Condition monitoring device and condition monitoring method

Abstract

To provide a state monitoring device and a state monitoring method capable of monitoring a state of a large number of object items without complicating configuration and processing.SOLUTION: A state monitoring device 1 includes: a vibration sensor 2 provided on a railway vehicle 11; a determination unit 3 for determining presence / absence of abnormality for each object item of state monitoring based on a detection signal from the vibration sensor 2; and a storage unit 4 for storing a reference table 5 in which a calculation interval is associated with a detection signal for each object item. The determination unit 3 calculates an interval sum of squares of an output value of a detection signal based on the calculation interval for each object item to determine the presence / absence of abnormality for each object item by comparing the interval sum of squares with a preset threshold value.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a condition monitoring device and a condition monitoring method. [Background technology]

[0002] In recent years, the maintenance system for railway vehicles has been shifting from time-based maintenance to condition-based maintenance. The change in the maintenance system requires the introduction of condition monitoring technology, and various research and development efforts are underway. A conventional condition monitoring device is the condition diagnosis device described in Patent Document 1. This conventional condition diagnosis device is configured to diagnose the condition of traction equipment mounted on a railway vehicle. The device includes: an aggregation means for aggregating the analysis results, obtained by octave-band analysis of vibration data detected by a vibration sensor for detecting vibrations in the traction equipment mounted on the railway vehicle, by operating mode and running speed of the railway vehicle, or by running speed; and a discrimination means for determining whether the aggregation results of the aggregation means conform to the aggregation result reference data for each of N (N≧1) types of known states of the traction equipment. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2017-77055 Summary of the Invention [Problem to be solved by the invention]

[0004] In conventional methods of monitoring the condition of an object based on the results of detecting vibrations, it is necessary to use complex algorithms to capture the vibration frequency characteristics and vibration changes for each item being monitored. When there are many items being monitored, installing vibration sensors and adding algorithms for each item can lead to complex configurations and processing. When the configuration and processing become more complex, the operation and management of the device itself becomes cumbersome, and labor-saving maintenance becomes a new challenge.

[0005] The present disclosure has been made to solve the above-mentioned problems, and aims to provide a status monitoring device and a status monitoring method that can monitor the status of a large number of target items without complicating the configuration and processing. [Means for solving the problem]

[0006] A condition monitoring device according to one aspect of the present disclosure comprises a vibration sensor provided on a railway vehicle, a judgment unit that judges whether or not there is an abnormality for each target item of condition monitoring based on a detection signal from the vibration sensor, and a memory unit that stores a reference table that associates a calculation interval for the detection signal with each target item, and the judgment unit calculates the interval sum of squares of the output value of the detection signal based on the calculation interval for each target item, and judges whether or not there is an abnormality for each target item by comparing the interval sum of squares with a predetermined threshold value.

[0007] This condition monitoring device calculates the interval sum of squares for the output value of the detection signal from the vibration sensor installed on the railway vehicle, and compares the interval sum of squares with a threshold to determine whether or not an abnormality exists for each target item. This method allows multiple feature quantities corresponding to each target item to be extracted from the same detection signal by adjusting the calculation interval for calculating the sum of squares. Therefore, this condition monitoring device does not require the installation of a vibration sensor for each target item or the addition of a complex algorithm, and can monitor the status of multiple target items without complicating the configuration or processing.

[0008] The reference table may include target items associated with a first calculation interval that includes the entire interval of the detection signal, and target items associated with a second calculation interval that is shorter than the first calculation interval. Target items for condition monitoring in railway systems may include, for example, vehicle running systems such as wheels and dampers, and infrastructure systems such as rails and bridges. These target items may be classified into items that should be monitored for long-term abnormalities and items that should be monitored for short-term abnormalities. Therefore, by associating the calculation interval for calculating the sum of squares with both short-term and long-term abnormality monitoring, condition monitoring that is more in line with the actual state of the railway system can be performed.

