of equipment  100 , but the failure prediction trends differ in the contribution of parameters indicating the condition of the hydraulic oil, depending on the user’s usage. 
     Therefore, in the failure prediction system  1  according to this embodiment, by having the failure prediction determination performed on, for example, equipment  100  of the same model and with the same period of use, the failure prediction information for each equipment  100  can be reported to the user. 
     A function to generate a learning model may be added to the failure prediction system  1 , and the learning model may be generated by the information used in the process of acquiring failure prediction information and the failure prediction information, and the learning model used in the process of acquiring failure prediction information may be updated. 
     Thus, the accuracy of generating failure prediction information in the failure prediction system  1  can be improved by updating the learning model using the newly obtained information. 
     Second Embodiment 
     Hereinafter, the failure prediction system  1 A according to the second embodiment of the invention will be described with reference to  FIG.  6   . 
     Configuration of Failure Prediction System  1 A 
     The configuration of the failure prediction system  1 A according to the second embodiment of the invention will be described. 
     The failure prediction system  1 A according to this embodiment comprises an oil condition sensor  10 , a parameter calculator  20 , and a failure prediction determiner  30 A. 
     Detailed descriptions of the components with the same symbols as in the first embodiment are omitted, since they have the same functions. 
     The failure prediction determiner  30 A outputs failure prediction information of equipment  100  in real time from the values of correlated parameters. 
     The failure prediction determiner  30 A does not perform machine learning on the next and subsequent contribution for parameters whose contribution is lower than the set value. 
     Configuration of Failure Prediction Determiner  30 A 
     The failure prediction determiner  30 A comprises a failure prediction determination algorithm  31 , a learning model storage  32 , a controller  33 A, and an information memory  34 . 
     Detailed descriptions of the components with the same symbols as in the first embodiment are omitted, since they have the same functions. 
     The controller  33 A controls the operation of the failure prediction determiner  30 A according to a control program stored in ROM (Read Only Memory) etc. 
     The controller  33 A detects parameters whose contribution is lower than the set value based on the database linking the information input from the parameter calculator  20  and the failure prediction information output from the failure prediction determination algorithm  31  stored in the information memory  34 , and controls the failure prediction determiner  30 A not to perform machine learning on the contribution from the next time onward for the parameter in question. 
     Processing of Failure Prediction System  1 A 
     Processing of the failure prediction system  1 A according to the second embodiment of the present invention will be described with reference to  FIG.  6   . 
     The controller  23  of the parameter calculator  20 , for example, monitors the output from the oil condition sensor  10  at predetermined time intervals and outputs sensor information obtained from the oil condition sensor  10 , including, for example, relative permittivity and conductivity, to the calculator  21  (step S 201 ). 
     The calculator  21  of the parameter calculator  20  calculates values of parameters indicating the oil condition of the hydraulic oil based on information input from the controller  23 , for example, sensor information including relative permittivity and conductivity, and correlation functions stored in the correlation function storage  22  described below (step S 202 ). 
     The controller  33 A of the failure prediction determiner  30 A determines the presence or absence of parameters whose contribution is lower than a set value based on a database stored in the information memory  34  that links the information input from the parameter calculator  20  and the failure prediction information output from the failure prediction determination algorithm  31  (step S 203 ). 
     At this time, if it is determined that there is no parameter in the database whose contribution is lower than the set value (“NO” in step S 203 ), the controller  33 A of the failure prediction determiner  30 A executes control so that all calculation results of the parameter calculator  20  are incorporated into the failure prediction determination algorithm  31 , wherein the failure prediction determination algorithm  31  takes as input the values of parameters indicating the oil state of the hydraulic oil calculated in the parameter calculator  20  and executes machine learning using a learning model to display on a display (not shown) or output to a user terminal or the like in real time, for example, failure prediction information including failure timing, contribution of parameters indicating the condition of the hydraulic oil, and corresponding messages for preventing failures (step S 205 ). 
     On the other hand, if it is determined that there is parameter in the database whose contribution is lower than the set value (“YES” in step S 203 ), the controller  33 A of the failure prediction determiner  30 A executes control so that the calculated results of the parameters excluding the calculation results of the parameters whose contribution is lower than the set value among the calculation results of the parameter calculator  20  are incorporated into the failure prediction determination algorithm  31 , wherein the failure prediction determination algorithm  31  takes as input the values of parameters indicating the oil state of the hydraulic oil calculated in the parameter calculator  20  and executes machine learning using a learning model to display on a display (not shown) or output to a user terminal or the like in real time, for example, failure prediction information including failure timing, contribution of parameters indicating the oil condition, and corresponding messages for preventing failures (step S 204 ). 
