JP2010225006A - Replacement component presentation method and replacement component presentation device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To support maintenance work related to replacement of a hardware component in an industry-oriented device comprising a plurality of hardware components by predicting deterioration of the device when the device includes a hardware component not having accumulation of sufficient knowledge, such as a newly developed hard ware component, and providing a replacement criterion allowing advance decision of whether or not performance deterioration of the device is within an allowable range. <P>SOLUTION: In this replacement component presentation method wherein a terminal device connected to the device including the plurality of hardware components presents the hardware component to be replaced in the device, the terminal device predicts improvement effect in the whole sequence when replacing the hardware component based on sequence information, performance information of the hardware component when it is a new article, and future performance information, and presents the hardware component to be replaced based on the predicted improvement effect. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to a replacement part presenting method and a replacement part presenting apparatus, and more particularly to a technology that supports execution determination of replacement work associated with deterioration or failure of hardware parts.

  When using a device consisting of multiple hardware parts, replace the hardware parts in a timely manner according to the hardware degradation status and whether or not there is a failure, and keep the device in a state where optimal processing performance can be obtained. is important.

  On the other hand, it is costly to frequently replace all hardware components in order to always obtain optimum processing performance. Therefore, at the time of hardware part replacement, it is necessary to compare the loss associated with the reduction in the processing performance of the apparatus with the loss associated with the replacement of the hardware part, and determine the replacement time and the hardware part to be replaced.

  One method for deciding to replace a hardware component is to determine a replacement standard that indicates the behavior and nature of the hardware component that is determined to require replacement, and replace the hardware component that meets the standard. There is.

  As an example of such a method, there is a method of monitoring the operation history of each hardware component, predicting a failure or performance degradation, and presenting the hardware component to be replaced so that replacement can be planned in advance. is there. (For example, refer to Patent Document 1).

  In such a conventional technique, the usage status of each hardware component is grasped and the future usage status is predicted. A part replacement time is predicted from the predicted future use state, and information on the replacement time is transmitted. In particular, in Patent Document 1, for a medical examination apparatus, a future use situation is predicted by a neural network or a genetic algorithm from a use situation such as the number of operations of an imaging apparatus for a predetermined period, and when the use situation exceeds the life of a component Is the parts replacement limit time. By notifying the part replacement limit time, the part replacement work is supported.

Japanese Patent Laying-Open No. 2005-235075

  However, the technique described in Patent Document 1 has the following problems.

  First, if processing performance such as production per hour is within an allowable range, it is important to keep losses associated with replacement of hardware parts as low as possible, but the technique described in Patent Document 1 solves this. I can't.

  Second, it is difficult to obtain a replacement standard in advance for newly developed hardware parts and hardware parts with large individual differences. In particular, low-cost hardware parts have large variations in deterioration from individual to individual, and it is difficult to assume the life and deterioration in advance.

  Third, industrial devices used in factories and the like are sometimes automatically controlled by a sequencer or control software and continue to perform routine operations, and the future situation can be predicted by linear prediction. In addition, it may be difficult to collect learning data necessary for a learning algorithm for newly developed hardware parts or hardware parts with large individual differences, and nonlinear prediction may not be suitable.

  In view of the above, an object of the present invention is to support hardware component replacement work in an apparatus including a plurality of hardware components, and to improve user convenience.

  It is another object of the present invention to reduce cost and apparatus operation burden by suppressing a decrease in apparatus processing performance by replacing fewer hardware parts.

  In order to solve the above problems, the present invention comprises the following arrangement. That is, a replacement component presentation method in which a terminal device connected to a device presents hardware components to be replaced in a device including a plurality of hardware components, and the processing of control software in which the terminal device controls the hardware components A sequence information acquisition step for acquiring sequence information indicating a flow, a new performance information acquisition step for the terminal device to acquire performance information when the hardware component is new, and a future performance information of the hardware component for the terminal device. The future performance information prediction step for predicting the performance, and the improvement effect for predicting the improvement effect of the entire sequence when the terminal device replaces the hardware parts based on the sequence information, the performance information at the time of the new product and the future performance information The predicting step and the terminal device based on the predicted improvement effect, It comprises a hardware component specifying step of specifying the hardware components to be exchanged Chi, and hardware components presenting step of presenting the identified hardware component, a.

  ADVANTAGE OF THE INVENTION According to this invention, the replacement | exchange operation | work of a hardware component can be supported and a user's convenience can be improved.

  In addition, it is possible to suppress a reduction in processing performance of the apparatus by replacing fewer hardware parts, and it is possible to realize cost reduction and apparatus operation burden reduction.

2 shows a typical system configuration and software configuration of an apparatus according to Embodiment 1 of the present invention. The system block diagram which shows an example of the maintenance object apparatus in Embodiment 1 of this invention. The flowchart which shows the procedure of the performance prediction method and replacement part identification method which concern on this invention. The flowchart which shows the procedure of the performance prediction which concerns on this invention. The flowchart which shows the procedure of the replacement part specification which concerns on this invention. FIG. 5 is a diagram showing an example of sequence design in the first embodiment of the present invention. The data structure figure showing an example of hardware operation time information concerning the present invention. The data structure figure showing an example of the prediction result of the operation time of the hardware parts concerning the present invention. The data structure figure showing an example of processing time information on a software processing block concerning the present invention. FIG. 5 is a flowchart showing details of processing time prediction processing in sequence design of the performance prediction procedure shown in FIG. 4; The figure which showed an example of the calculation method which concerns on prediction of the software single operation time and parallel operation time which are shown in FIG. FIG. 11 is a data structure diagram showing an example of a prediction result of the software single operation time and the parallel operation time shown in FIG. FIG. 5 is a diagram showing a result of processing time prediction on the sequence design of the performance prediction procedure shown in FIG. FIG. 6 is a data structure diagram showing an example of a new performance of the hardware component shown in FIG. 6 is a flowchart showing details of processing time calculation processing shown in FIG. The data structure figure which shows an example of the recalculation result of the parallel operation time shown in FIG. FIG. 6 is a diagram showing an example of a prediction result of processing performance after replacement shown in FIG. FIG. 6 is a data structure diagram showing an example of a calculation result of an improvement amount after replacement shown in FIG.

