Fatigue diagnosis device

Abstract

To provide a fatigue diagnostic device capable of diagnosing fatigue of a diagnostic subject member with stable accuracy.SOLUTION: A fatigue diagnostic device 400 includes: an acceleration acquisition part 410 for acquiring an acceleration measurement value 11 measured by an acceleration sensor 300 attached to a diagnostic subject member 200; a stress estimating part 420 for calculating a stress estimation value 12 on at least one evaluated point that is part of the diagnostic subject member 200, based on a finite element model reproducing the diagnostic subject member 200 by the acceleration measurement value 11 and a finite element method; and a fatigue determination part 430 for determining degree of fatigue of the diagnostic subject member 200 based on the stress estimation value 12.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a fatigue diagnostic device. [Background technology]

[0002] A conventional vehicle fatigue damage diagnosis system includes a sensor and a vehicle ECU. The sensor is attached to an object to be diagnosed. The object to be diagnosed is a component attached to the vehicle. The vehicle ECU calculates the fatigue damage level of the object to be diagnosed based on the detection value obtained from the sensor (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2013-79920 Summary of the Invention [Problem to be solved by the invention]

[0004] In the conventional fatigue and damage diagnosis system described above, fatigue diagnosis is performed based on the fatigue and damage level at the position where the sensor is attached. Therefore, the sensor needs to be attached at an appropriate position on the subject to be diagnosed, i.e., at a position where fatigue diagnosis can be performed accurately. However, depending on the installation position and orientation of the subject to be diagnosed, it may not be possible to attach the sensor at an appropriate position, which may result in a decrease in the accuracy of the diagnosis.

[0005] The present disclosure has been made to solve the above-mentioned problems, and aims to provide a fatigue diagnosis device that can perform fatigue diagnosis of a diagnosis target component with stable accuracy. [Means for solving the problem]

[0006] The fatigue diagnosis device according to the present disclosure includes an acceleration acquisition unit that acquires acceleration measurement values ​​measured by an acceleration sensor attached to a diagnosis target component, a stress estimation unit that calculates a stress estimation value at at least one evaluation target point that is part of the diagnosis target component based on the acceleration measurement values ​​and a finite element model that reproduces the diagnosis target component using the finite element method, and a fatigue determination unit that determines the degree of fatigue of the diagnosis target component based on the stress estimation value. The stress estimation unit calculates stress estimates at two or more evaluation points, and the fatigue determination unit determines the degree of fatigue of the diagnosis target component based on the stress estimates at each evaluation point. . [Effects of the Invention]

[0007] According to the fatigue diagnostic device of the present disclosure, fatigue diagnosis of a diagnostic target member can be performed with stable accuracy. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a block diagram showing an overall configuration according to a first embodiment. [Figure 2] 3 is a flowchart showing the process of creating an analysis program by the fatigue diagnostic device of FIG. 1. [Figure 3] FIG. 10 is a diagram illustrating a method for discretizing a degenerate model of a continuous system. [Figure 4] 2 is a flowchart showing an acceleration measurement process performed by the acceleration sensor of FIG. 1. [Figure 5] 3 is a flowchart showing a stress estimation value calculation process performed by the fatigue diagnostic device of FIG. 1. [Figure 6] 3 is a flowchart showing a fatigue determination process performed by the fatigue diagnostic device of FIG. [Figure 7] 7 is a diagram illustrating the process of counting the number of stress occurrences in FIG. 6. FIG. [Figure 8] 7 is a diagram illustrating a process for calculating the cumulative damage level of the evaluation target point shown in FIG. 6. FIG. [Figure 9] 1 is a configuration diagram showing a first example of a processing circuit that realizes the fatigue diagnostic device according to the first embodiment. [Figure 10] FIG. 4 is a configuration diagram showing a second example of a processing circuit that realizes the fatigue diagnostic device according to the first embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments will be described with reference to the drawings.

[0010] Embodiment 1 FIG. 1 is a block diagram showing the overall configuration according to the first embodiment.

[0011] In Fig. 1, a fatigue diagnosis system 1000 is a system that performs fatigue diagnosis on a diagnosis target component 200 provided in a vehicle body 100. Here, the vehicle body 100 is, for example, the main body of a railway vehicle. The diagnosis target component 200 is, for example, an inverter device. The diagnosis target component 200 is provided below the vehicle body 100.

