Performance diagnosis device, performance diagnosis method

Abstract

An object of the present application is to provide a performance diagnosing device capable of diagnosing performance reduction while taking into account the influence of the site characteristics and the load amount of the operation of a work machine. The performance diagnosing device of the present application diagnoses performance reduction of a work machine using a reference performance model generated for each combination of the type of operation performed by the work machine, the load amount, the content of operation, and the site characteristics (see FIG. 1).

Description

Technical Field

[0001] This invention relates to a performance measurement and diagnostic device for diagnosing the performance of machinery. Background Technology

[0002] As a method for diagnosing performance degradation of machinery due to years of deterioration, it is known that operators set the machinery to a specified state according to the process manual, perform prescribed actions, and compare the measured action time with a certain benchmark value to diagnose whether performance has degraded. However, this method compares a pre-set threshold with the action time and does not take into account the machinery's past operating history.

[0003] In contrast, Patent Document 1 discloses a method for generating data for normal driving using driving data accumulated from everyday vehicles such as cars driven in urban areas, including data on malfunctions and other incidents. This method sequentially accumulates and saves time-series data from a large number of vehicles, generating numerical vectors. These numerical vectors are then clustered into multiple clusters corresponding to specific features. Within each cluster, the range of frequently occurring values ​​for each driving parameter is calculated. This range of frequently occurring values ​​is saved as the range of normal values ​​for the driving parameters, and this range of normal values ​​is set as the baseline value for fault diagnosis.

[0004] The threshold used in Patent Document 1 to determine the range of frequently occurring values ​​is pre-set and stored in the diagnostic device, corresponding to the specific machine type. In other words, the technology described in this document makes it difficult to perform performance diagnostics that take into account individual machine differences.

[0005] To address the aforementioned issues, Patent Document 2 describes a technique that detects the state data of a vehicle, such as an automobile, traveling under pre-defined road conditions. After accumulating state data over a predetermined period or distance, a normal state model of the state data under the prescribed road conditions is created based on the vehicle's state data. After creating the normal state model, when traveling under pre-defined road conditions, malfunctions can be detected by comparing the acquired vehicle state data with the normal state model.

[0006] Existing technical documents

[0007] Patent documents

[0008] Patent Document 1: Japanese Patent No. 4414470

[0009] Patent Document 2: Japanese Patent No. 5018444 Summary of the Invention

[0010] The problem that the invention aims to solve

[0011] Besides operating in various environments and sites, construction machinery typically operates under varying loads even at the same site. While the technologies described in Patent Documents 1 and 2 can take into account individual differences in machinery, they do not consider the influence of site characteristics and load.

[0012] The present invention is made in view of the above circumstances, and aims to provide a performance diagnostic device that can diagnose performance degradation by taking into account the on-site characteristics of the operation of the machinery and the influence of the load.

[0013] Methods for solving problems

[0014] The performance diagnostic device of the present invention uses a benchmark performance model generated by combining the type of action performed by the machine, the load, the content of the action, and the on-site characteristics to diagnose the performance degradation of the machine.

[0015] Invention Effects

[0016] According to the performance diagnostic device of the present invention, it is possible to diagnose the performance degradation of operating machinery under various field conditions and loads with high precision. Attached Figure Description

[0017] Figure 1 This is a configuration diagram of the performance diagnostic device 1 in Embodiment 1.

[0018] Figure 2A This is a flowchart illustrating the steps of the performance diagnostic device 1 in diagnosing the performance of the operating machinery 100.

[0019] Figure 2B This is a flowchart illustrating the steps of the performance diagnostic device 1 in diagnosing the performance of the operating machinery 100.

[0020] Figure 3 This is a configuration diagram of the performance diagnostic device 1 in Embodiment 2.

[0021] Figure 4 This is a configuration diagram of the performance diagnostic device 1 in Embodiment 3.

[0022] Figure 5 This is a configuration diagram of the performance diagnostic device 1 in Embodiment 4.