  Hereinafter, embodiments of the present invention will be described with reference to examples.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. Note that the present invention is not limited to the embodiments.

  FIG. 1 is a diagram of a system configuration and a software configuration of a representative maintenance target device and a maintenance terminal according to an embodiment of the present invention, a control program, a performance prediction program, and a replacement part identification program. FIG. 2 is a system configuration diagram of a maintenance terminal including a maintenance target device, a performance prediction method, and a replacement part identification method according to the first embodiment of the present invention. FIG. 3 is a flowchart showing an example of a processing procedure of the performance prediction method and the replacement part specifying method according to the embodiment of the present invention. FIG. 4 is a flowchart showing an example of processing performance prediction means in the performance prediction method according to the embodiment of the present invention, and FIG. 5 is a flowchart showing an example of processing means of the replacement part specifying method. FIG. 6 is an example of a sequence design used in the first embodiment. FIG. 7 shows the result of measuring the operating time of hardware components for a certain period in the system configuration of FIG. 2 and the sequence design of FIG. FIG. 8 is a diagram illustrating a result of predicting an operation time after a certain period of each hardware component based on the measurement result of FIG. FIG. 9 shows the result of measuring the software processing time for a certain period based on the sequence design of FIG. FIG. 10 is a flowchart showing details of the processing time prediction method on the sequence design of the performance prediction procedure shown in FIG. 4, and FIG. 11 is a software single operation time based on the performance prediction method according to the embodiment of the present invention, FIG. 12 is a diagram illustrating an example of a method for calculating the parallel operation time, and FIG. 12 is a data structure diagram illustrating an example of a prediction result of the software single operation time and the parallel operation time. FIG. 13 is a diagram showing the results of processing time prediction on the sequence design of FIG. 6 in the performance prediction procedure shown in FIG. FIG. 14 is a data structure diagram showing an example of new performance of hardware parts used in the replacement part specifying method according to the embodiment of the present invention. FIG. 15 is a flowchart showing details of processing time calculation processing in the replacement part specifying method according to the embodiment of the present invention shown in FIG. FIG. 16 is a data structure diagram showing an example of a recalculation result of the parallel operation time shown in FIG. FIG. 17 is a diagram showing an example of a prediction result of the processing performance after replacement shown in FIG. 5, and FIG. 18 is an improvement after replacement in the replacement part specifying method according to the embodiment of the present invention shown in FIG. It is a data structure figure which shows an example of the calculation result of quantity.

  First, referring to FIG. 1 and FIG. 2, performance prediction according to the embodiment of the present invention, a maintenance target device including a replacement part identification method, and software such as performance prediction and replacement part identification program installed in the apparatus An example of the configuration will be described.

  FIG. 1 shows an outline of a typical system configuration and software configuration of a maintenance target apparatus according to an embodiment of the present invention.

  The operation terminal 101 is a device for a user to perform normal device operations such as device operation and operation status confirmation, and may include an input device such as a keyboard, a mouse, and a disk device, and an output device such as a display and a printer. good.

  The hardware component 103 is hardware to be controlled, and operates according to an instruction from the control sequence 104.

  The control controller 102 includes a control sequence 104 that is software for controlling the hardware component 103, and a processing measurement unit 107 that measures the operation time of the control sequence 104.

  The control sequence 104 outputs a control signal to the hardware component 103 based on an instruction from the operation terminal 101 and a preset procedure, and receives a sensor response from the hardware component 103. Hereinafter, in the present embodiment, it is assumed that the control sequence 104 is software. However, the control sequence 104 may be hardware capable of configuring a process such as a relay circuit, and may be configured as a control procedure for the hardware component 103. Any may be used.

  The processing measurement unit 107 acquires processing contents such as software processing of the control sequence 104, control signal output, and sensor signal input, and transmits them to the measurement result reception unit. In this embodiment, the processing measurement unit 107 is mounted on the control controller 102 and measures both the control sequence 104 and the hardware component 103. However, the process measurement unit 107 may be a part of the control sequence 104 or the hardware component 103. Equivalent functions may be included, the control sequence 104 and the hardware component 103 may be measured by different means, or the processing of the control sequence 104 and the operation of the hardware component 103 can be measured. Any means can be used.

  When the control processing sequence 104 is software, the software itself is stored in a storage unit (not shown), and a control unit (not shown) such as a CPU reads the program from the storage unit and executes it. Processing is realized. The same applies to the processing measurement unit.

  The maintenance terminal 113 includes a measurement result receiving unit 108 that receives processing contents from the process measuring unit 107, a sequence design receiving unit 109 that receives sequence design information from a user, and a design recording unit 112 that records and stores the received sequence design. Based on the predicted future processing performance, the operating time recording unit 114 that records and stores the operating time information, the processing performance prediction unit 110 that predicts future processing performance from the accumulated past operating time information, and It includes a replacement part specifying unit 111 that creates part replacement information for improvement. The maintenance terminal 113 is connected to an input device 115 mainly for obtaining an input of sequence design and an output device 116 for mainly presenting predicted future processing performance and parts replacement information for performance improvement. ing.

  The measurement result receiving unit 108 receives the processing content from the process measuring unit 107, identifies the execution location on the sequence design in the design recording unit 112, and performs the hardware operation, software processing, and waiting process described in the sequence design. The processing time is calculated and recorded in the operation time recording unit.

  The sequence design receiving unit 109 provides an interface for the user to input the sequence design from the input device 115, receives the sequence design, and records it in the design recording unit 112.

  The design recording unit 112 receives and records and accumulates the sequence design received by the sequence design receiving unit 109, and provides it to the measurement result receiving unit 108, the processing performance prediction unit 110, and the replacement part specifying unit 111. Hereinafter, in this embodiment, the design recording unit 112 assumes a database in the maintenance terminal 113, but an external disk such as a floppy (registered trademark), a compact flash (registered trademark), MOO, or CD connected to the maintenance terminal 113. It may be recorded in the apparatus.

