JP7079641B2 - Condition monitoring device - Google Patents

Description

The present invention relates to a condition monitoring device used in a railroad vehicle.

In order to ensure the safety of railway vehicles when traveling, a device has been developed in which sensors are installed on the vehicle body or traveling device to monitor the presence or absence of abnormalities in the traveling state and deterioration of bogie parts (for example, patent documents). 1). For example, the device described in Patent Document 1 includes an acceleration sensor installed in a railroad vehicle, and the integration obtained by integrating signals in a specific frequency band of acceleration detected by the acceleration sensor at predetermined time intervals. The state of the railroad vehicle is determined based on the difference between the value and the integrated value before a predetermined time.

Japanese Unexamined Patent Publication No. 2002-211,400

Inspecting railway vehicles and railroad tracks, taking into consideration the ride quality of railway vehicles, is generally performed once a month, for example, in consideration of the deterioration speed and work efficiency of parts and railroad tracks in railway vehicles. ing. However, due to the accelerated deterioration of parts and railroad tracks due to various causes, there are cases where a railway vehicle that does not have a desired ride quality is running. Therefore, it is required to improve the work efficiency in determining the riding comfort. In addition, the vibration of a railroad vehicle includes frequencies that passengers find comfortable and frequencies that passengers find unpleasant. That is, the degree of contribution to ride comfort differs depending on the frequency. Therefore, it is considered necessary to determine the riding comfort in consideration of not only the strength of the vibration but also the difference in the frequency component of the vibration generated in the railway vehicle.

An object of the present invention is to provide a condition monitoring device that can accurately determine the riding comfort of a railway vehicle with a small work load.

The state monitoring device according to one aspect of the present invention is an acceleration sensor provided in a railroad vehicle, and a first calculation means for calculating characteristic data related to acceleration for each frequency by Fourier-converting detection signal data from the acceleration sensor. , A second calculation means for calculating the distance of Maharanobis between the characteristic data calculated by the first calculation means and the unit data acquired in advance, and a comparison means for comparing the distance of Maharanobis with a preset threshold value. And a determination means for determining the ride comfort level of the railroad vehicle according to the comparison result in the comparison means.

In this condition monitoring device, the Mahalanobis distance between the characteristic data regarding the acceleration for each frequency calculated by the first calculation means and the unit data acquired in advance is calculated. The calculated Mahalanobis distance is a single piece of data that takes into account the frequency-by-frequency differences from the unit data for the desired frequency range. In the condition monitoring device, the ride quality level of the railway vehicle is determined by comparing the Mahalanobis distance calculated in this way with the threshold value. Therefore, the condition monitoring device can easily and accurately determine the ride quality level in consideration of the difference in the degree of contribution to the ride quality for each frequency while the railway vehicle is running. That is, the condition monitoring device has a small work load and can accurately determine the riding comfort of a railway vehicle.

The first calculation means may calculate the characteristic data by Fourier transforming each of the plurality of detection signal data output from the acceleration sensor at different times and then averaging them. In this case, the influence of erroneous detection by the acceleration sensor can be reduced.

The unit data is characteristic data when a normal railroad vehicle travels on a normal railroad track, and is the acceleration for each frequency obtained by Fourier-converting each of a plurality of detection signal data output from the acceleration sensor at different times or different traveling sections. May contain data about. In this case, the ride comfort level can be determined in consideration of the deterioration of both the railroad vehicle and the railroad track, and the variation in the detection result of the acceleration sensor due to the difference in time or the difference in the traveling section.

The plurality of detection signal data output from the acceleration sensor may include detection signal data output in overlapping traveling sections at different times. In this case, the ride quality level can be determined in consideration of the variation in the detection result of the acceleration sensor when traveling in the same traveling section.

At least one stop may be located between at least two of the different travel sections. In this case, the ride quality level can be determined in consideration of the variation in the detection result of the acceleration sensor in different traveling sections sandwiching at least one stop station.

Different travel sections may include different travel sections located between adjacent stations. In this case, the ride quality level can be determined by the unit data suitable for the corresponding stop stations.

The condition monitoring device may further include an acquisition means for acquiring at least one of the vehicle type of the railroad vehicle and the type of the traveling section in which the railroad vehicle travels, and the comparison means depends on the information acquired by the acquisition means. The threshold value may be compared with the Mahalanobis distance calculated by the second calculation means. In this case, the criterion for determining the ride quality level can be changed depending on the vehicle type of the railway vehicle and the type of the traveling section in which the railway vehicle travels.

The condition monitoring device may further include an acquisition means for acquiring at least one of the vehicle type of the railroad vehicle and the type of the traveling section in which the railroad vehicle travels, and the second calculation means is acquired by the acquisition means. The Mahalanobis distance may be calculated based on the unit data according to the information. In this case, the criterion for determining the ride quality level can be changed depending on the vehicle type of the railway vehicle and the type of the traveling section in which the railway vehicle travels.

The accelerometer may be located directly above the center plate of the railroad vehicle. In this case, the accelerometer can improve the accuracy of the vibrations that occur in the railcar.