[0009] In the lookup table, the target items may be further associated with vibration direction, frequency band, and sampling frequency, thereby enabling more accurate extraction of multiple feature amounts corresponding to the target items from the same detection signal.

[0010] The vibration sensor may be a three-axis acceleration sensor, which further reduces the number of vibration sensors that need to be installed.

[0011] The vibration sensors may be provided at each of the front and rear ends of the railway vehicle or on each of the front and rear bogies. By providing the vibration sensors at these locations on the railway vehicle, vibrations related to both the running system and the track system of the railway system can be suitably acquired. Furthermore, by extracting feature amounts from the detection signals from the vibration sensors at different locations on the front and rear of the railway vehicle, the accuracy of determining whether or not an abnormality exists can be improved.

[0012] A condition monitoring method according to one aspect of the present disclosure includes a judgment step of judging whether or not there is an abnormality for each target item of condition monitoring based on a detection signal from a vibration sensor. In the judgment step, a reference table that associates a calculation interval for the detection signal with each target item is referenced, an interval sum of squares of the output value of the detection signal is calculated based on the calculation interval for each target item, and the presence or absence of an abnormality for each target item is judged by comparing the interval sum of squares with a predetermined threshold value.

[0013] This condition monitoring method calculates the interval sum of squares for the output value of a detection signal from a vibration sensor installed on a railway vehicle, and compares the interval sum of squares with a threshold to determine whether or not an abnormality exists for each target item. This method allows multiple feature quantities corresponding to each target item to be extracted from the same detection signal by adjusting the calculation interval for calculating the sum of squares. Therefore, this condition monitoring method does not require the installation of a vibration sensor or the addition of an algorithm for each target item, and can monitor the status of multiple target items without complicating the configuration or processing. [Effects of the Invention]

[0014] According to the present disclosure, the status of a large number of target items can be monitored without complicating the configuration and processing. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a schematic configuration diagram illustrating an embodiment of a condition monitoring device according to the present disclosure. [Figure 2] FIG. 10 is a diagram showing an example of a reference table relating to target items associated with a first calculation interval. [Figure 3] FIG. 10 is a diagram showing an example of a reference table relating to target items associated with a second calculation interval. [Figure 4] 2 is a flowchart showing an example of the operation of the state monitoring device shown in FIG. [Figure 5] FIG. 2 is a perspective view showing the configuration of a simulated vehicle and a simulated rail used in the examples. [Figure 6] 10 is a diagram showing an example of a detection signal from a vibration sensor when a derailment occurs in a wheel on the rear side in the traveling direction of the simulated vehicle. FIG. [Figure 7] 7 is a diagram showing the section sum of squares of the detection signals shown in FIG. 6. FIG. [Figure 8] FIG. 10 is a diagram showing an example of a detection signal from a vibration sensor when a tread flat occurs on a wheel on the rear side in the traveling direction of a simulated vehicle. [Figure 9] 9 is a diagram showing the section sum of squares of the detection signals shown in FIG. 8. FIG. [Figure 10] 10 is a diagram showing an example of a detection signal from a vibration sensor when a wheel on the rear side in the traveling direction of the simulated vehicle is causing a snake motion. FIG. [Figure 11] 11 is a diagram showing the section sum of squares of the detection signals shown in FIG. 10. FIG. DETAILED DESCRIPTION OF THE INVENTION

[0016] Hereinafter, preferred embodiments of a condition monitoring device and a condition monitoring method according to one aspect of the present disclosure will be described in detail with reference to the drawings.

[0017] Fig. 1 is a schematic diagram showing the configuration of one embodiment of a condition monitoring device according to the present disclosure. This condition monitoring device 1 is configured as a device that monitors the status of a railway vehicle system, and performs condition monitoring of, for example, the running system of the vehicle, such as wheels and dampers, and the infrastructure system, such as rails and bridges. As shown in Fig. 1, the condition monitoring device 1 includes a vibration sensor 2, a determination unit 3, and a memory unit 4.