[0023] Figure 6 This is a configuration diagram of the performance diagnostic device 1 in Embodiment 5. Detailed Implementation

[0024] <Implementation Method 1>

[0025] Figure 1This is a configuration diagram of the performance diagnostic device 1 according to Embodiment 1 of the present invention. The performance diagnostic device 1 is used to diagnose performance degradation of the working machinery 100 based on load information, operating information, and on-site characteristics. In this Embodiment 1, the performance diagnostic device 1 is disposed inside the working machinery 100.

[0026] The performance diagnostic device 1 includes a work action classification unit 11, a work data acquisition unit 12, a work time measurement unit 13, a work data storage unit 14, a benchmark performance model creation unit 15, and a performance diagnostic unit 16.

[0027] The operation classification unit 11 identifies and classifies the machine's operations. For example, it can identify and classify operations such as digging / dumping by processing images from an onboard camera installed on the excavator. Alternatively, it can acquire sensor data from the controller of the machine 100, which records the results of the physical state detected by the sensors of the machine 100, and use this sensor data to determine the operation performed by the machine 100 at that time. The sensor data can be acquired, for example, in the form of CAN (Control Area Network) data transmitted in the network within the machine 100. The operation classification unit 11 determines the type of operation through classification processing.

[0028] The work data acquisition unit 12 is electrically connected to the work motion classification unit 11. Corresponding to the motion classified by the work motion classification unit 11, it acquires work data necessary to determine the duration of the motion and outputs it to the work time measurement unit 13. The work data acquisition unit 12 can retrospectively acquire past work data representing, for example, the load information and motion content of the work machine 100. For example, by always acquiring and storing work data associated with a desired motion in a time series in advance, it is possible to acquire work data from any point in the past.

[0029] The following indicators can be listed as indicators of the load capacity of the working machine 100. For example, the pressure value of the hydraulic equipment that actuates the operating parts by hydraulic pressure represents the load applied to the operating parts, and this hydraulic pressure can be used as an indicator of the load capacity. In addition, any parameter representing the working load of the working machine 100 can be used as an indicator of the load capacity.

[0030] The following indicators can be listed as physical characteristics of the work site where the machinery 100 operates. For example, the three-dimensional shape of the work site represents the physical characteristics of that work site. Alternatively, in the case of machinery 100 performing excavation work, the soil type, the firmness of the foundation, and the type of environment in which the machinery operates (crushing site, dismantling site, etc.) also affect work efficiency. Therefore, geological characteristics and operating environment information are also used as physical characteristics. Furthermore, any parameter that affects the work efficiency of the machinery 100 can be used as a physical characteristic of the site. Methods for obtaining site characteristics are described later.

[0031] The operation time measurement unit 13 determines the start and end times of actions classified by the operation action classification unit 11 based on the operation data acquired by the operation data acquisition unit 12, and measures the duration of the actions. For example, in the case of an excavator, the start and end times of the digging action are identified by image processing based on images obtained from the captured action, thereby enabling the measurement of the duration of the digging action. The operation time measurement unit 13 is electrically connected to the operation data storage unit 14 and outputs the measured time to the operation data storage unit 14.

[0032] The operation classification unit 11 classifies the operation data acquired by the operation data acquisition unit 12 and the operation duration measured by the operation time measurement unit 13 into a specified period or a specified running time. The operation data accumulation unit 14 accumulates the combination of operation type / load / operation content / site characteristics obtained by classification into time series data.

[0033] The baseline performance model creation unit 15 creates a baseline performance model based on the data accumulated by the work data accumulation unit 14. The created baseline performance model is stored in a storage device, for example, the same as the work data, according to the combination of action type / load / action content / site characteristics classified by the work action classification unit 11.

[0034] The performance diagnostic unit 16 uses the operation data categorized by the operation action classification unit 11 to compare with the baseline performance model stored in the operation data storage unit 14 for the same operation conditions (the combination of action classification / load / action content / site characteristics is the same or the same) to diagnose whether the current performance of the operating machine 100 has decreased below the baseline value. The diagnostic results can be output as an alarm from the operating machine 100 or can be notified to relevant personnel via the network.

[0035] The performance diagnostic unit 16 further performs regression analysis on the past specified period of work data stored in the work data storage unit 14, and compares the results with the benchmark performance model. This allows it to predict whether the performance of the work machine 100 might decrease below the benchmark value after a specified period from now.