The condition monitoring device may further include a communication means for communicating with an external depot, and the communication means determines that the Mahalanobis distance calculated by the second calculation means is equal to or greater than the threshold value. You may also notify the depot. In this case, the amount of communication with the depot can be reduced.

The second calculation means is from the characteristic data calculated from the detection signal data in the vertical direction of the railway vehicle, the characteristic data calculated from the detection signal data in the left-right direction of the railway vehicle, and the detection signal data in the front-rear direction of the railway vehicle. The distance of Maharanobis may be calculated based on each of the calculated characteristic data. When it is determined that the ride quality is poor based on the detection signal data in the vertical direction, the factors that deteriorate the ride quality are considered to be track deviation on the track, puncture of the air spring, or failure of the shaft damper. If it is determined that the ride quality is poor based on the detection signal data in the left-right direction, the factors that deteriorate the ride quality are considered to be track deviation on the track and failure of the left-right motion damper or yaw damper. If it is determined that the ride quality is poor based on the detection signal data in the front-rear direction, the factors that deteriorated the ride quality include track deviation on the track, abnormalities around the coupler, and failure of the yaw damper or inter-body damper. Conceivable. Therefore, by calculating the Mahalanobis distance based on each of the above-mentioned characteristic data by the second calculation means, it is possible to easily detect the track deviation on the railroad track and the occurrence of failure of each part in the railroad vehicle. ..

According to the present invention, it is possible to provide a condition monitoring device that has a small work load and can accurately determine the riding comfort of a railway vehicle.

It is a figure for demonstrating the whole structure of the state monitoring apparatus which concerns on embodiment of this invention. It is a block diagram of a part of the state monitoring apparatus shown in FIG. It is a block diagram of a part of the state monitoring apparatus shown in FIG. It is a figure which shows an example of the data about the vibration in the vertical direction among the detection signal data from an acceleration sensor. It is a figure which shows an example of the data about the vibration in the vertical direction among the detection signal data from an acceleration sensor. It is a figure which shows an example of the characteristic data about the acceleration in the vertical direction for every frequency. It is a figure which shows an example of the characteristic data about the acceleration in the vertical direction for every frequency. It is a figure which shows an example of the data about the vibration in the left-right direction among the detection signal data from an acceleration sensor. It is a figure which shows an example of the data about the vibration in the left-right direction among the detection signal data from an acceleration sensor. It is a figure which shows an example of the characteristic data about the acceleration in the left-right direction for every frequency. It is a figure which shows an example of the characteristic data about the acceleration in the left-right direction for every frequency. It is a figure which shows an example of the data about the vibration in the front-rear direction among the detection signal data from an acceleration sensor. It is a figure which shows an example of the data about the vibration in the front-rear direction among the detection signal data from an acceleration sensor. It is a figure which shows an example of the characteristic data about the acceleration in the anteroposterior direction for every frequency. It is a figure which shows an example of the characteristic data about the acceleration in the anteroposterior direction for every frequency. It is a figure for demonstrating the comparative example of the determination of the ride quality level. It is a figure for demonstrating the comparative example of the determination of the ride quality level.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the description, the same reference numerals are used for the same elements or elements having the same function, and duplicate explanations will be omitted.

First, the physical configuration of the condition monitoring device will be described with reference to FIG. FIG. 1 is a diagram for explaining the overall configuration of the condition monitoring device. The condition monitoring device 1 has various devices arranged in the railroad vehicle 2. As shown in FIG. 1, the railroad vehicle 2 is composed of a plurality of vehicles 20. The condition monitoring device 1 determines the ride quality level of the railway vehicle 2 by analyzing the vibration generated in each vehicle 20.

The railroad vehicle 2 has a bogie 21, an air spring 22, an acceleration sensor 23, a calculation unit 24, a relay unit 25, and a control unit 26 in each vehicle 20. The air spring 22 is provided between the vehicle body of the vehicle 20 and the bogie 21, and suppresses the vibration of the vehicle 20.

The acceleration sensor 23 is a part that detects the vibration of each vehicle 20. The calculation unit 24 is a part that mainly determines the ride quality level of the vehicle 20. Here, the "ride quality level" is a degree indicating whether the ride quality is good or bad. The relay unit 25 is a part that relays the determination result of each arithmetic unit 24 and transmits it to the control unit 26. The control unit 26 is a part that notifies and records the determination result received from each calculation unit 24.

As shown in FIG. 1, the acceleration sensor 23 is arranged directly above the bogie 21 of the railroad vehicle 2. In the present embodiment, the acceleration sensor 23 is arranged directly above the center plate (position corresponding to the rotation center of the carriage 21 on the floor, in the floor, or under the floor of the vehicle 20). The accelerometer 23 may be arranged on the end parts 27, 28 of the vehicle 20 (a portion formed by the end part of the vehicle 20 in the longitudinal direction), or may be arranged on a side wall near the end part (for example, a side structure). ) Or, it may be arranged on the ceiling (for example, a roof structure) near the wife.