[0018] The vibration sensor 2 is, for example, a three-axis acceleration sensor that detects acceleration in the X, Y, and Z directions of an object. The vibration sensor 2 is provided on the railway vehicle 11. The vibration sensor 2 may be provided on a bogie of the railway vehicle 11. In this embodiment, the vibration sensor 2 is provided on each of the front and rear bogies 12, 12 of the railway vehicle 11. In this case, each of the vibration sensors 2 may be located in an area directly above the center plate of the bogie when viewed from the height direction of the railway vehicle 11. The vibration sensor 2 detects vibrations (acceleration) applied to the bogie 12 of the railway vehicle 11, and sequentially outputs detection signals to the determination unit 3.

[0019] The determination unit 3 is a part that determines whether or not there is an abnormality for each item subject to condition monitoring based on the detection signal from the vibration sensor 2. Physically, the determination unit 3 is a computer system that includes memories such as RAM and ROM, a processor (arithmetic circuit) such as a CPU, a communication interface, and a storage unit such as a hard disk. Examples of such computer systems include personal computers, cloud servers, and smart devices (smartphones, tablet terminals, etc.). The computer system functions as the determination unit 3 by executing a program stored in the memory with the CPU. The determination unit 3 is located, for example, in a maintenance center for the railway vehicle system, and is capable of receiving the detection signal from the vibration sensor 2 via the network N.

[0020] When the determination unit 3 receives a detection signal from the vibration sensor 2, it calculates the interval sum of squares of the output value of the detection signal based on the calculation interval for each target item of condition monitoring. Then, it determines whether or not there is an abnormality for each target item by comparing the interval sum of squares with a preset threshold. The determination unit 3 determines whether or not there is an abnormality for each target item, for example, based on whether or not the interval sum of squares calculated using a predetermined calculation interval exceeds the threshold. If the determination unit 3 determines that there is an abnormality in the target item, it transmits a signal indicating that there is an abnormality in the target item to, for example, a running control device of the railway vehicle 11 or a control device at a maintenance center of the railway system.

[0021] The interval sum of squares is a value obtained by summing up the squares of data for a certain interval including the data. When calculating the interval sum of squares, the determination unit 3 refers to a lookup table 5 stored in the memory unit 4. The lookup table 5 associates a calculation interval for the detection signal with each target item of status monitoring. In this embodiment, the vibration sensor 2 repeatedly outputs a detection signal with one detection cycle lasting 180 seconds. As shown in FIG. 2, the lookup table 5 includes target items associated with a first calculation interval that includes the entire interval of the detection signal (the entire period of one detection cycle). As shown in FIG. 3, the lookup table 5 also includes target items associated with a second calculation interval that is shorter than the first calculation interval. The target items shown in FIG. 2 are items that should be monitored for abnormalities over the long term, and the target items shown in FIG. 3 are items that should be monitored for abnormalities over the short term.

[0022] In the reference table 5, "vibration direction," "frequency band," "sampling frequency," and "threshold" are associated with each target item. "Vibration direction" is a parameter indicating which direction of acceleration detection signal to use from the detection signals output from the vibration sensor 2, which is a three-axis acceleration sensor. "Frequency band" is a parameter indicating which frequency band signal components to use from the detection signals output from the vibration sensor 2. "Sampling frequency" is a parameter indicating how many digital data pieces per second the detection signal is converted into. "Threshold" is a parameter set for the interval square sum of the detection signal. The "threshold" may be a value normalized based on the maximum value of the detection signal.