  The operation time recording unit 114 records and accumulates the control sequence 104 received by the measurement result receiving unit 108 and the processing time of the hardware component 103, and the measurement result receiving unit 108, the processing performance prediction unit 110, and the replacement part specifying unit 111. To provide. Hereinafter, in this embodiment, the operation time recording unit 114 is assumed to be a database in the maintenance terminal 113, but an external disk such as a floppy (registered trademark), compact flash (registered trademark), MOO, or CD connected to the maintenance terminal 113. It may be recorded in the apparatus.

  When the sequence design receiving unit 109 is software, the software itself is stored in a storage unit (not shown), and a control unit (not shown) such as a CPU of the maintenance terminal 113 reads this from the storage unit. The processing is realized by executing. The same applies to the processing performance prediction unit 110, the replacement part identification unit 111, and the measurement result reception unit.

  The input device 115 is a device for a user to input a sequence design which is design information of the control sequence 104, and may include a disk device such as a floppy (registered trademark), a compact flash (registered trademark), MOO, or CD. Alternatively, an operation device such as a keyboard, a mouse, or a touch pad may be included.

  The output device 116 may be any device capable of presenting parts replacement information for future processing performance and performance improvement to the user. The output device 116 may be a display device such as a display, a printing device such as a printer, or an e-mail. The communication IF for presentation by the communication means may be used.

  In this embodiment, the maintenance terminal 113 and the software group stored in the maintenance terminal 113 are system components different from the control controller 102. However, the software is not provided in the control controller 102 without the special maintenance terminal 113. It is good also as a structure where a group is stored.

  FIG. 2 is a system configuration diagram of the maintenance target apparatus according to the first embodiment of the present invention. FIG. 2 is an example of a system configuration of an apparatus to be installed on a production line or the like and used as a maintenance target for performing maintenance work such as replacement of hardware parts in the embodiment of the present invention.

  In FIG. 2, the maintenance target device is a conveyor A201 for moving a conveyed product, an imager 203 and an imaging stage motor 204 for imaging the surface or inside of the conveyed product, and image processing for analyzing an image as a result of imaging. 214, arm 205 that sorts the destination of the transported object according to the imaging result, conveyor B206 for moving the transported object after imaging, control controller 102 for controlling the hardware parts group, operation terminal for operating the device 101, a maintenance terminal 113 that provides a performance prediction method / replacement part identification method according to an embodiment of the present invention, an input device 115 for inputting design information to the maintenance terminal 113, and a result from the maintenance terminal 113 is presented. The output device 116 is configured. Hereinafter, the operation of each unit will be described.

  The conveyor A201, the imager 203, the imaging stage motor 204, the arm 205, and the conveyor B206 image processor 214 are hardware components to be controlled connected to the maintenance target device, and the performance prediction method / exchange according to the embodiment of the present invention It is a hardware component that is subject to prediction and replacement in the component identification method.

  The conveyor A201 is loaded with the conveyed product 202 and moves to the lower side of the image pickup device 203. The image pickup device 203 takes an image of the conveyed product 202 on the image pickup stage motor 204 and transfers the image data to the image processor 214. Hereinafter, the image pickup device 203 captures the appearance of the conveyed object 202 with an optical camera, but it may be a non-optical imaging device such as an electron microscope, a sensor such as an infrared sensor, Any hardware that can inspect the shape and properties may be used.

  The imaging stage motor 204 adjusts the imaging position when imaging the conveyed object 202, and can capture an appropriate image. The image processor 214 receives image data from the image pickup device 203 and determines the characteristics of the conveyed product 202. Here, the characteristic is an external or internal characteristic of the conveyed product 202 that is clarified by analyzing the image data. For example, if the conveyed product 202 is a product on the production line, the quality of the product and the defect There is presence or absence.

  The arm 205 changes the movement destination of the conveyed product 202 based on the determination result in the image processor 214. For example, if the conveyed product 202 is a product on the production line, if it is determined that there is no defect in the conveyed product 202, it is carried to the next process as a good conveyed item 207, and if it is determined that there is a defect, the bad conveyed item 209 Removed from the production line.

  Based on the user instruction from the operation terminal 101 and the contents of the control sequence, the controller 102 determines the hardware parts of the conveyor A201, the imager 203, the imaging stage motor 204, the arm 205, and the conveyor B206 image processor 214 in advance. Control in order.

  The maintenance terminal 113 records the operation history of each hardware component based on the sequence design received from the input device 115, predicts the performance of the maintenance target device after a certain period, and improves the performance of the hardware component. Exchange information is presented from the output device 116 to the user.

  Next, FIG. 6 shows the operation of the maintenance target apparatus in the first embodiment of the present invention shown in FIG. 2, and shows an example of sequence design targeted by the performance improvement method and the replacement part presentation method.

  FIG. 6 is a diagram showing an example of sequence design, and describes a processing order for controlling the hardware component group of the maintenance target device shown in FIG. 2 in the first embodiment of the present invention. The sequence design shown in FIG. 6 is assumed to be implemented (coded) as a control sequence 104 and stored in the control controller 102. Here, an example using SFC (sequential function chart) is shown as sequence design. However, the description method may be a description method in other diagram formats such as state transition diagrams and Petri nets, or a description in a language format such as ladder. Any method may be used as long as the flow of processing of the control sequence 104, the output of control signals between the hardware components and the control sequence 104, and the input timing of the sensor response can be expressed.

  Hereinafter, an example of the operation of the control sequence 104 in the maintenance target apparatus in FIG. 2 will be described using the sequence design in FIG. When the conveyed product 202 is put on the apparatus and an operation start instruction is received from the input device 115, the control sequence 104 starts to be executed, and the conveyed product 202 is removed by the conveyor B206 or the arm 205, and the process is terminated.

