JP5382991B2 - Abnormality diagnosis method and abnormality diagnosis system for track system - Google Patents

Description

  The present invention relates to a low-cost abnormality diagnosis method and abnormality diagnosis system for a track facility or a vehicle facility in a track transportation system in which a vehicle travels on a defined track.

  In a track-type traffic system where a vehicle travels on a predetermined track, such as a conventional railroad, as a method for detecting and maintaining the abnormality of the track, various methods are used on the track during operation suspension hours of business vehicles such as at night. An inspection vehicle equipped with a measuring device is run to detect an abnormality in the track. For example, on the Shinkansen, an inspection vehicle called “Dr. Yellow” runs on the route about once every 10 days to inspect electric facilities and track facilities, but requires several operators.

  On some conventional lines, a track inspection vehicle, commonly called a Maya vehicle, runs to check the track equipment. Although this inspection vehicle is highly functional but expensive, it requires several operators to operate, and since it cannot be self-propelled, it can be inspected 2 to 6 times a year by organizing temporary trains. It is done.

  For example, Patent Document 1 (Japanese Patent Application Laid-Open No. 8-26106) discloses a train line inspection vehicle for inspecting a train line. This train line inspection vehicle is provided with a work table for train line inspection at the upper part of the vehicle body, the work table can be automatically raised and lowered following changes in the height of the train line, and the work table can be raised and lowered manually. It is unnecessary.

JP-A-8-26106

  Public transportation centered on railways will become more and more important from the viewpoint of reducing the global environmental burden and barrier-free. However, looking back at the actual railway, there are lines that are maintained by advanced equipment introduction and a sufficient maintenance system, such as the Shinkansen and some trunk lines, running the inspection vehicles mentioned above during the operation suspension time of business vehicles. On the other hand, the aging of facilities has progressed, and there are many line zones that are still in danger due to an increase in maintenance costs and management costs. The inspection vehicle is too expensive in the above-mentioned local line section and is difficult to introduce.

  In addition, maintenance work using inspection vehicles in overcrowded schedules is becoming more difficult year by year on Shinkansen and some trunk lines with good management conditions, and maintenance costs are increasing, and this solution is required. Yes.

  Therefore, the present invention has been made in view of such a background, and in a track-based traffic system in which a vehicle travels on a predetermined track, the burden of regular inspection by the inspection vehicle is reduced, and the vehicle is inexpensive for commercial vehicles. The purpose is to reduce maintenance costs and facilitate maintenance work by installing a measuring device and information processing equipment to travel along the route.

In order to solve the above problems, an abnormality diagnosis method for a track traffic system according to the present invention includes:
In a method for diagnosing an abnormality in a track facility or a vehicle facility of a track-type transportation system in which a vehicle travels on a predetermined track,
A first step of loading a business vehicle with a measurement device and capturing the position data of the business vehicle, time data, and measurement data representing a travel state of the business vehicle while the business vehicle is traveling on the track;
After removing noise of the measurement data, linking the measurement data with the time data and the position data, and obtaining a maximum value of the measurement data for each predetermined section of the trajectory;
Based on the determined maximum value, a section of the predetermined section that is likely to be abnormal is picked up, and the frequency distribution of the maximum value, the time-series change of the maximum value , or the maximum value for the picked-up section A third step for obtaining a difference with respect to the past measurement data history of
And a fourth step of diagnosing an abnormality in the track facility in the section from the value obtained in the third step.
Preferably, in the fourth step, an evaluation score corresponding to the possibility of occurrence of an abnormality is assigned to each value obtained in the third step, and the integrated value of the evaluation score exceeds a predetermined value. Diagnose that there is an abnormality in the track equipment in the section.

  In the abnormality diagnosis method of the present invention, a measuring device is mounted on a business vehicle, and while the business vehicle is traveling on a track, the position data of the business vehicle, time data, and measurement data representing the running state of the business vehicle By measuring the above and taking these data, the process from the second step to the fourth step is performed to diagnose the abnormality of the track facility. The measurement data representing the running state of the business vehicle includes, for example, acceleration data of vibration applied to the running wheel of the business vehicle, and sound pressure data of noise measured by a noise sensor provided inside or outside the business vehicle. Alternatively, there is measurement data obtained by measuring the power level obtained from the train line.