[0023] In the example in Figure 2, three items have been set as targets for long-term monitoring: "air spring abnormality," "axle spring / bearing abnormality," and "track abnormality." For "air spring abnormality," the "vibration direction" is up and down, the "frequency band" is 5-20 Hz, the "calculation interval" is 180,000 data points (180 seconds per entire interval), the "sampling frequency" is 1 kHz, and the "threshold" is 3,000. For "axle spring / bearing abnormality," the "vibration direction" is up and down, the "frequency band" is 5-20 Hz, the "calculation interval" is 180,000 data points (180 seconds per entire interval), the "sampling frequency" is 1 kHz, and the "threshold" is 2,000. For "track abnormality," the "vibration direction" is up and down, the "frequency band" is 5-20 Hz, the "calculation interval" is 180,000 data points (180 seconds per entire interval), the "sampling frequency" is 1 kHz, and the "threshold" is 2,000.

[0024] In the example of Figure 2, the parameters for "axle spring / bearing abnormality" and the parameters for "track abnormality" are the same, but in this case, although not shown, by introducing an additional parameter, the length of time during which the interval square sum calculated using a specified calculation interval exceeds a threshold, it becomes possible to distinguish between the two.

[0025] In the example in Figure 3, seven items have been set as targets for short-term monitoring of abnormalities: "derailment," "flat wheel tread," "snake motion," "lateral damper abnormality / yaw damper abnormality," "axle damper abnormality," "rail corrugation," and "bridge abnormality." For "derailment," the "vibration direction" is up and down, the "frequency band" is overall (DC to 1 kHz), the "calculation interval" is 100 data (100 msec), the "sampling frequency" is 1 kHz, and the "threshold" is 2000.

[0026] For "flat wheel tread," the "vibration direction" is up and down, the "frequency band" is overall (DC to 1 kHz), the "calculation interval" is 1500 data (1.5 seconds), the "sampling frequency" is 1 kHz, and the "threshold" is 1500. For "snake motion," the "vibration direction" is left and right, the "frequency band" is overall (DC to 1 kHz), the "calculation interval" is 1500 data (1.5 seconds), the "sampling frequency" is 1 kHz, and the "threshold" is 1500.

[0027] For "lateral damper abnormality / yaw damper abnormality," the "vibration direction" is left and right, the "frequency band" is 5 to 20 Hz, the "calculation interval" is 1000 data (1.0 sec), the "sampling frequency" is 1 kHz, and the "threshold" is 1000. For "axial damper abnormality," the "vibration direction" is up and down, the "frequency band" is 5 to 20 Hz, the "calculation interval" is 1000 data (1.0 sec), the "sampling frequency" is 1 kHz, and the "threshold" is 1000.

[0028] For "rail corrugated wear," the "vibration direction" is up and down, the "frequency band" is 5 to 20 Hz, the "calculation section" is 1000 data (1.0 sec), the "sampling frequency" is 1 kHz, and the "threshold" is 1000. For "bridge abnormalities," the "vibration direction" is up and down, the "frequency band" is 13 to 15 Hz, the "calculation section" is 1600 data (1.6 sec), the "sampling frequency" is 1 kHz, and the "threshold" is 1000.

[0029] In the example in Figure 3, the parameters for "axle damper abnormality" and "rail corrugation" are the same, but as in the cases of "axle spring / bearing abnormality" and "track abnormality" in Figure 2, by introducing an additional parameter, the length of time during which the interval sum of squares calculated using a specified calculation interval exceeds a threshold, it becomes possible to distinguish between the two.

[0030] FIG. 4 is a flowchart showing an example of the operation of the condition monitoring device 1 described above. The condition monitoring device 1 executes a judgment step for judging the presence or absence of an abnormality for each condition monitoring target item based on the detection signal from the vibration sensor 2. In the judgment step, as shown in FIG. 4, first, the detection signal from the vibration sensor 2 is acquired (step S01). Next, the judgment unit 3 refers to a lookup table 5 that associates a calculation interval for the detection signal with each target item (step S02). The judgment unit 3 calculates an interval sum of squares of the output value of the detection signal based on the calculation interval for each target item (step S03). After calculating the interval sum of squares, the calculated interval sum of squares is compared with a preset threshold value (step S04). The presence or absence of an abnormality for each target item is judged by comparing the interval sum of squares with the threshold value (step S05).