The arithmetic unit 24 is arranged on one end 27 of the vehicle 20. The arithmetic unit 24 may be provided only in at least one of the vehicles 20 having a crew room having a driver's cab or the like (for example, the leading vehicle 20 and the trailing vehicle 20). The relay unit 25 is arranged, for example, on the other wife portion 28 of the vehicle 20. The control unit 26 is arranged, for example, in the leading vehicle 20 and the trailing vehicle 20 of the railway vehicle 2, respectively. The control unit 26 may be provided in only one of the vehicles 20 having a crew room having a driver's cab or the like. The arithmetic unit 24, the relay unit 25, and the control unit 26 may be arranged inside or outside the vehicle.

Next, with reference to FIG. 2, the functional configurations of the acceleration sensor 23, the calculation unit 24, and the relay unit 25 will be described. FIG. 2 shows an acceleration sensor 23, an arithmetic unit 24, and a relay unit 25, which are a part of the condition monitoring device 1.

The acceleration sensor 23 detects the acceleration in the vertical direction, the horizontal direction, and the front-rear direction of the vehicle 20 generated in response to the vibration of the vehicle 20. The acceleration detected by the acceleration sensor 23 is sequentially transmitted to the calculation unit 24.

As shown in FIG. 2, the calculation unit 24 has a reception unit 31, a state calculation unit 32, a unit space database 33, and a communication unit 34. The receiving unit 31 receives the detection signal data from at least one acceleration sensor 23 provided in the same vehicle 20. The receiving units 31 are connected to each other so as to be able to communicate with each other by short-range wireless communication such as Bluetooth (registered trademark). The connection is not limited to wireless, but may be wired.

The state calculation unit 32 determines the state of the vehicle 20 based on the detection signal data from the acceleration sensor 23 received by the reception unit 31. In the present embodiment, the state calculation unit 32 determines the ride quality level of the vehicle 20 by the MTS (Mahalanobis-Taguchi System). Specifically, the state calculation unit 32 calculates the Mahalanobis distance with reference to the unit space database 33, and determines the ride comfort level of the vehicle 20 by comparing the calculated Mahalanobis distance with the threshold value. The state calculation unit 32 outputs the determination result of the ride quality level to the communication unit 34.

The unit space database 33 stores the unit data (unit space data) acquired in advance used for the MTS. The unit data is characteristic data when a normal railroad vehicle 2 travels on a normal railroad track. The unit data is obtained by fast Fourier transforming each of the n detection signal data output from the acceleration sensor 23, and is n data related to the acceleration for each frequency. In other words, the unit data is characteristic data showing information on acceleration for each frequency (frequency spectrum of information on acceleration).

The n detection signal data output from the acceleration sensor 23 include the detection signal data output from the acceleration sensor 23 at different times or different traveling sections. In the present embodiment, the n detection signal data includes detection signal data output in overlapping traveling sections at different times. For example, the n detection signal data may include detection signal data output from the acceleration sensor 23 when the railway vehicle 2 travels in the same traveling section a plurality of times.

In the present embodiment, the n detection signal data include detection signal data output in different traveling sections at overlapping times. For example, the n detection signal data includes a plurality of detection signal data output from the acceleration sensor when a plurality of normal railroad vehicles travel on different traveling sections of a normal track at overlapping times. May be good.

In the present embodiment, at least one stop station is located between at least two of the different traveling sections. In other words, the unit data in the present embodiment is the data related to the acceleration for each frequency obtained by fast Fourier transforming each of the plurality of detection signal data output from the acceleration sensor in different traveling sections sandwiching at least one stop station. Includes. In the present embodiment, the different traveling sections include different traveling sections located between the stations adjacent to each other. In other words, the unit data in the present embodiment relates to the acceleration for each frequency obtained by fast Fourier transforming each of the plurality of detection signal data output from the acceleration sensor in different traveling sections located between the stations adjacent to each other. Contains data.

As the unit data, for example, m railroad vehicle IDs and unit data are stored in advance for each traveling section ID and each time. Here, the railway vehicle ID and the traveling section ID indicate the vehicle type of the railway vehicle and the type of the traveling section in which the railway vehicle travels, respectively. Since the physically same railroad vehicle 2 may be used for different purposes at different times, even if different railroad vehicle IDs are attached to the physically same railroad vehicle 2 in consideration of such a case. good. For example, the railroad vehicle 2 used as a limited express train in the morning may be used as a normal train in the daytime, or the railroad vehicle 2 carrying passengers may travel as a forwarding train. The railroad vehicle ID may indicate the traveling mode of the railroad vehicle 2 such as a forwarding train, an ordinary train, a rapid train, and a limited express train, or indicates the grade of each vehicle 20 such as an ordinary vehicle and a green vehicle. May be.

In the present embodiment, the unit data is data showing acceleration for each frequency, that is, data showing frequency characteristics of acceleration (frequency spectrum of acceleration). The unit data is not limited to this as long as it is data indicating information on acceleration. The unit data may be, for example, data showing PSD (Power Spectral Density, power spectral density function) for each frequency, that is, PSD frequency characteristics (PSD frequency spectrum).

The communication unit 34 (acquisition means) has information on the state of the vehicle 20 determined by the state calculation unit 32 between the adjacent relay unit 25 (ride comfort level determination result and various information used for the determination (for example). , Characteristic data)), etc., is the part that sends and receives information. The communication unit 34 is connected to the adjacent vehicle 20 and the relay unit 25 arranged in the same vehicle 20 so as to be able to communicate with each other by short-range wireless communication such as Bluetooth (registered trademark). The connection is not limited to wireless, but may be wired.