[0012] The fatigue diagnostic system 1000 includes an acceleration sensor 300 and a fatigue diagnostic device 400 .

[0013] The acceleration sensor 300 is attached to the end of the lower surface of the diagnosis target member 200. The acceleration sensor 300 includes a sensor body 310 and an amplifier 320.

[0014] The sensor body 310 detects vibrations at the location where it is attached and converts the detected vibrations into an electric charge signal.

[0015] The amplifier 320 amplifies the charge signal converted by the sensor body 310 and converts the amplified signal into acceleration.

[0016] The acceleration sensor 300 performs the above operation at regular intervals to measure acceleration values. The acceleration sensor 300 transmits data to the fatigue diagnosis device 400 for each detection cycle in which the acceleration value is measured. The transmitted data is referred to as acceleration measurement values ​​11. The acceleration sensor 300 sequentially transmits the acceleration measurement values ​​11 via communication means (not shown). Alternatively, the acceleration sensor 300 may accumulate acceleration measurement values ​​11 for a certain period in a storage device (not shown) and transmit the accumulated data set to the fatigue diagnosis device 400.

[0017] The fatigue diagnosis device 400 includes an acceleration acquisition unit 410 , a stress estimation unit 420 , a fatigue determination unit 430 , an alarm generation unit 440 , and an analysis program creation unit 450 .

[0018] The acceleration acquisition unit 410 acquires the acceleration measurement value 11 measured by the acceleration sensor 300 .

[0019] The stress estimation unit 420 calculates a stress estimation value at at least one evaluation point that is part of the diagnosis target component 200. The stress estimation unit 420 acquires the acceleration measurement value 11 from the acceleration acquisition unit 410. The stress estimation unit 420 acquires the analysis program 13 that has been created in advance. The stress estimation unit 420 then incorporates the acceleration measurement value 11 into the analysis program 13 and executes the analysis program 13. By executing this analysis program 13, a stress estimation value 12 at the evaluation point is calculated.

[0020] In the first embodiment, the stress estimation values ​​are calculated at evaluation points P1, P2, and P3 shown in Fig. 1. Here, the evaluation points P1 and P2 are located at the joint between the vehicle body 100 and the diagnosis target component 200. The evaluation point P3 is located at the center of the top surface of the diagnosis target component 200.

[0021] The fatigue determination unit 430 determines the degree of fatigue of the diagnosis target member 200 based on the stress estimation value 12 calculated by the stress estimation unit 420. If the degree of fatigue of the diagnosis target member 200 is equal to or greater than a threshold, the diagnosis target member 200 instructs the alarm unit 440 to issue an alarm.

[0022] Upon receiving the instruction to issue a warning, the warning unit 440 issues a warning such as a voice notification via a speaker or a warning display on a monitor. The warning unit 440 may be provided outside the fatigue diagnosis device 400.

[0023] The analysis program creation unit 450 creates the analysis program 13. The analysis program creation unit 450 acquires sensor position information, evaluation target point position information, and other necessary parameters set by the operator. The analysis program creation unit 450 creates the analysis program 13 based on the various set data. Here, the sensor position information is position information of the acceleration sensor 300 in the diagnosis target component 200. The evaluation target point position information is position information of the evaluation target points P1 to P3 in the diagnosis target component 200.

[0024] FIG. 2 is a flowchart showing the process of creating the analysis program 13 by the fatigue diagnostic device 400 of FIG.

[0025] In step S101, the analysis program creation unit 450 of the fatigue diagnosis device 400 constructs a finite element model that reproduces the diagnosis target component 200. The operator specifies the type of element, such as solid element, shell element, or beam element, and the analysis program creation unit 450 acquires the specified type of element. The operator also specifies the number of dimensions, such as a two-dimensional model or a three-dimensional model, and the analysis program creation unit 450 acquires the specified number of dimensions.

[0026] In addition to the type and number of dimensions of the above elements, the analysis program creation unit 450 also acquires the material properties of the diagnosis target component 200 specified by the operator. The material properties include the Young's modulus, Poisson's coefficient, density, etc. The material properties used may be either isotropic or anisotropic.