As means for obtaining the position data of the business vehicle, a means equipped with a GPS receiver that obtains position information by performing positioning by a satellite navigation system (Global Positioning System) or a wheel rotation sensor is provided to integrate the number of rotations of the wheel. The position information is obtained by receiving a signal from a means or a point detection sensor (ground element) installed at an interval on the track by a point detection sensor (vehicle element) disposed in the vicinity of the wheel of the sales vehicle. There is a means.
These position information detection means need to be used properly according to the situation. For example, there is a possibility that there is no point detection facility in a local railway, and a GPS signal cannot be used in a subway or the like.

  As a second step of the method of the present invention, after removing noise of the measurement data, the measurement data is linked with time data and position data, and the maximum value of the measurement data is obtained for each predetermined section of the route. Noise removal can be performed by moving average processing, filter processing, or the like. In addition, the position information of the business vehicle is obtained by correcting the position information calculated from the integrated value of the number of rotations measured by the wheel rotation sensor with a satellite navigation system at a predetermined specific point, thereby obtaining accurate vehicle position information. Can do.

  Thereafter, the measurement data is linked with time information and position information, and the maximum value of the measurement data is obtained for each predetermined section of the route. The reason for obtaining the maximum value of the measurement data is that the situation of the track facility or the vehicle facility is closely related to the maximum value. For example, the vibration waveform of a wheel or the sound pressure waveform of noise generated in a vehicle has a correlation with the wear of rails constituting the track, the step at the rail joint, or the track displacement (for example, the displacement of the width between tracks). It can be said that the larger the waveform of vibration or noise generated by the vehicle, the greater the possibility that the track facility is in an abnormal state.

Therefore, in the second step of the method of the present invention, the maximum value of the measurement data is obtained for each predetermined section of the trajectory. In the second step, when the measurement data exhibits a high-frequency waveform, for example, when the frequency is 100 Hz or higher, the maximum value cannot be identified. The maximum value for each section should be obtained from
In a third step, a frequency distribution of the maximum value, a time-series change of the maximum value, or a difference of specific measurement data with respect to past measurement data history is obtained for each predetermined section. In the method of the present invention, at least one of these processes is performed. In the fourth step, the abnormality of the track facility in the section is diagnosed from the value obtained in the third step.

  In the diagnosis method of the fourth step, for example, an evaluation point is assigned to each measurement data from the value obtained in the third step, and the abnormality of the track facility for each predetermined section is determined from the integrated value of the evaluation point. Good. In the third step, when obtaining the time-series change of the maximum value or the past measurement data history of the specific measurement data, an approximate line obtained from the measurement data by the least square method may be used. An abnormal state of the track facility can be diagnosed by finding a time-series change of the maximum value from the inclination of the approximate line or by obtaining a difference of the current measurement data with respect to the approximate line.

  In the method of the present invention, enormous amounts of measurement data can be efficiently obtained by mounting measurement devices on a plurality of business vehicles and running them daily. By performing analysis processing using this enormous amount of measurement data, it is possible to accurately diagnose the track facility.

  Further, in the method of the present invention, it is possible to diagnose an abnormality in the vehicle equipment of a specific business vehicle by moving a plurality of business vehicles on the same track, capturing measurement data, and comparing the measurement data captured by each business vehicle. it can. In other words, when measurement data is obtained from multiple sales vehicles in the same section, if the measurement data of a specific sales vehicle is different from the measurement data of other sales vehicles, the vehicle equipment of the specific sales vehicle may be abnormal There is sex. From this result, an abnormal portion of the business vehicle can be found by inspecting the specific business vehicle.

The abnormality diagnosis system of the present invention
In a system for diagnosing an abnormality in a track facility or a vehicle facility of a track-type transportation system in which a vehicle travels on a specified track,
A measuring device that is attached to a business vehicle traveling on the track and that measures position data of the business vehicle, time data, and measurement data representing a travel state of the business vehicle;
A maximum value is obtained for each predetermined section of the trajectory from the measurement data measured by the measuring device, a section in which the abnormality is a concern among the predetermined sections is picked up based on the determined maximum value, and the picked section A frequency distribution of the maximum value, a time series change of the maximum value, and a difference with respect to the past measurement data history of the specific measurement data that is the maximum value of the measurement data are obtained, and a trajectory is obtained for each predetermined section from the obtained value. A determination device provided with a means for determining an abnormality of the facility;
It is equipped with.