[0031] As described above, the condition monitoring device 1 calculates the interval sum of squares for the output value of the detection signal from the vibration sensor 2 installed on the railway vehicle 11, and determines whether or not an abnormality exists for each target item by comparing the interval sum of squares with a threshold. According to this method, by adjusting the calculation interval for calculating the sum of squares, it is possible to extract multiple feature quantities corresponding to each target item from the same detection signal. Therefore, the condition monitoring device 1 does not need to install a vibration sensor 2 for each target item or add a complex algorithm, and can monitor the status of a large number of target items without complicating the configuration or processing.

[0032] In this embodiment, the lookup table 5 includes target items associated with a first calculation interval that includes the entire interval of the detection signal, and target items associated with a second calculation interval that is shorter than the first calculation interval. Target items for condition monitoring in railway systems may include, for example, vehicle running systems such as wheels and dampers, and infrastructure systems such as rails and bridges. These target items may be classified into items that should be monitored for long-term abnormalities and items that should be monitored for short-term abnormalities. Therefore, by associating the calculation interval for calculating the sum of squares with both short-term and long-term abnormality monitoring, condition monitoring that is more in line with the actual state of the railway system can be performed.

[0033] In this embodiment, the vibration direction, frequency band, and sampling frequency may be further associated with the target items in the look-up table 5. This allows multiple feature amounts corresponding to the target items to be extracted more accurately from the same detection signal.

[0034] In this embodiment, a three-axis acceleration sensor is used as the vibration sensor 2. This makes it possible to reduce the number of vibration sensors 2 that need to be installed. Furthermore, in this embodiment, a vibration sensor 2 is provided on each of the front and rear bogies 12, 12 of the railway vehicle. By providing the vibration sensor 2 on the bogie 12 of the railway vehicle 11, it is possible to preferably acquire vibrations related to both the running system and the track system of the railway system. Furthermore, by extracting feature amounts from the detection signals from the vibration sensors 2 at different positions, it is possible to improve the accuracy of determining whether or not an abnormality exists.

[0035] The present disclosure is not limited to the above-described embodiment. For example, in the above-described embodiment, the determination unit 3 receives the detection signal from the vibration sensor 2 via the network N. However, the determination unit 3 may be provided on the railway vehicle 11 (e.g., in the driver's cab) and receive the detection signal from the vibration sensor 2 via a wired or wireless connection. Furthermore, the vibration sensor 2 does not necessarily have to be provided on each of the front and rear bogies 12, 12 of the railway vehicle 11, and may be provided on only one of the bogies 12. The location where the vibration sensor 2 is provided is not necessarily limited to the bogie 12, and may be provided at each of the front and rear ends of the railway vehicle 11, for example. When the vibration sensor 2 is provided at the front and rear ends of the railway vehicle 11, the vibration sensor 2 may be disposed under the floor in an area that does not overlap with the bogie 12 when viewed from the height direction of the railway vehicle 11.

[0036] An example of condition monitoring will be described below. In this example, a simulated vehicle 21 and a simulated rail (not shown) as shown in Fig. 5 were used, and detection signals were acquired by the vibration sensor 2 and the section sum of squares of the detection signals was calculated for three of the target items of condition monitoring shown in Figs. 2 and 3: "derailment," "wheel tread flat," and "snake behavior." In the example of Fig. 5, the vibration sensors 2 were placed near the center of the simulated vehicle 21 in the width direction (the portion corresponding to the center pan) on the front and rear bogie-equivalent portions 23, 23 of the simulated vehicle 21.