[0027] In step S102, the analysis program creation unit 450 performs eigenvalue analysis on the constructed finite element model. At this time, the analysis program creation unit 450 acquires boundary conditions specified by the operator. The boundary conditions are conditions under which the displacement of the diagnosis target component 200 at a fixed position is set to zero.

[0028] The analysis program creation unit 450 extracts natural frequencies, modal vectors, and modal stresses by performing eigenvalue analysis.

[0029] Natural frequency is a numerical value that represents the resonant frequency. When a force is applied to an object repeatedly at a certain period, for example, every 0.1 seconds, the object resonates and vibrates violently. The reciprocal of this period is the natural frequency.

[0030] The modal vector is the distribution of the movement of an object when it resonates at its natural frequency. The modal vector of the part that vibrates strongly due to resonance will have a high value.

[0031] Modal stress is the distribution of stress that occurs in an object when it resonates at its natural frequency. The modal stress in the part that supports the vibration of the component will be high.

[0032] In step S103, the analysis program creation unit 450 creates a degenerated model of a continuous system based on the extracted natural frequencies, mode vectors, and modal stresses. The analysis program creation unit 450 performs a simulation using the natural frequencies, mode vectors, and modal stresses for the finite element model prepared as described above.

[0033] The analysis program creation unit 450 acquires data for each of the first, second, ..., nth natural vibration modes by performing a simulation. The data for the natural vibration modes includes information on the natural frequencies, mode vectors, and mode stresses extracted in step S102.

[0034] Furthermore, when performing the simulation, the analysis program creation unit 450 acquires from the operator the damping ratio, the load input points, the number of acceleration sensors 300, sensor position information, the number of evaluation target points, and evaluation target point position information for each natural vibration mode. Here, the load input points are points where a load is applied. Specifically, the load input points are the connection parts between the vehicle main body 100 and the diagnosis target member 200, and are the evaluation target points P1 and P2 in FIG. 1.

[0035] Based on these data, the analysis program creation unit 450 creates a degenerated model of a continuous system. The following equation 1 expresses the degenerated model of a continuous system in state space representation.

[0036]

number

[0037] The state space expression of Equation 1 is made up of the state equation shown in the upper part and the observation equation shown in the lower part. Matrices A to D are matrices with fixed values ​​that are obtained by the processing of step S103.

[0038] The determinants of matrix A are the eigenvalues ​​and damping ratios. The determinants of matrix B are the modal displacements of the load input points. The determinants of matrix C are the eigenvalues, damping ratios, modal displacements of the response points, and modal stresses of the response points. The determinants of matrix D are the modal displacements of the load input points and modal displacements of the response points.

[0039] In Equation 1, the state quantities of displacement and stress in the real world are represented by {W, σ}. That is, the acceleration acquired by the acceleration sensor 300 is represented on the left side of the observation equation as the second derivative of the displacement W. Furthermore, the estimated value of the stress to be sought is represented on the left side of the observation equation as σ.

[0040] In Equation 1, as the number of evaluation points increases, the number of rows in matrices C and D increases. Also, as the number of acceleration sensors 300 increases, the number of rows in matrices C and D increases.

[0041] Returning to the explanation of the flowchart in Fig. 2, in step S104, the analysis program creation unit 450 discretizes the created continuous system degenerated model. The acceleration measurement values ​​11 are data that are discrete for each detection cycle of the acceleration sensor 300. For this reason, the analysis program creation unit 450 discretizes the continuous system degenerated model with respect to time.

[0042] The degenerated model of a continuous system is composed of two equations, a state equation and an observation equation, as shown in Equation 1. Of these two equations, only the state equation is a continuous system equation. Therefore, the analysis program creation unit 450 performs discretization on the state equation.

[0043] The following equation 2 expresses the degenerated model of the discretized system in state space representation.

[0044]

number

[0045] Figure 3 is a diagram illustrating a method for discretizing a degenerate model of a continuous system. To perform discretization, it is necessary to find matrices A2 and B2 in the above formula 2. Here, matrices A2 and B2 are found using the fourth-order Runge-Kutta method.