When the communication unit 34 receives (acquires) at least one of the railroad vehicle ID and the traveling section ID from the control unit 26 via the adjacent relay unit 25, the communication unit 34 receives at least one of the received railway vehicle ID and traveling section ID. Is output to the state calculation unit 32. The traveling section ID received by the communication unit 34 may be the ID of the traveling section in which the railway vehicle 2 is currently traveling, or may be the ID of the traveling section to be traveled in the future.

The relay unit 25 has, for example, a communication unit 41 as a functional component. The communication unit 41 is a part that relays transmission / reception with the adjacent arithmetic unit 24 or the control unit 26. The communication unit 41 is connected to the arithmetic unit 24 or the control unit 26 arranged in the adjacent vehicle 20 and the same vehicle 20 so as to be able to communicate with each other by short-range wireless communication such as Bluetooth (registered trademark). The connection is not limited to wireless, but may be wired.

Next, with reference to FIG. 3, the functional configuration of the control unit 26 will be described in detail. FIG. 3 shows a control unit 26 that is a part of the condition monitoring device 1.

The control unit 26 has a communication unit 51, a notification unit 52, and a determination result storage unit 53. The communication unit 51 is a part that transmits / receives (communicates) information between the external vehicle base 70 and the adjacent relay unit 25. The communication unit 51 is connected to the relay unit 25 arranged in the vehicle 20 so as to be able to communicate with each other by short-range wireless communication such as Bluetooth. The connection is not limited to wireless, but may be wired.

The communication unit 51 includes, for example, a determination result receiving unit 54 and an ID receiving unit 55. The determination result receiving unit 54 outputs the determination result received from the adjacent relay unit 25 to the notification unit 52 and the determination result storage unit 53, respectively. Further, the ID receiving unit 55 receives at least one of the railroad vehicle ID and the traveling section ID from the depot 70 and transmits them to each arithmetic unit 24 via the relay unit 25. The vehicle base 70 that transmits at least one of the railway vehicle ID and the traveling section ID is located, for example, in a station yard or each train ward. The traveling section ID transmitted from the depot 70 may be the ID of the traveling section in which the railway vehicle 2 is currently traveling, or may be the ID of the traveling section to be traveled in the future.

The notification unit 52 is a unit that notifies the determination result received from the communication unit 51. The notification unit 52 is provided with, for example, a display, and displays a determination result for each vehicle 20 when the railway vehicle 2 is traveling. The determination result storage unit 53 is a portion for storing the determination result. In the determination result storage unit 53, for example, the time when the communication unit 51 receives the determination result and the determination result are stored in association with each other.

Next, a method for determining the state of the railroad vehicle 2 in the arithmetic unit 24 will be described in detail. As shown in FIG. 2, the state calculation unit 32 includes a first calculation unit 36 (first calculation means), a second calculation unit 37 (second calculation means), and a comparison unit 38 ( It has a comparison means) and a determination unit 39 (determination means).

The first calculation unit 36 calculates characteristic data related to acceleration for each frequency by performing a fast Fourier transform on the detection signal data from the acceleration sensor 23 received by the reception unit 31. In the present embodiment, the first calculation unit 36 averages the plurality of detection signal data output from the acceleration sensor 23 at different times (in different traveling sections) by the acceleration sensor 23 after performing a fast Fourier transform. Calculate characteristic data.

For example, the first calculation unit 36 acquires eight detection signal data from the acceleration sensor 23 for each of eight different traveling sections, and fast Fourier transforms each of the eight detection signal data. The first calculation unit 36 calculates the characteristic data by averaging the eight data obtained by the fast Fourier transform. The first calculation unit 36 calculates the above-mentioned characteristic data from the detection signal data in the vertical direction, the horizontal direction, and the front-rear direction output from the acceleration sensor.

In the present embodiment, the first calculation unit 36 calculates characteristic data indicating acceleration for each frequency, that is, data indicating frequency characteristics of acceleration (frequency spectrum of acceleration). The characteristic data calculated by the first calculation unit 36 is not limited to this as long as it is data indicating information on acceleration. For example, in the first calculation unit 36, when the unit data stored in the unit space database 33 is the PSD data for each frequency, the characteristic data indicating the PSD for each frequency, that is, the frequency characteristic of the PSD (PSD). Data indicating the frequency spectrum of) may be calculated.

4 and 5 show an example of data related to vibration in the vertical direction among the detection signal data from the acceleration sensor 23. The vertical axis shows acceleration and the horizontal axis shows time. 6 and 7 are diagrams showing an example of characteristic data regarding vertical acceleration for each frequency calculated by the first calculation unit 36. The vertical axis shows the acceleration in logarithm, and the horizontal axis shows the frequency in logarithm. 4 and 6 show detection signal data in a state where the ride quality is normal, and characteristic data calculated based on the detection signal data. 5 and 7 show detection signal data in a state where the ride quality is abnormal, and characteristic data calculated based on the detection signal data.