  First, the control sequence 104 transmits an operation start control signal to the conveyor A201 (conveyor A operation start 601), and waits until the conveyed product 202 placed on the conveyor A201 moves onto the imaging stage motor 204 (602). Thereafter, an imaging preparation start control signal is transmitted to the image pickup device 203 (image pickup image pickup preparation start 607). In parallel with the start of imaging preparation, a control signal for moving the conveyed object 202 to a position where imaging can be performed is transmitted to the imaging stage motor 204 (stage movement start 606). At the same time, an operation stop control signal is transmitted to the conveyor A201 in which the image pickup device 203 has disappeared (conveyor A operation stop 619). After receiving a sensor response (604) indicating completion of preparation from the image pickup device 203 and a sensor response (608) indicating completion of movement from the image pickup stage motor 204, a control signal for starting image pickup is transmitted to the image pickup device 203. (Imager imaging start 612). After receiving the sensor response indicating completion of imaging (613), an analysis start instruction is transmitted to the image processor 214 (image processor analysis start 616), and simultaneously, a control signal for starting operation is transmitted to the conveyor B206 to convey the object 202. From the bottom of the imager 203 (Conveyor B operation start 615). After receiving the analysis completion report (623) from the image processor 214 and the sensor response (617) indicating the completion of the movement of the conveyed product 202 to the front of the arm 205 where the conveyor B206 is the terminal, a control signal for starting the operation to the arm 205 To move the conveyed object 202 in accordance with the analysis result (arm operation start 622). When the movement operation by the arm 205 is successful (626), the operation of the control sequence 104 is normally completed. When the movement operation by the arm 205 fails (626), the operation of the conveyor B206 is stopped urgently (conveyor B operation stop 535, 636), and the operation of the control sequence 104 is ended.

  Next, the processing procedure of the processing performance prediction method and the replacement part identification method for performance improvement according to the embodiment of the present invention is shown by the flowcharts of FIGS. 3, 4, and 5, and the processing performance prediction in the maintenance terminal 113 is shown. Operations of the unit 110 and the replacement part specifying unit 111 will be described.

  FIG. 3 is a flowchart showing a processing procedure of a processing performance prediction method and a replacement part specifying method for performance improvement before and after the operation start of the maintenance target device. FIG. 4 is a diagram showing a performance prediction method.

  First, before the apparatus is operated, in step 301, the sequence design is acquired from the user through the input device 115 and the sequence design receiving unit 109. The acquired sequence design is recorded in the design recording unit 112.

  Next, in step 302, a new performance of each hardware component is acquired from the input device 115 and the sequence design receiving unit 109 from the user. The acquired new performance is recorded in the design recording unit 112.

  Here, the new product performance is the performance of the hardware component expected after replacing each hardware component, and includes at least the hardware operation time until the control signal is received and the sensor response is returned. Here, the new performance is created and input by the user in advance from the specifications of the hardware parts and the result of the operation test. In the above example, the new product performance is created and input by the user in advance. For example, hardware performance time information measured by the process measurement unit 107 and accumulated in the operation time recording unit 114 is used as hardware performance information. The first measured value after the operation of the maintenance target device during the operation time of the wear part may be used as the new product performance value, the average value of the elapsed time during the first fixed period after operation may be used as the new product performance value, and the elapsed time The new product performance may be linearly predicted based on the change tendency of the correlation coefficient, etc., and may be obtained by any method as long as the value indicates the performance before the hardware components are deteriorated.

  Next, after the apparatus is operated (step 303), the following processing is continued.

  In step 304, the operation status of each hardware component and the control sequence 104 is acquired by the process measurement unit 107 and the measurement result reception unit 108 and recorded in the operation time recording unit 114. At that time, the contents to be measured and recorded are measured and recorded the control signal described in the sequence design recorded in the design recording unit 112 and the operation time between sensor responses.

  In step 305, the operation state of the control sequence 104 is acquired by the process measurement unit 107 and the measurement result reception unit 108 and recorded in the operation time recording unit 114. At that time, the contents to be measured and recorded are measured and recorded for the processing time of the software processing block described in the sequence design recorded in the design recording unit 112.

  When maintenance work is started in the maintenance terminal 113 (step 306), in step 307, statistical information is calculated from each hardware component operation status. As the statistical information, at least an average value of the operation time and an index indicating a change in the operation time, for example, a correlation coefficient between the time and the operation time are calculated from the measured operation time of the hardware component. In addition, variance, operation frequency, and the like may be calculated as an index indicating variation in operation time.

  Next, in step 308, the processing performance after a certain period in the entire apparatus is predicted from the statistical information and the description contents of the sequence design.

  In step 309, the performance prediction result is presented to the output device 116.

  When device information relating to hardware part replacement is required (step 310), in step 312 the performance improvement effect due to hardware part replacement is predicted.

  In step 312, a hardware component having a high improvement effect is identified based on the improvement effect amount.

  In step 313, the specified parts to be replaced are presented through the output device 116.

  FIG. 4 is a flowchart showing details of step 308 and step 309 of FIG. 3, for predicting the performance of the maintenance target device by the processing performance prediction unit 110 and for notifying the user of the prediction result by the output device 116. 2 is a flowchart showing a performance prediction method.

  First, in step 401, information related to the operation time of each hardware component in the past certain period (hereinafter, operation time information) is acquired. The operation time information is accumulated in the operation time recording unit 114 from the measurement result in the measurement result receiving unit 108, and at least the elapsed time from the acquisition of the control signal of each hardware component to the sensor response output in the past fixed period, and the elapsed time The average value of time and the change information of the elapsed time in the past fixed period are included. As the change information, for example, the correlation coefficient of the elapsed time may be used, or the difference between the average value of the past fixed period and the average value of the previous period may be used. Any index may be used. In addition, it may include an index indicating variation such as variance of elapsed time in a certain past period and change tendency of variance.

  Next, in step 402, the operation time of each hardware component after a certain period is predicted from the operation time information. For prediction, for example, an increase / decrease in the average value after a certain period is calculated from change information included in the operation time information, and is added to the average value to obtain a predicted value. Further, when the operation time information includes an index indicating variation such as variance, a value obtained by adding a numerical value proportional to variance to the predicted value calculated from the change information and the average value in consideration of variation may be used as the predicted value. As a specific example of the prediction method, there is a method shown in (Expression 1).

T _i (t + Δt) = Ave _i (t−Δt, t) + ΔtD _i (t−Δt, t) + 3σ _i (t−Δt, t) (Equation 1)
Here, T _i (t + Δt) is the estimated elapsed time after Δt at time t of the hardware component i, and Ave _i (t−Δt, t) is the elapsed time from time t−Δt to t of the hardware component i. It is an average value of time, D _i (t−Δt, t) is a correlation coefficient of elapsed time from time t−Δt to t of hardware component i, and σ _i (t−Δt, t) is hardware This is the standard deviation of the elapsed time from the time t-Δt to t of the wear part i.