[0036] Figures 2A-2B This is a flowchart illustrating the steps of the performance diagnostic device 1 in diagnosing the performance of the operating machinery 100. For ease of description, one flowchart is divided into two diagrams, connected by the symbol "A" in the diagram. The following explanation... Figures 2A-2B Each step.

[0037] ( Figure 2A Step S201)

[0038] Next, the operation motion classification unit 11 selects the motion type (diagnostic target motion) of the work machine 100 to be diagnosed. At this time, based on the motion type, the basic motion content that is assumed for that motion is preset (for example, if it is a movement motion, the work machine 100 moves straight, etc.). The operation motion classification unit 11 saves the selected motion type and motion content as the assumed operation in advance.

[0039] ( Figure 2A Step S202)

[0040] The operation classification unit 11 determines the operation of the operating machine 100 by using image recognition from camera images and pattern matching of CAN data, thereby identifying the operation type. Examples of operation types include, for example, rotational operation, boom extension operation, digging operation, and movement operation.

[0041] ( Figure 2A Step S203)

[0042] The operation classification unit 11 investigates whether the operation identified in S202 is intended to be a diagnostic target operation set in S201 (i.e., whether it is the intended operation). If the identified operation is a diagnostic target operation, proceed to S204 (S203 Yes). Otherwise, return to step S202 (S203 No).

[0043] ( Figure 2A Step S204)

[0044] The work data acquisition unit 12 acquires work data of the actions set in S201 from the machine, for example, via CAN communication. In this step, work data can also be acquired and saved in real time, and the most recent work data that is sufficiently long in terms of the expected time of the action can be retrieved from the time the action ended.

[0045] ( Figure 2A (Supplement to step S204)

[0046] The work data acquired in this step includes, for example, data indicating the load of the action and data indicating the content of the action. Examples of load include, for instance, the discharge pressure of the hydraulic pump driving the work machinery. Examples of action content include, for example, the rotational speed of the wheels if the work machinery is moving. The work data acquisition unit 12 further receives the action type identified by the work action classification unit 11. Data describing this action type can also be processed as part of the work data.

[0047] ( Figure 2A (Supplement to step S204, part 2)

[0048] In this step, parameters representing the physical characteristics of the environment in which the work machine is placed can also be acquired. For example, the shape of the location where the work machine is placed and the road surface condition can be obtained from images taken of the area surrounding the work machine. Alternatively, in the case of excavation, the road surface hardness can be obtained based on the driving force (e.g., hydraulic pressure) required to drive the bucket. Alternatively, data describing these parameters can be acquired via a suitable interface. This physical characteristic data can also be processed as part of the work data.

[0049] ( Figure 2A Step S205)

[0050] The operation time measurement unit 13 calculates the operation duration (time from the start to the end of the operation) by detecting the start and end times of the operation based on the input operation data.

[0051] ( Figure 2A Step S206)

[0052] The work data storage unit 14 stores the work data obtained in S204 and the work duration calculated in S205 in the database according to the determined action.

[0053] ( Figure 2A Step S207)

[0054] Performance diagnostic device 1 checks whether data for a certain period or a certain running time has been accumulated. If no data has been accumulated (207 No), it returns to S204. If data accumulation has ended (S207 Yes), it proceeds to S208.

[0055] ( Figure 2B (Steps S208 to S210)

[0056] The baseline performance model creation unit 15 checks whether a baseline performance model for the action identified in S202 has been created (S208). If not created (S208 No), a baseline performance model is created for the identified action and saved in the database (S209). If created (S208 Yes), the performance diagnosis unit 16 uses the baseline performance model of the identified action to interpolate the baseline time in the acquired work data as needed (S210).

[0057] ( Figure 2B Step S209: Supplement)

[0058] The baseline performance model creation unit 15 creates a baseline performance model according to the following combination: (a) the type of operation of the machine; (b) the load of the operation of the machine; (c) the content of the operation of the machine; and (d) the physical characteristic parameters of the environment in which the machine is placed.