8 and 9 show an example of data related to vibration in the left-right direction among the detection signal data from the acceleration sensor 23. The vertical axis shows acceleration and the horizontal axis shows time. 10 and 11 are diagrams showing an example of characteristic data regarding acceleration in the left-right direction for each frequency calculated by the first calculation unit 36. The vertical axis shows the acceleration in logarithm, and the horizontal axis shows the frequency in logarithm. 8 and 10 show detection signal data in a state where the ride quality is normal, and characteristic data calculated based on the detection signal data. 9 and 11 show detection signal data in a state where the ride quality is abnormal, and characteristic data calculated based on the detection signal data.

12 and 13 show an example of data related to vibration in the front-rear direction among the detection signal data from the acceleration sensor 23. The vertical axis shows acceleration and the horizontal axis shows time. 14 and 15 are diagrams showing an example of characteristic data regarding acceleration in the front-rear direction for each frequency calculated by the first calculation unit 36. The vertical axis shows the acceleration in logarithm, and the horizontal axis shows the frequency in logarithm. 12 and 14 show detection signal data in a state where the ride quality is normal, and characteristic data calculated based on the detection signal data. 13 and 15 show detection signal data in a state where the ride quality is abnormal, and characteristic data calculated based on the detection signal data.

The second calculation unit 37 calculates the Mahalanobis distance between the characteristic data calculated by the first calculation unit 36 and the unit data stored in the unit space database 33. In the present embodiment, the first calculation unit 36 calculates characteristic data from the detection signal data in the vertical direction, the horizontal direction, and the front-back direction, respectively. Therefore, the second calculation unit 37 calculates the Mahalanobis distance based on the characteristic data in the vertical direction, the horizontal direction, and the front-back direction, respectively.

In the present embodiment, the second calculation unit 37 acquires at least one of the railroad vehicle ID of the railroad vehicle 2 provided with the calculation unit 24 and the traveling section ID of the railroad vehicle 2, and is based on these. Unit data for calculating the distance of Maharanobis is extracted from the unit space database 33. In the present embodiment, the second calculation unit 37 acquires at least one of the railroad vehicle ID of the railroad vehicle 2 and the traveling section ID in which the railroad vehicle 2 travels from the communication unit 34. In this case, the second calculation unit 37 calculates the Mahalanobis distance using the extracted unit data and the characteristic data calculated by the first calculation unit 36. That is, the second calculation unit 37 calculates the Mahalanobis distance based on the unit data corresponding to the information acquired by the communication unit 34. The unit data extracted by the second calculation unit 37 may be set by the user. The Mahalanobis distance may be calculated for each vehicle 20 by the second calculation unit 37 of the calculation unit 24 provided in each vehicle 20, or by one second calculation unit 37, the leading vehicle 20 to the tail vehicle. It may be calculated collectively for all the vehicles 20 up to 20.

The second calculation unit 37 determines, for example, whether it is a normal vehicle or a green vehicle from the railway vehicle ID. When the second calculation unit 37 determines that the vehicle is an ordinary vehicle, the unit data corresponding to the ordinary vehicle is extracted from the unit space database 33, and the Mahalanobis distance is calculated using the unit data corresponding to the ordinary vehicle. do. When the second calculation unit 37 determines that the vehicle is a green vehicle, the unit data corresponding to the green vehicle is extracted from the unit space database 33, and the Mahalanobis distance is calculated using the unit data corresponding to the green vehicle. do. The second calculation unit 37 may extract different unit data according to the traveling mode of the deadhead train, the ordinary train, the rapid train, the limited express train, etc., and calculate the Mahalanobis distance according to the traveling mode. ..

The comparison unit 38 compares the Mahalanobis distance calculated by the second calculation unit 37 with a preset threshold value. Specifically, the comparison unit 38 determines whether or not the Mahalanobis distance calculated by the second calculation unit 37 is equal to or greater than the threshold value. In the present embodiment, the comparison unit 38 compares the distances of the Mahalanobis in the vertical direction, the horizontal direction, and the front-rear direction, respectively.

In the present embodiment, the threshold value used by the comparison unit 38 is "4", and the comparison unit 38 determines whether or not the calculated Mahalanobis distance is "4" or more. The determination unit 39 determines the ride quality level of the vehicle 20 according to the comparison result in the comparison unit 38. In the present embodiment, the comparison unit 38 acquires at least one of the railroad vehicle ID of the railroad vehicle 2 provided with the calculation unit 24 and the traveling section ID in which the railroad vehicle 2 travels, and the threshold value is based on these. To decide. In the present embodiment, the comparison unit 38 acquires at least one of the railway vehicle ID of the railway vehicle 2 and the traveling section ID in which the railway vehicle 2 travels from the communication unit 34. That is, the comparison unit 38 determines the threshold value according to the information acquired by the communication unit 34. The threshold may be set by the user.

For example, the comparison unit 38 determines whether it is a normal vehicle or a green vehicle from the railway vehicle ID. When the comparison unit 38 determines that the vehicle is an ordinary vehicle, the comparison unit 38 compares the distance between the threshold value corresponding to the ordinary vehicle and the Mahalanobis. When the comparison unit 38 determines that the vehicle is a green vehicle, the comparison unit 38 compares the distance between the threshold value corresponding to the green vehicle and the Mahalanobis. The comparison unit 38 may perform the above comparison using different threshold values depending on the traveling mode of the deadhead train, the ordinary train, the rapid train, the limited express train, and the like.