  Next, in step 403, time information of the software processing block (hereinafter referred to as software processing time information) is acquired. The software processing block is a processing unit described in the sequence design. For example, the software processing block corresponds to each step and transition in the case of SFC, and corresponds to each step in the case of a flowchart. Similar to the operation time of the hardware component, the processing time of the software processing block is measured by the measurement result receiving unit 108 and stored in the operation time recording unit 114. The software processing time information includes at least an average value of processing times in each software processing block. Similarly to the hardware operation time information, it may include an index related to a change or variation in the average value, or information related to the occurrence frequency of each software processing block.

  Next, in step 404, for each software processing block in the sequence design and the operation of the hardware component, the processing time average value of the software processing block and the predicted value of the hardware operation time are applied, and all the paths in the sequence design are applied. The time from the start of the process to the end of the process (hereinafter referred to as the performance prediction value) is calculated. At that time, the time during which the software processing and the operation of the hardware component are operating in parallel and the time during which the software is operating alone are calculated based on the description of the waiting process in the sequence design. For example, when the sequence design is described in SFC, transitions and joins correspond to waiting processing, and control signals corresponding to the corresponding hardware components are sent according to the sensor response conditions from the hardware components described in the transition. By identifying and applying the predicted value of the operation time, the parallel operation time of the hardware and software including the hardware wait and the processing time of the software alone are predicted.

  Finally, in step 405, the output device 116 is notified of the performance prediction result. The content to be notified includes the operation time from the start of processing of all paths to the completion of processing in the sequence design at least after a certain period, may include the operation time of a specific part included in the sequence design, or software processing time information If the information on the occurrence frequency is included, the occurrence frequency of each path may be calculated from the occurrence frequency information.

  FIG. 5 is a flowchart showing details of Step 311 and Step 312 of FIG. 3, and shows a method of specifying a part to be replaced for performance improvement by the replacement part specifying unit 111 and presenting it to the output device 116. is there.

  First, in step 501, new performance values of hardware components of the maintenance target device are acquired from the operation time recording unit 114. Here, the new performance indicates an operation time expected to be obtained when a hardware part is replaced with a new one.

  Next, in step 503, step 504, and step 505, the performance improvement effect when each hardware part is replaced with a new one is predicted.

  For all hardware parts of the maintenance target device (step 502), in step 503, the prediction result of the operation time on the sequence design predicted by the performance prediction method is acquired, and the operation time related to the hardware part is replaced with the new performance. .

  Next, in step 504, an operation time (hereinafter referred to as a performance improvement predicted value) in the entire design sequence by replacing the operation time with the new performance is calculated. The calculation method is the same as the step 403 in the performance prediction method, and recalculates the software waiting time and the hardware waiting time with only the hardware operating time of the corresponding hardware component as the new performance value.

  Next, in step 505, the performance improvement predicted value and the performance predicted value predicted by the performance prediction method are compared, and the extent to which the operation time is improved by replacing the hardware component is calculated. As a calculation method, an improvement amount is obtained by adding the difference between the performance improvement predicted value and the performance predicted value for all paths in the sequence design. Also, when the occurrence frequency of each path in the sequence design is recorded in the operation time recording unit 114, the difference between the performance improvement predicted value and the performance predicted value is weighted according to the frequency of occurrence of each path to generate more You may calculate by the method of estimating the improvement amount in a path | pass with high frequency highly.

  After predicting the improvement amount for all hardware parts, in step 506, hardware parts having a large improvement amount are extracted (hereinafter, the extracted hardware parts are referred to as replacement candidates). As an extraction method, one hardware component with the highest improvement amount may be selected, hardware components may be ranked in descending order of improvement amount, and a fixed number may be selected in descending order. The input of the improvement amount may be received and all the hardware components having the input improvement amount may be selected, and any method may be used as long as hardware components having a high improvement amount are preferentially extracted.

  Finally, in step 507, the output device 116 is notified of hardware component replacement information for performance improvement. The information to be notified includes at least the extracted replacement candidates, and may include a performance improvement predicted value corresponding to each replacement candidate.

  Note that the operation process shown in each flowchart of FIGS. 3 to 5 is a maintenance terminal when the sequence design receiving unit 109, the processing performance prediction unit 110, the replacement part specifying unit 111, and the measurement result receiving unit 108 are software. This is realized by the CPU provided in 113 executing these software stored in the storage unit. The same applies to the flowcharts shown in FIGS.

  Next, FIG. 7, FIG. 8, and FIG. 9 show details of the performance prediction method.

  FIG. 7 is a table showing an example of the operation time information of the hardware operation component acquired in step 401. While the maintenance target device in FIG. 2 is operated for a certain period by the sequence design in FIG. 4 is a table of hardware component operation time information created by the measurement result receiving unit 108 based on the measurement results in and accumulated in the operation time recording unit 114. FIG.

  In the hardware operation time information of FIG. 7, the control signal (control signal 702 sequence) described in the sequence design of FIG. 6 in the operation of the hardware component (hardware component 701 sequence) of the maintenance target device of FIG. The elapsed time between sensor responses (sensor response 703 rows) is measured, and based on this, the frequency of occurrence of each operation in the past fixed period (occurrence frequency 704 rows), the average value of the elapsed time (average value (msec) 705 rows) Then, a correlation coefficient (correlation coefficient 706 sequence) indicating a tendency of change in elapsed time due to deterioration and a variance (dispersion 707 sequence) indicating variation are calculated and stored.

  For example, in line 710, hardware operation time information related to the operation from receiving the operation start control signal at the conveyor A201 to returning the position end sensor response is stored, and 200 operations are performed within the measured period. The average time was 10 msec. In addition, there is stored operating time information that the elapsed time increases at a rate of about 0.5 msec per unit period due to deterioration, and that the variance varies from the average value by about 0.001.