[0046] Formula F1 in Fig. 3 is a discretization formula of the fourth-order Runge-Kutta method. If it is assumed that the weight u is constant in this formula F1, the discrete matrix shown in formula F2 in Fig. 3 is calculated.

[0047] In formula F2, AdT is k 1a , BdT k 1b By making substitutions such as , , etc., matrix A2 and matrix B2 become as shown in formula F3, where I in formula F3 represents the identity matrix.

[0048] Returning to the explanation of the flowchart in Fig. 2, in step S105, analysis program creation unit 450 creates analysis program 13. Analysis program 13 is a program that combines a degenerated model after discretization, that is, a state space expression after discretization, with a Kalman filter.

[0049] The analysis program 13 created in this way is called by the stress estimation unit 420 during the stress estimation value calculation process described later.

[0050] FIG. 4 is a flowchart showing the acceleration measurement process performed by the acceleration sensor 300 of FIG.

[0051] In step S201, the acceleration sensor 300 starts measuring acceleration. For example, when a start operation by the worker is received or when a specified time arrives, the acceleration sensor 300 starts measuring acceleration.

[0052] In step S202, the sensor body 310 of the acceleration sensor 300 converts vibrations occurring at the position where it is attached into an electric charge signal.

[0053] In step S203, the amplifier 320 amplifies the signal from the sensor main body 310 and converts it into a voltage signal. The amplifier 320 converts the amplified voltage signal into an acceleration by coefficient calculation.

[0054] In step S204, the acceleration sensor 300 transmits the measured acceleration to the fatigue diagnostic device 400 as an acceleration measurement value 11. This acceleration measurement value 11 is acquired by the acceleration acquisition unit 410 of the fatigue diagnostic device 400.

[0055] In step S205, the acceleration sensor 300 determines whether to end the acceleration measurement. For example, the acceleration sensor 300 ends the acceleration measurement when it receives an end operation from the worker or when a specified time arrives. If the acceleration sensor 300 does not end the acceleration measurement, the process returns to step S202.

[0056] Fig. 5 is a flowchart showing the stress estimation value calculation process performed by the fatigue diagnosis device 400 of Fig. 1. Here, load control on the diagnosis target component 200 is not taken into consideration. That is, in the flowchart of Fig. 5, matrix D shown in Equation 2 is not used. Note that if the load applied to the diagnosis target component 200 is known, matrix D is also used.

[0057] The stress estimation unit 420 of the fatigue diagnosis device 400 calls the acceleration measurement value 11 and the analysis program 13 to perform stress estimation value calculation processing.

[0058] In step S301, the stress estimation unit 420 sets initial values ​​for the state estimate value X and the error covariance matrix P. Here, the state estimate value X means the above-mentioned state variable z.

[0059] Since there is no vibration in the initial state, the state estimate value X is 0. In addition, the error covariance matrix P is set to a unit matrix as the initial value I here.

[0060] In addition to the above initial values, constant parameters of the system noise Q and the observation noise R are also set. The system noise Q and the observation noise R are values ​​that are appropriately determined by the operator according to the noise level.

[0061] In step S302, the stress estimation unit 420 calculates the prior state estimate X using the state estimate X and the matrix A2. - Here, the prior state estimate X - is the estimate of the state variable z at time t based on the data available up to time t-1.

[0062] In step S303, the stress estimation unit 420 calculates a priori error covariance matrix P using the error covariance matrix P, the matrix A2, the matrix B2, the system noise Q, the matrix A2′, and the matrix B2′. - Here, matrix A2' is a matrix obtained by transposing matrix A2, and matrix B2' is a matrix obtained by transposing matrix B2.

[0063] In step S304, the stress estimation unit 420 calculates the prior error covariance matrix P - , matrix C, matrix C', and observation noise R are used to calculate the Kalman gain matrix G. Here, matrix C' is a matrix obtained by transposing matrix C.

[0064] In step S305, the stress estimation unit 420 calculates the Kalman gain matrix G and the prior state estimate X - , matrix C′ and acceleration measurement value 11. Stress estimation unit 420 updates the state estimation value X that has been used so far to the calculated one.