[0037] FIG. 6 is a diagram showing an example of a detection signal from a vibration sensor when a derailment occurs in the rear wheel of a simulated vehicle in the traveling direction. In FIG. 6, the horizontal axis represents time (sec) and the vertical axis represents acceleration (m / sec 2 6) and plots the detection signal in the Z direction (vehicle height direction) from the vibration sensor 2. Graph A in FIG. 6 is the detection signal from the vibration sensor 2 arranged on the bogie-equivalent portion 23 at the rear of the simulated vehicle 21 in the traveling direction, and graph B in FIG. 6 is the detection signal from the vibration sensor 2 arranged on the bogie-equivalent portion 23 at the front of the simulated vehicle 21 in the traveling direction. The sampling frequency is 1 kHz. In FIG. 6, it can be seen that the acceleration increases and decreases sharply from 2.8 seconds to 5.6 seconds after the start of measurement. In addition, several small increases and decreases in acceleration can be seen outside of this period.

[0038] FIG. 7 is a diagram showing the sectional sum of squares of the detection signal shown in FIG. 6. In FIG. 7, the horizontal axis shows time (sec) and the vertical axis shows the sectional sum of squares (normalized value). Graphs A and B in FIG. 7 are plots of the sectional sum of squares of the detection signal corresponding to graphs A and B in FIG. 6, respectively. The calculation interval for the sectional sum of squares is 100 data points (1 msec). As shown in FIG. 7, when the sectional sum of squares of the detection signal is calculated, the waveform of the vibration component related to the derailment is extracted from the detection signal as a feature.

[0039] This waveform of the vibration component shows that a derailment actually occurred in the simulated vehicle 21 during the period from 4.2 seconds to 5.6 seconds after the start of measurement. Furthermore, a comparison of graphs A and B in Fig. 7 shows that during the period from 4.2 seconds to 5.6 seconds after the start of measurement, the peak value and integral value of the waveform of the sum of squares of the section of the detection signal from the vibration sensor 2 arranged on the bogie where the derailment occurred are greater than the peak value and integral value of the waveform of the sum of squares of the section of the detection signal from the vibration sensor 2 arranged on the bogie where the derailment occurred. Therefore, by setting a threshold value for at least one of the peak value and the integral value for each of the two waveforms, it is possible to determine whether a derailment has occurred and which bogie's wheel has derailed.

[0040] FIG. 8 is a diagram showing an example of a detection signal from a vibration sensor when a tread flat occurs on the rear wheel of a simulated vehicle in the traveling direction. In FIG. 8, the horizontal axis represents time (sec) and the vertical axis represents acceleration (m / sec 2 8) and plots the detection signal in the Z direction (vehicle height direction) from the vibration sensor 2. Graph A in FIG. 8 is the detection signal from the vibration sensor 2 arranged in the bogie-equivalent portion 23 on the rear side of the simulated vehicle 21 in the traveling direction, and graph B in FIG. 8 is the detection signal from the vibration sensor 2 arranged in the bogie-equivalent portion 23 on the front side of the simulated vehicle 21 in the traveling direction. The sampling frequency is 1 kHz. In FIG. 8, it can be seen that the acceleration increases and decreases sharply in the period from 2 seconds to 14 seconds after the start of measurement.

[0041] FIG. 9 is a diagram showing the interval sum of squares of the detection signal shown in FIG. 8. In FIG. 9, the horizontal axis shows time (sec) and the vertical axis shows the interval sum of squares (normalized value). Graphs A and B in FIG. 9 are plots of the interval sum of squares of the detection signal corresponding to graphs A and B in FIG. 8, respectively. The calculation interval for the interval sum of squares is 1500 data points (1.5 seconds). As shown in FIG. 9, when the interval sum of squares of the detection signal is calculated, the waveform of the vibration component related to the tread flat is extracted from the detection signal as a feature.

[0042] The waveform of this vibration component shows that a wheel tread flat actually occurred on the simulated vehicle 21 between 2 and 12 seconds after the start of measurement. Furthermore, comparing graphs A and B in Figure 9, it can be seen that there is a difference in the shape of the two waveforms. Therefore, by setting threshold values ​​such as peak values ​​and integral values ​​for the two waveforms, it is possible to determine whether a wheel tread flat has occurred and on which bogie wheel the wheel tread flat occurred.