  FIG. 8 is a table showing prediction results of the operation time of each hardware component calculated in step 402 based on FIG. In step 401 and step 402, the processing performance prediction unit 110 acquires the operation time information shown in FIG. 7 accumulated in the operation time recording unit 114, and calculates the average elapsed time after a certain period for the operation of each hardware component. The predicted average performance 804 column is predicted by adding the average value 705 up to the present and the average value change prediction after a certain period calculated from the correlation coefficient 706. The predicted worst performance 805 is calculated by calculating the variance of the performance due to variation from the predicted value and adding it to the predicted value. The predicted worst performance 805 column is calculated by the calculation formula shown in (Formula 1). For example, in line 810, the predicted performance related to the movement from receiving the operation start control signal in the conveyor A201 to returning the position end sensor response is calculated, and the average after a certain period is calculated from the operation time information in line 710. The time is predicted to increase by 0.5 msec to 10.5 msec (predicted averageness 804 sequence), and it is predicted to operate at 10.6 msec at worst (predicted worst performance 805 sequence) due to the variation of 0.1 msec at the maximum.

  Here, as the calculation method, prediction average performance calculation by linear prediction using average value and correlation coefficient and calculation of worst-case prediction performance by probability distribution model of normal distribution using variance (3σ interval) are used. It may be calculated by a method capable of nonlinear prediction, such as a neural network, from the individual measurement results accumulated in the recording unit 114. If the deterioration tendency of each hardware component is known, it is specific to each hardware component in advance. A performance prediction model may be prepared and calculated based on it.

FIG. 9 is a table showing an example of the processing time information of the software processing block acquired in step 403. While the maintenance target device in FIG. 2 is operated for a certain period by the sequence design in FIG. 10 is a table of processing time information of software processing blocks accumulated in the operation time recording unit 114 based on the measurement results of FIG. Here, the software processing block is a unit of processing described in the sequence design, and corresponds to each step, transition, and join in the example of the SFC shown in FIG. In FIG. 9, the occurrence frequency of processing (occurrence frequency 902 columns) and the average value of processing times (average value 903 columns) are calculated in the software processing block and stored in the operation time recording unit 114. Here, only the occurrence frequency and the average value are calculated, but if the software processing operation time is likely to vary depending on the operating environment, etc., the variance may be calculated, and the software processing time compared to the hardware operation time Is extremely short, the processing time of a block that does not accept a sensor response from a hardware component such as a step may not be calculated, and a sufficiently small constant value may be used.
Next, details of calculation of the processing time in the sequence design performed in step 404 of the processing performance prediction unit 110 will be described using the flowchart of FIG. 10, the diagrams of FIGS. 11, 12, and 13 and the table.

  FIG. 10 is a flowchart showing a processing time calculation method in sequence design, and is a flowchart showing details of step 404 in FIG.

For all software processing blocks included in the sequence design (step 1001), only the software operates in the following manner and the hardware component waits for a control signal (hereinafter referred to as software single processing time), and the software. Predict the time that the hardware is operating in parallel (hereinafter referred to as parallel processing time).
In step 1002, the operation time information of the hardware parts having input / output of the control signal and sensor response in the software processing block and the processing time information of the software processing block and the software processing blocks before and after the software processing block are acquired.

  Next, in step 1003, when the software processing block has a control signal output, a block waiting for a corresponding sensor response is extracted from the software processing blocks processed after the software processing block, and the corresponding hardware is extracted. The value obtained by subtracting the average processing time of all processing blocks on the path from the average operation time of the wear part to the block waiting for the sensor response is the parallel operation time, and the calculated parallel operation time is subtracted from the average processing time of the software processing block. This value is used as the soft unit processing time. Further, when the software processing block has a sensor response input, if the average operation time of the corresponding hardware component is longer than the average processing time of the software processing block, the entire processing time of the processing block is set as a parallel processing time. If the average operation time of the corresponding hardware component is shorter than the average processing time of the software processing block, the average operation time is set as the parallel operation time, and the difference between the average processing time and the average operation time is set as the software single processing time.

  FIG. 11 is an example in which the processing time is predicted as the software single processing time and the parallel processing time in the conveyor design operation start 601 step and 602 transition in the sequence design of FIG. 6 by the method of step 1003. From FIG. 9, the average processing time of the conveyor A operation start 601 step is 3 msec (904), and the average processing time of the 602 transition is 8 msec (905). From FIG. 7, the average operation time between the operation start and position end of the related conveyor A201 is 10 msec (710). In the conveyor A operation start 601 step having the control signal output, 2 msec obtained by subtracting the average processing time of the 602 transition for receiving the sensor response from the average operation time of the conveyor A201 is the parallel operation time, and the conveyor A operation start 601 step 1msec, which is the average processing time minus the parallel operation time, is the software single operation time. Since the 602 transition is a process of waiting for a sensor response, the entire 8 msec is the parallel operation time.

  Next, in step 1004, the predicted operation time of the hardware component is replaced with the average operation time, and the parallel operation time is recalculated. At that time, the software single operation time and the parallel operation time of the processing block that outputs the control signal are assumed not to fluctuate from the values calculated in step 1003, and are recalculated by changing the parallel operation time of the processing block waiting for the sensor response. . For example, when the average operation time between the operation start and position end of the conveyor A201 in FIG. 11 is replaced with the predicted value, the software single operation time 1113 and the parallel operation time 1112 of the conveyor A operation start 601 step remain as they are, and the 602 transition The parallel operation time 1110 is recalculated.

  FIG. 12 shows the calculation of the processing time prediction of each software processing block by calculating the software single operation time and the parallel operation time in step 1003 and step 1004 and replacing them with the predicted values. This is the result of the calculations shown for the start 601 step and the 602 transition performed for all hardware component operations. The total average value 1202 column is the average value of the processing time of each block as shown in FIG. 9, and the software unit 1203 and the parallel operation 1204 are the calculated software unit operation time and the parallel operation time. The prediction 1201 is a predicted value of the parallel operation time calculated from the hardware operation time predicted value, and the overall prediction 1206 is a predicted value of the processing time of the software processing block calculated from the predicted value of the parallel operation time.

  Next, in step 1005, the calculation results in all software processing blocks are added for each path to obtain a predicted processing time value in sequence design.