[0059] ( Figure 2B Step S210: Supplement)

[0060] Benchmark performance models sometimes describe the benchmark performance as discrete values. In this case, the values ​​between the discrete values ​​need to be compensated for, for example, by linear interpolation. This step is used to perform this interpolation process.

[0061] ( Figure 2B (Steps S211 to S213)

[0062] The performance diagnostic unit 16 confirms whether the motion time measured in S205 deviates from the reference value calculated in S210 by more than a threshold (S211). If the motion time deviates from the reference value by more than the threshold (S211 Yes), a diagnostic result of the degree of performance degradation of the work machine 100 is output according to the degree of deviation (step S212). If not (S211 No), a regression model is calculated between the identified motion and the data of a specified period in the acquired work data, and the regression model is used to predict the motion time after a specified period of the motion (S213).

[0063] ( Figure 2B Step S212: Supplement)

[0064] The performance diagnostic unit 16 diagnoses that the greater the difference between the measured operating time and the baseline performance, the greater the performance degradation. The performance diagnostic result can be represented by the difference itself, or by a parameter obtained by performing some calculation on the difference. The same applies in S215.

[0065] ( Figure 2B (Steps S214-S215)

[0066] The performance diagnostic unit 16 confirms whether the predicted operating time after the specified period deviates from the calculated baseline value by more than a threshold (S214). If the predicted operating time after the specified period deviates from the baseline value by more than the threshold (S214 Yes), a predicted diagnostic result of the degree of performance degradation of the operating machine 100 is output according to the degree of deviation (S215). Otherwise, this flowchart ends (S214 No).

[0067] <Summary of Implementation Method 1>

[0068] The performance diagnostic device 1 of this embodiment creates a baseline performance model based on a combination of (a) the type of operation of the machine; (b) the load of the machine's operation; (c) the content of the machine's operation; and (d) the physical characteristics of the environment in which the machine is placed. The device diagnoses whether the operation is normal by comparing the baseline performance obtained with the duration of the operation. Therefore, performance diagnostics can be performed considering the individual environment and load of the machine, thus improving diagnostic accuracy compared to conventional performance diagnostics.

[0069] In this embodiment, the performance diagnostic device 1 is installed inside the machine tool 100. That is, the machine tool 100 can diagnose its own performance. Thus, the machine tool 100 can automatically perform self-diagnosis without relying on user operation.

[0070] <Implementation Method 2>

[0071] Figure 3 This is a configuration diagram of the performance diagnostic device 1 according to Embodiment 2 of the present invention. The configuration of the performance diagnostic device 1 in this embodiment is the same as that in Embodiment 1, except that the performance diagnostic device 1 is configured inside the mobile terminal 110 (e.g., a smartphone).

[0072] The mobile terminal 110 can communicate with the machine 100 via the communication unit 112 to obtain CAN data (including sensor data such as sensor detection results) transmitted in the network inside the machine 100. The operation classification unit 11 can use the CAN data to determine the operation type of the machine 100 or to measure the duration of its operation.

[0073] Alternatively, by installing a mobile terminal 110 on the work machinery 100, and using sensors such as an accelerometer 111 built into the mobile terminal 110 to measure physical actions such as vibrations associated with the work machinery 100's movements, it is possible to determine the type of movement of the work machinery 100 and measure its duration. For example, the work movement classification unit 11 can pre-store vibration patterns associated with the movements of the work machinery 100 according to movement type, and match the vibration patterns measured by the sensors with the pre-stored vibration patterns, thereby identifying the movement type and movement duration.

[0074] The performance diagnostic device 1 of this embodiment allows operators to periodically collect data necessary for creating a baseline performance model using a mobile terminal 110. Furthermore, diagnostic results can be quickly notified to the operator via notification functions such as email provided by the mobile terminal 110, enabling timely repair and other countermeasures. Additionally, since the operator can always carry the mobile terminal 110, performance diagnostics can be performed on specific actions when needed, compared to Embodiment 1.