[0065] In step S306, the stress estimation unit 420 calculates an estimated value σ of stress at the evaluation point based on the updated state estimate value X. The stress estimation unit 420 substitutes the state estimate value X as a state variable z(t+Δt) into the left side of the state equation shown in Formula 2. If the load u(t) is not taken into consideration or is constant, the stress estimation unit 420 can obtain the state variable z(t) from the state equation of Formula 2 one cycle ago.

[0066] Then, the stress estimation unit 420 substitutes the state variable z(t) into the observation equation of Expression 2. If the load u(t) is not taken into consideration or is constant, the stress estimation unit 420 can obtain the estimated value σ of the stress at the evaluation point. Here, the estimated values ​​σ of the stress at the evaluation points P1 to P3 are obtained simultaneously.

[0067] The stress estimating unit 420 also writes the obtained stress estimate σ to the data that has been saved up until now, thus generating the stress estimate 12.

[0068] In step S307, the stress estimation unit 420 calculates the initial value I of the error covariance matrix, the Kalman gain matrix G, the matrix C′, and the prior error covariance matrix P - The stress estimating section 420 updates the error covariance matrix P that has been used so far to the calculated one.

[0069] In step S308, the stress estimation unit 420 determines whether to acquire a new acceleration measurement value 11 within a specified time. If the stress estimation unit 420 acquires the acceleration measurement value 11 within the specified time, the process returns to step S302. Then, the stress estimation unit 420 performs the processes from step S302 onwards on the newly acquired acceleration measurement value 11. If the stress estimation unit 420 does not acquire the acceleration measurement value 11 within the specified time, the process ends.

[0070] Fig. 6 is a flowchart showing the fatigue determination process performed by the fatigue diagnosis device of Fig. 1. The flowchart of Fig. 6 is performed for each of the evaluation points P1 to P3, but here, as an example, the process for the evaluation point P1 will be described.

[0071] In step S401, the fatigue determination unit 430 counts the number of occurrences of stress occurring at the evaluation point P1 for each stress estimation value.

[0072] FIG. 7 is a diagram illustrating the process of counting the number of stress occurrences in FIG. 6. The graph shown in FIG. 6 shows the waveform of the stress estimation value 12 at the evaluation point P1, with the horizontal axis representing time and the vertical axis representing the stress estimation value. The fatigue determination unit 430 applies the rainflow method to the waveform of this stress estimation value. As a result, the fatigue determination unit 430 counts the number of occurrences of stress occurring at the evaluation point P1 for each stress estimation value. Here, the stress estimation values ​​σ1, σ2, σ3, . . . , σ n For each value of M1, M2, M3, . . ., M n is required.

[0073] In step S402, the fatigue determination unit 430 calculates the cumulative damage level d of the evaluation point P1.

[0074] 8 is a diagram illustrating the process of calculating the cumulative damage level d of the evaluation point in FIG. 6. The fatigue determination unit 430 uses the SN curve of the diagnosis target component 200 when calculating the cumulative damage level d. As a result, the estimated values ​​σ1, σ2, σ3, . . . , σ nThe number of repetitions until component failure, N1, N2, N3,..., N n is required. The fatigue determining unit 430 calculates the cumulative damage level d at the evaluation point P1 using the following formula 3.

[0075]

number

[0076] In step S403, the fatigue determination unit 430 determines whether the cumulative damage level d is equal to or greater than a threshold value, which may be, for example, 0.3.

[0077] If the cumulative damage level d is equal to or greater than the threshold, in step S404, the fatigue determination unit 430 instructs the alarm issuing unit 440 to issue an alarm. Upon receiving the alarm issuing instruction, the alarm issuing unit 440 issues an audio alert via a speaker, displays a warning on a monitor, or the like.

[0078] If the cumulative damage level d is not equal to or greater than the threshold value, the fatigue determining unit 430 ends the process.

[0079] The stress estimation unit 420 of the first embodiment calculates stress estimation values ​​at evaluation target points P1 to P3 that are part of the diagnosis target component 200. The stress estimation values ​​are calculated based on acceleration measurement values ​​and a finite element model that reproduces the diagnosis target component using the finite element method.

[0080] The fatigue diagnosis device 400 performs fatigue diagnosis using a finite element model, thereby enabling fatigue diagnosis at positions where no acceleration sensor 300 is attached. Therefore, the fatigue diagnosis device 400 can stably and accurately diagnose fatigue of the diagnosis target member 200 even when the acceleration sensor 300 is not attached at an appropriate position.