The determination unit 39 determines the ride quality level of the railway vehicle 2 according to the comparison result in the comparison unit 38. In the present embodiment, the determination unit 39 determines the ride quality level in the vertical direction, the horizontal direction, and the front-rear direction, respectively. For example, when the comparison unit 38 determines that the Mahalanobis distance is "4" or more, the determination unit 39 determines that the ride quality level is poor. For example, in the characteristic data shown in FIGS. 6, 10, and 14, the second calculation unit 37 derives the Mahalanobis distance less than “4”. In this case, the determination unit 39 determines that the ride quality level is good. In the characteristic data shown in FIGS. 7, 11 and 15, the second calculation unit 37 derives the Mahalanobis distance of “4” or more. In this case, the determination unit 39 determines that the ride quality level is poor. The determination of the ride quality level of the railroad vehicle 2 may be calculated for each vehicle 20 by the determination unit 39 of the calculation unit 24 provided in each vehicle 20, or by one determination unit 39, the leading vehicle 20 to the tail vehicle. All vehicles 20 up to 20 may be collectively determined.

When the determination unit 39 determines that the ride quality level is poor (when the comparison unit 38 determines that the Mahalanobis distance calculated by the second calculation unit 37 is equal to or greater than the threshold value), the communication unit 34 determines that the ride quality level is poor. The determination result and the characteristic data calculated by the first calculation unit 36 are transmitted to the control unit 26 via the adjacent relay unit 25. In this case, the communication unit 51 (communication means) of the control unit 26 transmits (notifies) data indicating that the determination unit 39 has determined that the ride quality level is poor to the external vehicle base 70. The communication unit 34 may directly transmit / receive information to / from the control unit 26 or to an external vehicle base 70. For example, when the determination unit 39 determines that the ride quality level is poor, the communication unit 34 directly transfers the determination result and the characteristic data calculated by the first calculation unit 36 to the control unit 26 or an external unit. It may be transmitted to the depot 70.

Next, the operation and effect of the state monitoring device 1 will be described. The acceleration sensor 23 detects the vibration generated when the railroad vehicle 2 is traveling as acceleration in the vertical direction, the horizontal direction, and the front-rear direction, and outputs the detection signal data. The state calculation unit 32 determines the ride quality level of the railway vehicle 2 based on the detection signal data output from the acceleration sensor 23.

The vibration of a railroad vehicle includes frequencies that passengers find comfortable and frequencies that passengers find unpleasant. That is, the degree of contribution to ride comfort differs depending on the frequency. Therefore, for example, as shown in FIGS. 16 and 17, the ride quality level is determined by comparing the ride quality reference line and the characteristic data in consideration of the difference in the frequency component of the vibration generated in the railway vehicle. It is conceivable to do. 16 and 17 show characteristic data relating to vertical acceleration for each frequency as an example of characteristic data.

FIG. 16 shows the reference lines (a), (b), (c), (d) of the ride quality and the characteristic data (e) in the state where the ride quality is normal. FIG. 17 shows the reference lines (a), (b), (c), (d) of the ride quality and the characteristic data (f) in the state where the ride quality is abnormal. The reference lines (a), (b), (c) and (d) correspond to better riding comfort in the order of (a), (b), (c) and (d). For example, the reference line (a) is a reference line indicating the standard of riding comfort of a normal vehicle, and the reference line (b) is a reference line indicating the standard of riding comfort of a green vehicle.

The more the characteristic data exceeds the above-mentioned reference line, the worse the ride quality of the vehicle 20 from which the characteristic data is obtained. For example, in FIG. 16, since the characteristic data (e) is characteristic data in a state where the ride quality is normal (a state where the ride quality is good), the reference lines (a), (b), (c), (d). ) Is located below any of them. On the other hand, in FIG. 17, a part of the characteristic data (f) is located above the reference lines of (b) and (c). Therefore, it can be seen that the railroad vehicle 2 from which the characteristic data (f) has been acquired is less comfortable to ride than the railroad vehicle 2 from which the characteristic data (e) has been acquired. However, with this method, it is difficult to determine at what position and how much the characteristic data exceeds the reference line to determine whether it is appropriate to determine that the ride quality is poor.

In the condition monitoring device 1, the Mahalanobis distance between the characteristic data related to the acceleration for each frequency calculated by the first calculation unit 36 and the unit data acquired in advance is calculated. The calculated Mahalanobis distance is a single piece of data that takes into account the frequency-by-frequency differences from the unit data for the desired frequency range. In the condition monitoring device 1, the ride quality level of the railway vehicle is determined by comparing the Mahalanobis distance calculated in this way with the threshold value. Therefore, in the condition monitoring device 1, the determination is made using the Mahalanobis distance, so that the ride comfort level is set in consideration of the difference in the degree of contribution to the ride comfort for each frequency while the railway vehicle 2 is running. Judgment can be performed easily and accurately. That is, the condition monitoring device 1 has a small work load and can accurately determine the riding comfort of a railway vehicle.