  13 is based on the sequence design of FIG. 6, the hardware operation time prediction of FIG. 8, and the processing time information of the software processing block of FIG. It is an example of an output result in which time is calculated and an operation time prediction is applied to the parallel operation time, and is an example of a result presented to the output device 116 in step 405. In FIG. 13, the software unit processing time and the parallel operation time at the location that is the bottleneck in the path in the sequence design, and the prediction performance of the entire sequence in each path are output. Here, a display program that provides a display method such as a display screen and a GUI is assumed as the output device 116, and a sequence design diagram and a prediction result are output. May be presented in a text format, or may be presented in a table format for each processing block as shown in FIG.

  By predicting the performance in sequence design, the user can estimate the processing performance of the entire maintenance target device after a certain period, and quantitatively determine the necessity of maintenance work such as replacement of hardware parts. Is possible.

  If the performance prediction method reveals that the performance of the device will be insufficient in the future and it is determined that hardware parts need to be replaced (step 310), the replacement part information presentation method for performance improvement shown in FIG. Thus, an exchange candidate product that is predicted to be most effective for performance improvement is presented.

  Next, details of a method for presenting replacement part information for performance improvement will be described with reference to the flowchart of FIG. 15, the diagrams of FIGS. 14 and 16, and tables.

  FIG. 14 is a table showing an example of new performance of hardware parts described in Step 302, Step 501, and Step 503. The new performance is described in the new performance 1404 column for each operation of each hardware component. New performance is the expected operating time after replacing each hardware component. For example, the operation time between the operation start and position end of the conveyor A shown in row 1405 is about 10 msec in FIG. 7 and FIG. 8, which is the average and predicted value of the measurement. It is shown that operation is expected.

  FIG. 15 is a flowchart showing a processing time calculation method using a new performance value, and is a flowchart showing details of the replacement method to the new performance value in step 503 of FIG.

  First, in step 1501, the software single operation time and parallel operation time (FIG. 12) and the operation time prediction value (FIG. 8) of the hardware component are acquired in the performance prediction method. Next, in step 1502, the predicted operation time of the hardware component is replaced with a new performance value, and in step 1503, the parallel operation time is calculated in the same manner as in step 1004 of the performance prediction method. However, it is calculated as a new product performance value instead of the hardware operating time prediction value.

  FIG. 16 shows the result of recalculating the parallel operation time in the sequence design of FIG. 6 based on the new performance value of FIG. 14 in step 1503 and summarizing all software processing blocks. Column 1605 after parallel replacement indicates the parallel operation time when hardware components related to each software processing block are replaced, and 1606 after total replacement is the processing time after hardware component replacement in each software processing block as a whole. . In line 1608, it is shown that the transition process, which conventionally has been 8 mec on average, operates in 1 msec by replacing the conveyor A201.

  FIG. 17 is a diagram illustrating a prediction result when the conveyor B206 is replaced as an example of calculation of the improvement amount by component replacement in step 505.

  When the conveyor B206 is replaced, the parallel operation time is shortened from 57.1 msec to 25 msec at 615 steps of the conveyor B operation start and the subsequent transition. However, in the entire sequence design, since the image processors operating in parallel require an operation time of 300 msec, there is no improvement effect at 615 steps of the conveyor B operation start.

  In addition, the parallel operation time is shortened from 13.2 msec to 3 msec by replacing 535 steps of conveyor B operation stop and subsequent transition. In the entire sequence design, an improvement effect of 10.2 msec is predicted for the path in which the sensor response to the arm operation start 622 has failed.

  FIG. 18 is a table showing the result of calculating the improvement amount by replacement for each hardware component, and is the result of the improvement amount calculation in step 505. Line 1801 is the calculation result for the path where the sensor response after 622 steps of arm movement start was completed (hereinafter referred to as path 1), and the processing of the entire sequence design when each hardware component was replaced It is a shortened time. Line 1802 is a calculation result in a path (hereinafter, referred to as path 2) in which the sensor response after the arm operation start 622 step has failed. Cells 1807, 1805, 1806, 1804, 1804, and 1803 are improvements when each hardware component is replaced, and the processing reduction time for each path is calculated by weighting the occurrence frequency for each path. For example, the cell 1804 is an improvement amount when the conveyor B206 is replaced, and the weighted average of the shortening time 0 in the pass 1 and the shortening time 10.2 in the pass 2 is weighted with a weight according to the frequency of occurrence of each pass ((0 × 190 + 7.6 × 10) / 200) Improvement amount is 0.51. Here, the shortening time is weighted according to the occurrence frequency. However, the shortening time of all the paths may be simply added to obtain an improvement amount, or when an important path in the sequence design is known, The improvement amount may be calculated with special emphasis on the shortening time.

  As shown in FIG. 18, in the maintenance target device shown in each drawing of the first embodiment of the present invention, the improvement effect by exchanging the conveyor A201 is the highest, and the conveyor A201 is presented to the user as an exchange candidate from the output device 116. .

  As described above, the performance prediction method in the embodiment of the present invention is based on the design information (sequence design) of the sequencer and control software that controls each hardware component constituting the industrial device. The operation time is measured, the operation time of the hardware component after a certain period is predicted, and the operation time of the corresponding hardware component in the sequence design is applied to the predicted value of the operation time in the entire processing sequence of the apparatus. Estimate processing time.

  The sequence design is based on the relationship between the software processing and the output of the control signal that instructs the operation to each hardware component controlled by the sequencer or control software, the input of the sensor signal that receives the status of the hardware component, and the waiting time. It is assumed that the process is described, for example, in a notation such as a state transition diagram or SFC. Here, the waiting process refers to a process for stopping (waiting for) a software process until a specific software process ends or a sensor signal is input.

  When the device is in operation, the current processing contents are sequentially tracked in the sequence design, the generation interval between the control signal and the sensor signal in each hardware component described in the sequence design, the processing start and processing end of each software processing And the interval between the start of wait processing and the wait release.

  The measured result is continuously recorded, and statistical information such as the frequency of occurrence of each path in the sequence design, the average value of each interval, and the change of the average value per unit period is calculated for every fixed period. (Of the measured intervals, the interval between the control signal and the sensor signal in the hardware component is the hardware operation time, and the interval between the start and end of the software processing is the software processing time).