In such a configuration, the determination device includes:
Filter means for removing noise in the measurement data;
First calculation means for linking the measurement data with the time data and position data to obtain a maximum value of the measurement data for each predetermined section of the trajectory;
A second calculation means for obtaining a frequency distribution for each section of the maximum value, a time-series change for each section of the maximum value, or a difference with respect to past measurement data history of specific measurement data;
Means for assigning an evaluation score to each measurement data from the value obtained by the second calculation means, and determining that there is an abnormality in the track facility for a section in which the integrated value of the evaluation points exceeds a predetermined value. It can be.

  In such a configuration, the measurement data linked to the time data and the position data is stored in the database and managed centrally, so that it is easy for the maintenance company to conduct a detailed examination, and preventive maintenance of the track facility can be facilitated.

  Further, in the abnormality diagnosis system of the present invention, a plurality of business vehicles equipped with the measuring device are run on the same track, and the database of each business vehicle is installed in each business vehicle or provided collectively on the ground, It is possible to provide a vehicle abnormality determination means capable of comparing and calculating the measurement data of each business vehicle stored in the database to determine the abnormality of the vehicle equipment of the business vehicle. By comparing and calculating the measurement data of the individual business vehicles by the vehicle abnormality determination means, it is possible to diagnose an abnormality in the vehicle equipment of the business vehicle as well as the track equipment.

  According to the method of the present invention, since the measuring device is mounted on the business vehicle and the measurement data representing the running state of the business vehicle is captured while the business vehicle is traveling on the route, the burden of periodic inspection by the inspection vehicle is reduced. The cost required for the inspection can be reduced and the measurement data can be captured during the commercial operation, so that no special time is required for the measurement. In addition, an enormous amount of measurement data can be captured during daily travel of a business vehicle equipped with a measuring device, and an abnormality diagnosis is performed based on the enormous amount of measurement data, so that an accurate diagnosis can be performed.

  Also, by making daily measurements with a sales vehicle equipped with a measuring device, it is possible to constantly grasp the state of the track equipment, reduce maintenance work, and easily perform data analysis, thus reducing work costs. . In addition, since daily measurement is performed by the business vehicle, even if any abnormality occurs, the situation can be easily grasped, the cause can be easily clarified, and quick troubleshooting can be performed.

  In addition, according to the abnormality diagnosis system of the present invention, in addition to the operational effects obtained by the method of the present invention, a database for accumulating measurement data is provided, so that the accumulated measurement data can be managed in an integrated manner, and therefore, the maintenance operator can carefully examine it. And preventive maintenance of track equipment becomes easy.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the drawings. However, the dimensions, materials, shapes, relative arrangements, and the like of the components described in this embodiment are not intended to limit the scope of the present invention unless otherwise specified.
(Embodiment 1)

  A first embodiment of the present invention will be described with reference to FIGS. The first embodiment is an embodiment in the case of diagnosing the presence or absence of wear and scratches on the rails constituting the track, steps at the rail joints, or other abnormal track displacement (for example, an abnormality in the distance between tracks). FIG. 1 is a block diagram of a business vehicle equipped with various measuring devices in the first embodiment, FIG. 2 is a block diagram of the abnormality diagnosis device of the first embodiment, and FIG. 3 is a flowchart of the abnormality diagnosis method of the first embodiment. It is.

  In FIG. 1, a business vehicle 1 travels on a rail 2, and various measurement devices are mounted on the business vehicle 1. That is, a GPS receiver 4 that receives a GPS signal (own vehicle position coordinates) from a GPS (Global Positioning Satellite) satellite, the rotational speed of the wheel 3 attached to the wheel 3 is measured, and the own vehicle position is calculated from the integrated value of the rotational speed. A wheel rotation sensor 5 or a point detection sensor (vehicle upper member) 7 for receiving a signal from a plurality of point detection sensors (ground child) 6 arranged at appropriate intervals along the rail 2 and measuring the position of the vehicle is attached. . These three types of measuring devices are properly used depending on the situation, and at least one type of measuring device may be provided.

  In addition, an acceleration sensor 8 that measures the acceleration of vibrations in the traveling business vehicle, an acceleration sensor 9 that measures the acceleration of vibrations of the running wheels 3, and a noise sensor 10 that measures noise in the vehicle are provided. . Since the acceleration sensor 8 is disposed inside the business vehicle, the vibration of the wheel 3 is transmitted through a shock absorber interposed between the wheel 3 and the vehicle body. Therefore, there is a problem that the minute vibration component among the vibration components of the wheel 3 cannot be accurately measured.