The first calculation unit 36 calculates characteristic data by performing a Fourier transform on each of the plurality of detection signal data output from the acceleration sensor 23 at different times and then averaging them. Therefore, the influence of erroneous detection by the acceleration sensor 23 can be reduced.

The unit data is characteristic data when a normal railroad vehicle 2 travels on a normal railroad track, and is for each frequency obtained by Fourier-converting each of a plurality of detection signal data output from the acceleration sensor 23 at different times or different traveling sections. Contains data on the acceleration of. Therefore, the ride comfort level can be determined in consideration of the deterioration of both the railroad vehicle 2 and the railroad track, and the variation in the detection result of the acceleration sensor 23 due to the difference in time or the difference in the traveling section.

The plurality of detection signal data output from the acceleration sensor 23 includes the detection signal data output in the traveling sections overlapping at different times. Therefore, the ride quality level can be determined in consideration of the variation in the detection result of the acceleration sensor 23 when traveling in the same traveling section.

At least one stop is located between at least two of the different travel sections. Therefore, the ride quality level can be determined in consideration of the variation in the detection result of the acceleration sensor 23 in different traveling sections sandwiching at least one stop station.

Different travel sections include different travel sections located between adjacent stations. Therefore, the ride quality level can be determined by the unit data suitable for the corresponding stop stations.

The communication unit 34 acquires at least one of the vehicle type (railroad vehicle ID) of the railway vehicle 2 and the type of the traveling section (traveling section ID) in which the railway vehicle 2 travels. The comparison unit 38 compares the threshold value according to the above information acquired by the communication unit 34 with the Mahalanobis distance calculated by the second calculation unit 37. Therefore, the criterion for determining the ride quality level can be changed depending on the vehicle type of the railway vehicle 2 and the type of the traveling section in which the railway vehicle 2 travels.

For example, when the comparison unit 38 determines from the railroad vehicle ID that the vehicle 20 is a normal vehicle, the comparison unit 38 compares the threshold value corresponding to the normal vehicle and the Mahalanobis distance. When the comparison unit 38 determines from the railroad vehicle ID that the vehicle 20 is a green vehicle, the comparison unit 38 compares the threshold value corresponding to the green vehicle and the Mahalanobis distance. The comparison unit 38 may perform the above comparison using different threshold values depending on the traveling mode of the deadhead train, the ordinary train, the rapid train, the limited express train, and the like. According to this, the ride quality level can be appropriately determined according to the use of each vehicle 20 or the traveling mode of the railway vehicle 2.

The second calculation unit 37 calculates the Mahalanobis distance based on the unit data according to the above information acquired by the communication unit 34. Therefore, the criterion for determining the ride quality level can be changed depending on the vehicle type of the railway vehicle 2 and the type of the traveling section in which the railway vehicle 2 travels.

For example, when the second calculation unit 37 determines from the railroad vehicle ID that the vehicle 20 is a normal vehicle, the second calculation unit 37 calculates the Mahalanobis distance using the unit data corresponding to the normal vehicle. When the second calculation unit 37 determines from the railroad vehicle ID that the vehicle 20 is a green vehicle, the second calculation unit 37 calculates the Mahalanobis distance using the unit data corresponding to the green vehicle. The second calculation unit 37 may make the above comparison using different unit data depending on the traveling mode of the deadhead train, the ordinary train, the rapid train, the limited express train, and the like. According to this, the ride quality level can be appropriately determined according to the use of each vehicle 20 or the traveling mode of the railway vehicle 2.

The acceleration sensor 23 is arranged directly above the center plate of the railway vehicle 2. Therefore, the acceleration sensor 23 can improve the accuracy of the vibration generated in the railway vehicle 2.

When the comparison unit 38 determines that the Mahalanobis distance calculated by the second calculation unit 37 is equal to or greater than the threshold value, the communication unit 51 notifies the external vehicle base 70. Therefore, the amount of communication with the vehicle base 70 can be reduced.

The second calculation unit 37 calculates the Mahalanobis distance based on the characteristic data calculated from the detection signal data in the vertical direction of the railway vehicle 2. When it is determined that the ride quality is poor based on the detection signal data in the vertical direction, the factors that deteriorate the ride quality are considered to be track deviation on the track, puncture of the air spring, or failure of the shaft damper. Therefore, the second calculation unit 37 calculates the Mahalanobis distance based on the characteristic data calculated from the detection signal data in the vertical direction, so that the track is out of alignment on the track and the air spring is punctured or the shaft damper is damaged. The occurrence can be easily detected.

When the determination unit 39 intermittently determines that the ride quality is poor based on the detection signal data in the vertical direction, at least one of high-low deviation, flatness deviation, and level deviation occurs on the track. There is a high possibility that it is. When the determination unit 39 continuously determines that the ride quality is poor based on the detection signal data in the vertical direction, it is highly possible that a puncture of the air spring or a failure of the shaft damper has occurred.