  Further, the hardware operating time after an arbitrary fixed period is predicted from the statistical information of the hardware operating time in the past fixed period. As a prediction method, from the history of the average value included in the statistical information and the correlation coefficient of the measurement result, for example, predict how much hardware operation time will be increased due to deterioration of each hardware part after a certain period by linear approximation, for example To do.

  In addition, the hardware operation time is predicted for all the hardware operations described in the sequence design, and the predicted hardware operation time is applied to the operation location of the corresponding hardware part in the sequence design. Is applied with an appropriate value, for example, the average value of the recorded interval or the latest measured value, and the wait processing time is calculated according to the increase or decrease of the hardware operation time based on the contents of the sequence design. Thus, the processing time after a certain period in the entire sequence is predicted.

  Further, using the prediction result by the performance prediction method, the hardware parts are replaced to improve the processing performance of the apparatus. Specifically, from the predicted processing time of the sequence based on the predicted value of the hardware operating time, the operating time after replacement (new operating time) for any one hardware component is applied to the sequence design, and the entire sequence is The processing performance prediction is calculated, the processing performance prediction after replacement is performed for all hardware components, and hardware components that have a large effect of improving processing performance in the entire processing sequence by replacement are set as replacement candidate products, at least Notification of component replacement information of the device including the effect candidate product and the improvement effect.

  As a result, hardware components that are truly effective in improving processing performance by predicting the improvement in processing performance in the entire sequence and using it as a replacement standard, as well as the presence or absence of failures and performance degradation of individual hardware components. It becomes possible to exchange only.

101 ・ ・ ・ Operation terminal
102 ... Control controller
103 ・ ・ ・ Hardware parts
104 ... Control sequence
107 ・ ・ ・ Processing measurement unit
108 ・ ・ ・ Measurement result receiver
109 ・ ・ ・ Sequence design reception
110 ・ ・ ・ Processing performance prediction unit
111 ・ ・ ・ Replacement part identification part
112 ・ ・ ・ Design Record
113 ・ ・ ・ Maintenance terminal
114 ・ ・ ・ Operating time recording section
115 ・ ・ ・ Input device
116 ... Output device
101 ・ ・ ・ Operation terminal
102 ... Control controller
113 ・ ・ ・ Maintenance terminal
115 ・ ・ ・ Input device
116 ... Output device
201 ・ ・ ・ Conveyor A
202 ・ ・ ・ Transported object
203 ... Image sensor
204 ... Imaging stage motor
205 ・ ・ ・ Arm
206 ・ ・ ・ Conveyor B
214 ... Image processor

Claims (11)

A replacement part presentation method in which a terminal device connected to the apparatus presents a hardware part to be replaced in an apparatus including a plurality of hardware parts,
A sequence information acquisition step in which the terminal device acquires sequence information indicating a processing flow of control software for controlling the hardware components;
The terminal device acquires new performance information when the hardware component is new, and a new performance information acquisition step;
A future performance information prediction step in which the terminal device predicts future performance information of the hardware component; and
An improvement effect prediction step for predicting an improvement effect in the entire sequence when the hardware component is replaced based on the sequence information, the performance information at the time of the new product, and the future performance information.
A hardware component specifying step for specifying a hardware component to be replaced among the plurality of hardware components based on the predicted improvement effect;
And a hardware component presentation step of presenting the identified hardware component. The replacement part presentation method according to claim 1,
The hardware component performance information is an operation time of the hardware component in a processing block of a processing flow indicated in the sequence information. In the replacement part presentation method according to claim 1 or 2,
The performance information when the hardware part is new is information indicating the operation time when the hardware part is new in the processing block of the processing flow indicated in the sequence information,
The future performance information of the hardware component is information indicating the future operation time of the hardware component in the processing block of the processing flow indicated in the sequence information. In the replacement part presentation method according to any one of claims 1 to 3,
The future performance information of the hardware component predicts the future operation time of the hardware component based on a change in the operation time of the hardware component for a certain period in the past. The replacement part presentation method according to claim 4,
The future performance information of the hardware component is based on the change in the operation time of the hardware component in the past certain period and the variation in the operation time of the hardware component in the past certain period. A replacement part presentation method characterized by predicting time. In the replacement part presentation method according to any one of claims 1 to 5,
The method of presenting a replacement part, wherein the improvement effect is a reduction in operation time of the hardware part obtained by replacing the hardware part. In the replacement part presentation method according to any one of claims 1 to 6,
The terminal device predicts an improvement effect in the entire sequence when each of the plurality of hardware components is replaced, and specifies a hardware component that maximizes the improvement effect. . The replacement part presentation method according to claim 1,
The terminal device
In the improvement effect prediction step, when there is a first processing block and a second processing block that are executed in parallel with the processing flow indicated in the sequence information, the first processing block is controlled. Improvement effect in the entire sequence obtained when the first hardware component is replaced, and improvement effect in the entire sequence obtained when the second hardware component controlled by the second processing block is replaced And compare
In the hardware component specifying step, a hardware component having a large improvement effect when replaced is identified as a hardware component to be replaced, of the first hardware component and the second hardware component. Replacement part presentation method. The replacement part presentation method according to claim 8,
The method of presenting a replacement part, wherein the improvement effect is a reduction in operation time of the hardware part obtained by replacing the hardware part. The replacement part presentation method according to claim 9,
If the operation time of the first hardware component in the first processing block is longer than the operation time of the second hardware component in the second processing block, the first hardware component is A replacement part presentation method characterized by specifying as a hardware part to be replaced. A replacement part presentation apparatus that is connected to an apparatus including a plurality of hardware parts and presents hardware parts to be replaced in the apparatus,
A sequence information acquisition unit for acquiring sequence information indicating a processing flow of control software for controlling the hardware components from the apparatus;
A new-time performance information acquisition unit that acquires new-time performance information of the hardware parts;
A future performance information prediction unit for predicting future performance information of the hardware component;
Based on the sequence information, the new performance information and the future performance information, an improvement effect prediction unit that predicts an improvement effect in the entire sequence when the hardware parts are replaced;
A replacement part presentation device comprising: a hardware part specifying unit that specifies a hardware part to be replaced among the plurality of hardware parts based on the predicted improvement effect.

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