[0081] Furthermore, the stress estimation unit 420 calculates stress estimates at two or more evaluation points. The fatigue determination unit 430 determines the degree of fatigue of the diagnosis target component 200 based on the stress estimates at each evaluation point. Therefore, even if measurement values ​​at multiple positions are required, there is no need to prepare multiple sensors.

[0082] The stress estimation unit 420 calculates a stress estimation value based on a state equation and an observation equation. This observation equation includes at least an acceleration element and a stress element at the evaluation point. Therefore, the stress estimation unit 420 can perform calculation processing that takes into account the acceleration in the real world and the stress at the evaluation point in the real world.

[0083] Furthermore, the stress estimation unit 420 calculates stress estimates by executing a pre-created analysis program, which eliminates the need to create an analysis program each time a deterioration diagnosis is performed, allowing for smoother diagnosis work.

[0084] Furthermore, the analysis program creation unit 450 creates an analysis program based on the sensor position information and the evaluation target point position information, thereby making it possible to create an analysis program that takes into account the positions of the acceleration sensor 300 and the positions of the evaluation target points P1 to P3.

[0085] The fatigue determination unit 430 calculates the cumulative damage level d, which is an index value indicating the degree of fatigue, based on the stress estimation value. If the cumulative damage level d is equal to or greater than a threshold value, the fatigue determination unit 430 instructs the alarm unit to issue an alarm. Therefore, by checking whether an alarm has been issued, the worker can determine whether the diagnosis target component 200 needs to be replaced.

[0086] Furthermore, strain gauges are generally used as data detection means for fatigue diagnosis. In contrast, in the first embodiment, acceleration sensor 300 is used. Therefore, the acceleration sensor 300 does not require the same level of precision when being installed as a strain gauge, making installation easier. Furthermore, acceleration sensors output relatively large voltage signals. Therefore, they are more resistant to external noise than strain gauges.

[0087] In the first embodiment, fatigue diagnosis is performed on a diagnosis target component 200 provided on a vehicle body 100 of a railway vehicle. However, the diagnosis target component is not limited to this, and components provided on a moving body other than a railway vehicle may also be the diagnosis target component. Furthermore, the diagnosis target component is not limited to components provided on a moving body, and may be components provided on devices, equipment, and structures. For example, a communication antenna may be the diagnosis target component.

[0088] In the first embodiment, the degenerated model is constructed by the mode degeneration method. However, the degenerated model may be constructed by another method as long as it is possible to determine the deterioration.

[0089] Furthermore, in the first embodiment, the discretization formula based on the Runge-Kutta method is used, but other discretization formulas such as the Euler method or the Hoyn method may also be used.

[0090] Furthermore, a linear Kalman filter is incorporated into the analysis program 13 of the first embodiment. However, from the viewpoint of versatility or improved accuracy, a non-linear Kalman filter may be incorporated.

[0091] Furthermore, when constructing a finite element model, there are no restrictions on elements such as solid elements, shell elements, beam elements, etc., as long as they can reproduce the vibration state of the target. There are also no restrictions on the dimensions of the model, such as 3D models and 2D models.

[0092] There is no limit to the number of eigenvalue analyses for constructing a degenerated model, and there is no limit to the positions and numbers of the load input points, acceleration sensors 300, and evaluation points.

[0093] Furthermore, there is no restriction on the type of acceleration sensor 300, such as whether it is a single-axis sensor or a multi-axis sensor. As long as it can measure acceleration in the required direction, there is no restriction on the type.

[0094] The acceleration sensor 300 may be attached to the diagnosis target member 200 at any position where the diagnosis target member 200 vibrates greatly.

[0095] Furthermore, in the first embodiment, the rainflow method is used to determine deterioration, but the present invention is not limited to this. For example, a cycle counting method such as a peak counting method or a range counting method may be used. Alternatively, a fatigue limit diagram such as the modified Goodman rule, the Gerber rule, or the Soderberg diagram may be used. Alternatively, a damage assessment formula such as the modified Miner rule, the Miner rule, or the Heibach rule may be used. Alternatively, instead of these theoretical approaches, an assessment using AI may be performed.