[0075] <Implementation Method 3>

[0076] Figure 4 This is a configuration diagram of the performance diagnostic device 1 according to Embodiment 3 of the present invention. In this embodiment, the performance diagnostic device 1 is configured inside the mobile terminal 110. The performance diagnostic device 1 includes a work action classification unit 11, a work data acquisition unit 12, a work time measurement unit 13, and a performance diagnostic unit 16. The work data storage unit 14 and the benchmark performance model creation unit 15 are disposed inside the work machine 100. Other configurations are the same as in Embodiment 1.

[0077] Similar to Embodiment 2, the communication unit 112 can acquire CAN data and other data from within the work machine 100 by communicating with it. Therefore, as in Embodiment 2, it can perform action detection and work duration measurement. The communication unit 112 further acquires reference performance by referring to a reference performance model maintained by the work machine 100, or acquires the reference performance model itself.

[0078] The machine 100 acquires work data (including action type, action content, and environmental characteristics) via the communication unit 112 and stores it in the work data storage unit 14. The benchmark performance model creation unit 15 uses the work data stored in the work data storage unit 14 to create a benchmark performance model.

[0079] According to the performance diagnostic device 1 of this embodiment, the same effects as in Embodiment 1 are achieved. Furthermore, since a baseline performance model is created and maintained within the machine 100, compared to the case where a baseline performance model is created / maintained by the mobile terminal 110, the same baseline performance model can be provided regardless of each mobile terminal 110. That is, in the case where a baseline performance model is created / maintained by the mobile terminal 110, each mobile terminal 110 maintains its own baseline performance model. In contrast, in this embodiment, the same baseline performance model can be provided for any mobile terminal 110.

[0080] <Implementation Method 4>

[0081] Figure 5 This is a configuration diagram of the performance diagnostic device 1 according to Embodiment 4 of the present invention. In this embodiment, the performance diagnostic device 1 is configured inside the external operating terminal 120. Other configurations are the same as in Embodiment 1. The external operating terminal 120 is a terminal that can be subsequently installed on the work machine 100, and the work machine 100 can be operated via the external operating terminal 120 after installation. The external operating terminal 120 includes sensors such as an acceleration sensor 121 and a communication unit 122.

[0082] The external operating terminal 120 communicates with the working machine 100 via the communication unit 122, thereby enabling it to acquire CAN data from the working machine 100 in real time. The performance diagnostic device 1 can use this CAN data to perform action identification and action time measurement. Alternatively, by matching the vibration pattern detected by the acceleration sensor 121 with a pre-held vibration pattern, it can identify the action type and action duration.

[0083] According to the performance diagnostic device 1 of this embodiment, the same effects as in embodiment 1 are achieved. Furthermore, by configuring the performance diagnostic device 1 within the external operating terminal 120, the performance diagnostic function provided by the performance diagnostic device 1 can be subsequently applied to existing work machinery 100 that does not possess a performance diagnostic function. Thus, even work machinery 100 that does not possess a self-diagnostic function can subsequently acquire this function.

[0084] <Implementation Method 5>

[0085] Figure 6This is a configuration diagram of the performance diagnostic device 1 according to Embodiment 5 of the present invention. In this embodiment, the performance diagnostic device 1 is configured as, for example, a server computer configured on a network. The performance diagnostic device 1 includes a job time measurement unit 13 (which also functions as a communication unit for acquiring job data), a job data accumulation unit 14, a benchmark performance model creation unit 15, and a performance diagnostic unit 16. The job action classification unit 11 and the job data acquisition unit 12 are configured inside the mobile terminal 110. Other configurations are the same as in Embodiment 1.

[0086] Similar to Embodiment 2, mobile terminal 110 acquires work data (including action type, action content, and environmental characteristics) via accelerometer 111 and communication unit 112, and transmits the work data to performance diagnostic device 1 via communication unit 112. Performance diagnostic device 1 receives the work data. The subsequent processing by work time measurement unit 13 is the same as in Embodiment 1.

[0087] The performance diagnostic device 1 according to this embodiment achieves the same effects as in Embodiment 1. Furthermore, by using, for example, a server computer with high computing performance to construct the performance diagnostic device 1, not only is processing faster, but the diagnostic results can also be quickly communicated to the operator.