The second calculation unit 37 calculates the Mahalanobis distance based on the characteristic data calculated from the detection signal data in the left-right direction of the railway vehicle 2. If it is determined that the ride quality is poor based on the detection signal data in the left-right direction, the factors that deteriorate the ride quality are considered to be track deviation on the track and failure of the left-right motion damper or yaw damper. Therefore, the second calculation unit 37 calculates the Mahalanobis distance based on the characteristic data calculated from the detection signal data in the left-right direction, thereby causing a track deviation on the track and a failure of the left-right moving damper or the yaw damper. It can be easily detected.

When the determination unit 39 intermittently determines that the ride quality is poor based on the detection signal data in the left-right direction, at least one of a deviation, a flatness deviation, and a level deviation occurs on the track. There is a high possibility that it is. When the determination unit 39 continuously determines that the ride quality is poor based on the detection signal data in the left-right direction, it is highly possible that the left-right motion damper or the yaw damper has failed.

The second calculation unit 37 calculates the Mahalanobis distance based on the characteristic data calculated from the detection signal data in the front-rear direction of the railway vehicle 2. If it is determined that the ride quality is poor based on the detection signal data in the front-rear direction, the factors that deteriorated the ride quality include track deviation on the track, abnormalities around the coupler, and failure of the yaw damper or inter-body damper. Conceivable. Therefore, the second calculation unit 37 calculates the Mahalanobis distance based on the characteristic data calculated from the detection signal data in the front-rear direction, so that the track is out of alignment on the track, an abnormality around the coupler, a yaw damper, or between the vehicle bodies. The occurrence of damper failure can be easily detected.

If the determination unit 39 intermittently determines that the ride quality is poor based on the detection signal data in the front-rear direction, there is a possibility that at least one of high and low deviations and deviations has occurred on the track. high. If the determination unit 39 continuously determines that the ride quality is poor based on the detection signal data in the front-rear direction, there is a possibility that an abnormality around the coupler or a failure of the yaw damper or the inter-vehicle body damper has occurred. high.

Although the preferred embodiment of the present invention has been described above, the present invention is not necessarily limited to the above-described embodiment, and various modifications can be made without departing from the gist thereof.

For example, the state calculation unit 32 and the unit space database 33 are also arranged in the relay unit 25, and the processing related to determining whether or not there is an abnormality in the state of the vehicle 20 is shared between the calculation unit 24 and the relay unit 25. You may do it.

1 ... Condition monitoring device, 2 ... Railway vehicle, 23 ... Accelerometer, 34, 51 ... Communication unit, 36 ... First calculation unit, 37 ... Second calculation unit, 38 ... Comparison unit, 39 ... Judgment unit.

Claims (10)

The accelerometer installed on the railroad vehicle and
A first calculation means for Fourier transforming the detection signal data from the acceleration sensor and calculating characteristic data related to acceleration for each frequency.
A second calculation means for calculating the Mahalanobis distance between the characteristic data calculated by the first calculation means and the unit data acquired in advance, and a second calculation means.
A comparison means for comparing the Mahalanobis distance with a preset threshold, and
A determination means for determining the ride quality level of the railway vehicle according to the comparison result in the comparison means is provided .
The unit data is characteristic data when a normal railroad vehicle travels on a normal railroad track, and is a frequency obtained by Fourier transforming each of a plurality of detection signal data output from the acceleration sensor at different times or different traveling sections. A state monitoring device that contains data on each acceleration . The state according to claim 1, wherein the first calculation means calculates the characteristic data by Fourier transforming each of the plurality of detection signal data output from the acceleration sensor at different times and then averaging them. Monitoring device. The condition monitoring device according to claim 1 or 2 , wherein the plurality of detection signal data output from the acceleration sensor includes the detection signal data output in the traveling sections overlapping at different times. The condition monitoring device according to any one of claims 1 to 3 , wherein at least one stop station is located between at least two of the different traveling sections. The condition monitoring device according to any one of claims 1 to 4 , wherein the different traveling sections include different traveling sections located between stations adjacent to each other. Further provided with an acquisition means for acquiring at least one of the vehicle type of the railway vehicle and the type of the traveling section in which the railway vehicle travels.
The comparison means according to any one of claims 1 to 5 , wherein the comparison means compares the threshold value according to the information acquired by the acquisition means with the distance of the Mahalanobis calculated by the second calculation means. The described condition monitoring device. Further provided with an acquisition means for acquiring at least one of the vehicle type of the railway vehicle and the type of the traveling section in which the railway vehicle travels.
The condition monitoring device according to any one of claims 1 to 6 , wherein the second calculation means calculates the distance of the Mahalanobis based on the unit data according to the information acquired by the acquisition means. .. The condition monitoring device according to any one of claims 1 to 7 , wherein the accelerometer is arranged directly above the center plate of the railway vehicle. Further equipped with communication means to communicate with external depots,
Any of claims 1 to 8 , wherein the communication means notifies the depot when the comparison means determines that the Mahalanobis distance calculated by the second calculation means is equal to or greater than the threshold value. The condition monitoring device described in item 1. The second calculation means includes the characteristic data calculated from the detection signal data in the vertical direction of the railroad vehicle, the characteristic data calculated from the detection signal data in the left-right direction of the railroad vehicle, and the railroad vehicle. The state monitoring device according to any one of claims 1 to 9 , which calculates the distance of the maharanobis based on each of the characteristic data calculated from the detection signal data in the front-rear direction.

Images