[0096] The fatigue diagnosis device 400 sequentially acquires the acceleration measurement values ​​11. Alternatively, for example, the acceleration measurement values ​​11 may be accumulated over a certain period and processed collectively. For example, the acceleration may be sent to an external device using IoT technology, and fatigue diagnosis processing may be performed externally.

[0097] Each function of the fatigue diagnosis device 400 of the first embodiment is realized by a processing circuit. Fig. 9 is a configuration diagram showing a first example of the processing circuit that realizes each function of the fatigue diagnosis device 400 of the first embodiment. The processing circuit 50 of the first example is dedicated hardware.

[0098] The processing circuit 50 may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a combination thereof. Each function of the fatigue diagnosis device 400 may be realized by a separate processing circuit 50. Alternatively, all functions of the fatigue diagnosis device 400 may be realized collectively by a processing circuit 50.

[0099] 10 is a diagram showing a second example of a processing circuit that realizes each function of the fatigue diagnostic device 400 according to the embodiment 1. The processing circuit 60 of the second example includes a processor 61 and a memory 62.

[0100] In the processing circuit 60, each function of the fatigue diagnosis device 400 is realized by software, firmware, or a combination of software and firmware. The software and firmware are written as programs. The software and firmware are stored in the memory 62. The processor 61 realizes the function of each part by reading and executing the programs stored in the memory 62.

[0101] It can be said that the programs stored in the memory 62 cause the computer to execute the procedures or methods of the above-mentioned sections. Here, the memory 62 corresponds to, for example, non-volatile or volatile semiconductor memory such as RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electrically Erasable and Programmable Read Only Memory), etc. In addition, magnetic disks, flexible disks, optical disks, compact disks, minidisks, DVDs, etc. also correspond to the memory 62.

[0102] The functions of the above-described units may be partially realized by dedicated hardware and partially realized by software or firmware.

[0103] In this way, the processing circuit can realize the functions of each of the above-mentioned units by hardware, software, firmware, or a combination of these. [Explanation of symbols]

[0104] 11 acceleration measurement value, 12 stress estimation value, 13 analysis program, 200 diagnosis target component, 300 acceleration sensor, 400 fatigue diagnosis device, 410 acceleration acquisition unit, 420 stress estimation unit, 430 fatigue determination unit, 440 alarm unit, 450 analysis program creation unit.

Claims

1. an acceleration acquisition unit that acquires acceleration measurement values ​​measured by an acceleration sensor attached to the diagnosis target component; a stress estimating unit that calculates a stress estimated value at at least one evaluation target point that is a part of the diagnosis target component based on the acceleration measurement value and a finite element model that reproduces the diagnosis target component by a finite element method; and a fatigue determination unit that determines the degree of fatigue of the diagnosis target member based on the stress estimated value; Equipped with the stress estimation unit calculates the stress estimation values ​​at two or more of the evaluation target points; The fatigue determination unit determines the degree of fatigue of the diagnosis target component based on the stress estimated value at each of the evaluation target points.

2. the stress estimator calculates the stress estimated value based on a state equation and an observation equation, each of which is derived based on the finite element model; 2. The fatigue diagnostic device according to claim 1, wherein the observation equation includes at least an acceleration element and a stress element at the evaluation point.

3. the stress estimation unit calculates the stress estimated value by executing an analysis program; 3. The fatigue diagnosis device according to claim 2, wherein the analysis program is a program created in advance based on a combination of a state space representation formed by the state equation and the observation equation and a Kalman filter.

4. further comprising an analysis program creation unit that creates the analysis program; The analysis program creation unit acquiring sensor position information, which is position information of the acceleration sensor in the diagnosis target component, and evaluation target point position information, which is position information of the evaluation target point in the diagnosis target component; 4. The fatigue diagnosis device according to claim 3, wherein the analysis program is created based on the sensor position information and the evaluation point position information.

5. 5. A fatigue diagnosis device as claimed in claim 1, wherein the fatigue determination unit calculates an index value indicating the degree of fatigue of the diagnosed component based on the stress estimation value, and if the index value is greater than or equal to a threshold value, issues an alarm instruction to an alarm issuing unit.

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