[0088] <Modifications of the Invention>

[0089] This invention is not limited to the foregoing embodiments and includes various modifications. For example, the above embodiments are detailed descriptions provided for clarity and ease of understanding of the invention and are not intended to limit the invention to all described configurations. Furthermore, a portion of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of one embodiment. Additionally, additions, deletions, or substitutions of other configurations can be made to a portion of the configuration of each embodiment.

[0090] In the above embodiments, it was described that the work data acquisition unit 12 acquires work data (including action type, action content, and environmental characteristics), and the work time measurement unit 13 measures the work duration. This data is used by the performance diagnostic unit 16 when acquiring baseline performance; therefore, the performance diagnostic unit 16 acquires at least all of this data at that point in time. Therefore, incidentally, the performance diagnostic unit 16 functions as a data acquisition unit for acquiring this data.

[0091] In the above embodiments, the machine 100 can, for example, be a microcomputer consisting of a CPU (Central Processing Unit) for performing calculations, a ROM (Read Only Memory) storing the calculation program, and RAM (Random Access Memory) as a temporary storage device for storing the calculation process and temporary control variables. The microcomputer can acquire CAN data by executing the stored program.

[0092] In the above embodiments, the work action classification unit 11, work data acquisition unit 12, work time measurement unit 13, work data accumulation unit 14, benchmark performance model creation unit 15, and performance diagnosis unit 16 can be constructed by hardware such as circuit devices with these functions installed, or they can be constructed by software with these functions installed executed by a computing device.

[0093] Explanation of reference numerals in the attached figures

[0094] 1 Performance Diagnostic Device

[0095] 11. Work Action Classification Department

[0096] 12 Operations Data Acquisition Department

[0097] 13. Work Time Measurement Department

[0098] 14 Operational Data Accumulation Department

[0099] 15. Benchmark Performance Model Creation Department

[0100] 16 Performance Diagnostics Department

[0101] 100 Operating Machinery

[0102] 110 mobile terminal

[0103] 120 external operating terminal

Claims

1. A performance diagnostic device for diagnosing the performance of operating machinery. The performance diagnostic device is characterized in that it includes: The motion classification department determines the type of motion by classifying the motions performed by the operating machinery. The work data acquisition unit acquires work data representing the load amount of the action, the content of the action, and data describing the physical characteristics of the site where the work machinery is placed. The work time measurement unit measures the time from the start to the end of the action of the type obtained by the action classification unit, i.e., the duration, based on the work data obtained by the work data acquisition unit. as well as The performance diagnostics department diagnoses whether the performance of the machine has deteriorated by referring to a benchmark performance model that describes the benchmark performance of the machine. The baseline performance model describes the baseline performance according to a combination of the type, the load, the content, and the physical characteristics. The performance diagnostic unit obtains the baseline performance of the action under the given type, load, content, and physical characteristics by using the type, load, content, and physical characteristics and referring to the baseline performance model, and calculates a baseline value for the duration of the action of the given type as the baseline performance. The performance diagnostic unit compares the acquired baseline performance with the duration. If the duration of the action of the specified type deviates from the calculated baseline value by more than a threshold, the unit diagnoses the performance of the operating machinery as reduced and outputs the result.

2. The performance diagnostic device according to claim 1, characterized in that, The action classification section uses... Images obtained by capturing the aforementioned actions The document describes acceleration data, including the results of measuring the acceleration generated in the machine during the action described. The data describes sensor data obtained by sensors that detect the physical state of the working machinery associated with the action, and the results of measuring the physical state of the machinery. At least one of them classifies the action to determine the type.

3. The performance diagnostic device according to claim 1, characterized in that, The operation time measurement unit uses... Images obtained by capturing the aforementioned actions The document describes acceleration data, including the results of measuring the acceleration generated in the machine during the action described. The data describes sensor data obtained by sensors that detect the physical state of the working machinery associated with the action, and the results of measuring the physical state of the machinery. The duration is determined by at least one of the following methods to determine the start time and end time of the action.

4. The performance diagnostic device according to claim 1, characterized in that, The performance diagnostic device further includes: The data storage unit stores data containing the type, load, content, physical characteristics, and duration of the data; and The model creation unit uses the data stored in the data accumulation unit to create the benchmark performance model.