     Effect 
     As explained above, the controller  33 A of the failure prediction determiner  30 A in the failure prediction system  1 A according to this embodiment determines the presence or absence of parameters whose contribution is lower than a set value based on a database stored in the information memory  34  that links the information input from the parameter calculator  20  and the failure prediction information output from the failure prediction determination algorithm  31 , and when it is determined that there is a parameter in the database whose contribution is lower than the set value (“YES” in step S 203 ), it executes control so that the calculated results of the parameters excluding the calculation results of the parameters whose contribution is lower than the set value among the calculation results of the parameter calculator  20  are incorporated into the failure prediction determination algorithm  31 , wherein the failure prediction determination algorithm  31  takes as input the values of parameters indicating the oil state of the hydraulic oil calculated in the parameter calculator  20  and executes machine learning using a learning model to display on a display (not shown) or output to a user terminal or the like in real time, for example, failure prediction information including failure timing, contribution of parameters indicating the oil condition, and corresponding messages for preventing failures. 
     Therefore, if there is a parameter whose contribution is lower than a set value based on the database linking the information input from the parameter calculator  20  and the failure prediction information output from the failure prediction determination algorithm  31  stored in the information memory  34 , the processing load of the failure prediction determiner  30 A can be reduced. 
     Variation 1 
     In the first and second embodiments, the parameter calculator  20  calculates the value of the parameter indicating the oil condition of the hydraulic oil based on the information obtained from the oil condition sensor  10 , for example, sensor information including relative permittivity and conductivity, and a correlation function calculated by mathematical regression from a vast amount of data stored in the correlation function storage  22  described below. 
     However, the parameter calculator  20  may calculate the value of the parameter from the correlation between the sensor information including relative permittivity and conductivity obtained from a vast amount of data using a specific algorithm and the value of the parameter indicating the oil condition of the hydraulic oil. 
     In this way, the values of parameters can be calculated with a high degree of accuracy even when the relationship between sensor information including relative permittivity and conductivity and the values of parameters indicating the oil condition of the hydraulic oil have a relationship that cannot be expressed by a function. 
     In Variation 1, it is exemplified that different algorithms are applied to the parameter calculator  20  and the failure prediction determiner  30  and  30 A to perform failure prediction determination. However, under the condition that it is limited to certain specific data, such as data with high dielectric constant, for example, the parameter calculator  20  and the failure prediction determiner  30  and  30 A may be operated using one algorithm. 
     In the first and second embodiments, the application of a machine learning classification algorithm to the failure prediction determiners  30  and  30 A is exemplified, but a theory-based or rule-based algorithm may also be applied. 
     Variation 2 
     The failure prediction determiner  30  may perform machine learning of the contribution even if the equipment  100  is the same model as the equipment  100  for which machine learning of the contribution of the parameters indicating the condition of the hydraulic oil has been performed so far. 
      In other words, even for the same model of equipment, different usage conditions, such as its usage method and operating environment, will result in different contributions, and different failure modes can be expected. 
     Therefore, even if the equipment is of the same model, the user can be notified of the true failure prediction information for that equipment by performing contribution machine learning. 
     The failure prediction systems  1  and  1 A of the present invention can be realized by recording the processes of the failure prediction systems  1  and  1 A on a recording medium readable by a computer system and having the program recorded on this recording medium read and executed by the failure prediction systems  1  and  1 A. The computer system here includes hardware such as an OS and peripheral devices. 
     “Computer system” shall include the homepage provision environment (or display environment) if the WWW (World Wide Web) system is used. The above program may be transmitted from a computer system storing this program in a memory device or the like to another computer system via a transmission medium or by transmission waves in a transmission medium. Here, “transmission medium” for transmitting the program refers to a medium that has the function of transmitting information, such as a network (communication network) such as the Internet or a communication channel (communication line) such as a telephone line. 
     The above programs may also be those that can be used to realize some of the aforementioned functions. Furthermore, it may be a so-called difference file (difference program), which can realize the aforementioned functions in combination with a program already recorded in the computer system. 
     Although the above embodiments of this invention have been described in detail with reference to the drawings, specific configurations are not limited to these embodiments, and includes designs and the like within a range that does not deviate from the gist of the present invention. 
     According to the above failure prediction system, it is possible to output not only the condition of hydraulic oil but also information on failure prediction of equipment through real-time processing. 
     DESCRIPTION OF THE REFERENCE NUMERALS 
     
         
           1 ; Failure prediction system 
           1 A ; Failure prediction system 
           10  ; Oil condition sensor 
           20  ; Parameter calculator 
           21  ; Calculator 
           22  ; Correlation function storage 
           23  ; Controller 
           30  ; Failure prediction determiner 
           30 A; Failure prediction determiner 
           31  ; Failure prediction determination algorithm 
           32  ; Learning model storage 
           33  ; Controller 
           33 A; Controller 
           34  ; Information memory 
           100  ; Equipment