[0043] FIG. 10 is a diagram showing an example of a detection signal from a vibration sensor when a wheel on the rear side of the simulated vehicle in the traveling direction starts to snake. In FIG. 10, the horizontal axis represents time (sec) and the vertical axis represents acceleration (m / sec 2 ) and plots the detection signal in the Y direction (vehicle width direction) from the vibration sensor 2 arranged on the bogie-equivalent portion 23 at the rear of the simulated vehicle 21 in the traveling direction. The sampling frequency is 1 kHz. In FIG. 10, it can be seen that the acceleration increases and decreases sharply in the period from immediately after the start of measurement to 10 seconds.

[0044] FIG. 11 is a diagram showing the interval sum of squares of the detection signal shown in FIG. 10. In FIG. 11, the horizontal axis represents time (sec) and the vertical axis represents the interval sum of squares (normalized value). The calculation interval for the interval sum of squares is 1500 data points (1.5 sec). As shown in FIG. 11, when the interval sum of squares of the detection signal is calculated, the waveform of the vibration component related to hunting is extracted from the detection signal as a feature. In FIG. 10, the acceleration appears to increase and decrease uniformly from immediately after the start of measurement to the 10-second period. However, in FIG. 11, the increase and decrease in the interval sum of squares is clearly evident, and it can be seen that the waveform peaks around 5.5 seconds after the start of measurement. Therefore, by setting a threshold for the peak value of this interval sum of squares waveform, it is possible to determine whether hunting is occurring.

[0045] In the detection signals during abnormal conditions shown in Figures 6, 8, and 10, a sudden increase or decrease in acceleration continues in response to the occurrence of the abnormality, but the waveforms of the detection signals during the period when the abnormality occurs are similar to each other, making it difficult to find the feature quantities for each target item for condition monitoring from the waveforms. On the other hand, the section sum of squares of the detection signals shown in Figures 7, 9, and 11 shows that multiple feature quantities corresponding to each target item can be extracted as waveforms. Therefore, by setting appropriate thresholds for these section sums of squares, it is possible to monitor the condition of multiple target items without installing vibration sensors for each target item or adding complex algorithms. [Explanation of symbols]

[0046] 1...condition monitoring device, 2...vibration sensor, 3...judgment unit, 4...memory unit, 5...reference table, 11...railroad vehicle, 12...bogie

Claims

1. a vibration sensor provided on the railway vehicle; a determination unit that determines whether or not there is an abnormality for each item subject to status monitoring based on the detection signal from the vibration sensor; a storage unit that stores a reference table that associates a calculation interval for the detection signal with each of the target items, The judgment unit calculates an interval sum of squares of the output values ​​of the detection signal without performing filter processing based on the calculation interval for each of the target items, and determines whether or not there is an abnormality for each of the target items by comparing the interval sum of squares with a preset threshold value.

2. 2. The condition monitoring device according to claim 1, wherein the reference table includes target items associated with a first calculation interval that includes the entire interval of the detection signal, and target items associated with a second calculation interval that is shorter than the first calculation interval.

3. 3. The condition monitoring device according to claim 1, wherein the target items in the reference table are further associated with a vibration direction, a frequency band, and a sampling frequency.

4. 4. The condition monitoring device according to claim 1, wherein the vibration sensor is a three-axis acceleration sensor.

5. 5. The condition monitoring device according to claim 1, wherein the vibration sensors are provided at each of the front and rear ends of the railway vehicle or on each of the front and rear bogies.

6. a determining step of determining whether or not there is an abnormality for each item to be monitored based on a detection signal from the vibration sensor; In the judgment step, a reference table that associates a calculation interval for the detection signal with each target item is referenced, an interval sum of squares of the output value of the detection signal without filter processing is calculated based on the calculation interval for each target item, and the interval sum of squares is compared with a preset threshold value to determine whether or not there is an abnormality for each target item.

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