5. The performance diagnostic device according to claim 4, characterized in that, The model creation unit creates the baseline performance model based on a combination of the type described in the data, the load described in the data, the content described in the data, and the physical characteristics described in the data.

6. The performance diagnostic device according to claim 1, characterized in that, The performance diagnostic device further includes a data storage unit that stores data recording the type, the load, the content, the physical characteristics, and the duration. The performance diagnostics unit uses the history of the past data stored in the data storage unit to predict the duration after a specified time point. The performance diagnostic unit predicts whether the performance of the operating machinery will decrease after the specified time has elapsed, based on whether the difference between the predicted duration after the specified time has elapsed and the baseline performance described by the baseline performance model is above a threshold.

7. The performance diagnostic device according to claim 1, characterized in that, The performance diagnostic device is configured as a mobile terminal that can be detached from and attached to the operating machinery. The performance diagnostic device further includes an accelerometer that measures the acceleration applied to the mobile terminal. The action classification unit classifies the actions using the acceleration measured by the accelerometer. The operation time measurement unit determines the start time and end time of the action by using the acceleration measured by the acceleration sensor, thereby measuring the duration.

8. The performance diagnostic device according to claim 1, characterized in that, The performance diagnostic device is configured as a mobile terminal that can be detached from and attached to the operating machinery. The performance diagnostic device further includes a communication unit, which acquires sensor data from the operating machinery via communication. This data data records the results of sensors measuring the physical state of the operating machinery, which is generated in conjunction with the action. The action classification unit uses the sensor data acquired by the communication unit to classify the action. The operation time measurement unit determines the start time and end time of the action by using the sensor data acquired by the communication unit, thereby measuring the duration.

9. The performance diagnostic device according to claim 7, characterized in that, The operating machinery includes: The data storage unit stores data containing the type, load, content, physical characteristics, and duration of the data; and The model creation unit uses the data stored in the data accumulation unit to create the benchmark performance model. The performance diagnostic device further includes a communication unit that acquires the baseline performance model from the operating machinery. The performance diagnostic unit uses the benchmark performance model obtained from the machine by the communication unit to diagnose the performance of the machine.

10. The performance diagnostic device according to claim 1, characterized in that, The performance diagnostic device is configured as an external operating terminal, which is configured to be detached from the machine and allowed to be operated by the operator.

11. The performance diagnostic device according to claim 1, characterized in that, The performance diagnostic device further includes a communication unit connected to a terminal that collects data describing the type, load, content, physical characteristics, and duration, and obtains the data from the terminal. The performance diagnostic unit uses the data obtained from the terminal by the communication unit to diagnose the performance of the operating machinery.

12. The performance diagnostic device according to claim 1, characterized in that, The physical characteristics are parameters that affect the working efficiency of the machine when it performs the action.

13. The performance diagnostic device according to claim 12, characterized in that, The physical properties are determined by The geological characteristics of the site, The type of the site, The shape of the scene, At least one of the provisions in the law.

14. The performance diagnostic device according to claim 1, characterized in that, The load is determined by the discharge pressure of the hydraulic pump that drives the machine.

15. A performance diagnostic method for diagnosing the performance of operating machinery. The performance diagnosis method is characterized by having the following steps: The type of action is determined by classifying the actions performed by the operating machinery. Acquire operational data representing the load amount of the action, the content of the action, and data describing the physical characteristics of the site where the operating machinery is placed; Based on the acquired task data, determine the duration (from start to finish) of the classified actions of each type; and The performance of the machine is diagnosed by referring to a benchmark performance model that describes the baseline performance of the machine. The baseline performance model describes the baseline performance according to a combination of the type, the load, the content, and the physical characteristics. In the performance diagnosis step, a baseline performance of the action under the given type, load, content, and physical characteristics is obtained by using the type, load, content, and physical characteristics and referring to the baseline performance model. A baseline value for the duration of the action of that type is then calculated as the baseline performance. In the step of diagnosing the performance, the obtained benchmark performance is compared with the duration. If the duration of the action of the type deviates from the calculated benchmark value by more than a threshold, the performance of the working machine is diagnosed as reduced and the result is output.

Images