On the other hand, since the acceleration sensor 9 is directly attached to the wheel 2, there is an advantage that the vibration of the wheel 3 can be directly measured including a slight vibration component. However, a current generated by the drive motor flows through the wheel 3, and noise due to the current is transmitted to the acceleration sensor 9.
Therefore, both the acceleration sensors 8 and 9 are provided, and accurate acceleration data can be obtained by combining measurement data from both acceleration sensors.

  The own vehicle position data and measurement data are stored in a database 11 mounted in the vehicle 1. A business vehicle (hereinafter referred to as “probe vehicle”) 1 equipped with such various measuring devices travels daily with a business vehicle not equipped with a measuring device, and accumulates measurement data measured by the measuring device in the database 11. .

  In FIG. 2, noise is removed from the measurement data stored in the in-vehicle database 11 by a moving average process, a filter process, and the like by the filter device 12. Next, the first arithmetic unit 13 links the measurement data with the time data and the vehicle position data, and obtains the maximum value for each predetermined section of the rail 2. Next, the second arithmetic unit 14 creates a frequency distribution map of the maximum value for each predetermined section, creates an approximate line obtained from the maximum value by the least square method with the time axis as the horizontal axis, and specifies The difference between the maximum value and the past maximum value history is obtained.

Next, the trajectory abnormality determination device 15 diagnoses the presence / absence of abnormality for each predetermined section of the trajectory from the frequency distribution diagram, the approximate line, and the difference obtained by the second arithmetic device 14.
Next, the procedure of the abnormality diagnosis method according to the present embodiment will be described with reference to the flowchart of FIG. In FIG. 3, first, the traveling of the probe vehicle 1 is started, and measurement data measured by various measurement devices mounted on the probe vehicle 1 is taken into the in-vehicle database 11.

  Next, data processing at stage 1 is performed. That is, the filter device 12 in FIG. 2 performs a filtering process such as a conventionally known moving average process to remove noise from the measurement data. Next, the vehicle position data is corrected. That is, the vehicle position is confirmed by the vehicle position data measured by the GPS receiver 4, the wheel rotation sensor 5 or the point detection sensors 6, 7, but the position calculated from the integrated value of the rotation speed measured by the wheel rotation sensor 5. By correcting the data with the position information of the GPS receiver 4 targeting a specific point determined in advance, accurate vehicle position data can be obtained.

  Next, the acceleration data of vibration measured by the acceleration sensors 8 and 9 arranged inside and outside the probe vehicle 1 and the maximum value of the noise data measured by the noise sensor 10 are set for each section (for example, 50 m / 100 m / 500 m section). Asking) When the wavy wear of the rail, for example, a wavy flaw (unevenness) having a length of 5 to 10 cm is measured as a vibration waveform or a noise waveform applied to the wheel or the vehicle by the acceleration sensor 8, 9 or the noise sensor 10, a high-frequency waveform of 100 Hz or more. It becomes. The step at the rail joint is measured as a single vibration waveform of about 10 to 20 Hz. Abnormal orbital displacement (for example, an abnormality in the interval between rails) is measured as long-wave vibration of 1 to 10 Hz. In the present embodiment, the presence or absence of abnormality in the track facility is diagnosed by measuring the acceleration of vibration or the sound pressure of noise having a unique correlation with the vibration waveform or noise waveform.

  When the measured waveform data exhibits a high-frequency waveform of 100 Hz or higher, it is difficult to identify the maximum value, and therefore, a Fourier transform is performed to find a significant value. FIG. 4 shows an example of a diagram in which the maximum value for each section obtained from this remarkable value is plotted.

  The reason for obtaining the maximum value of the vibration waveform or noise waveform will be described below. That is, the maximum value is based on the knowledge that rail wear, the occurrence of a step at the rail joint, and the like are highly correlated with the maximum value. That is, it can be said that the higher the maximum value, the higher the possibility that these abnormalities have occurred. Therefore, paying attention to the maximum value and analyzing the maximum value makes it possible to accurately diagnose an abnormality in the orbital state.

  Next, data sorting at stage 2 is performed. In stage 2, the maximum values of vibration acceleration data and noise sound pressure data are sorted in descending order. In this case, for example, it is performed every time the probe vehicle 1 goes around the abnormal track once. As an example, FIG. 5 shows a diagram in which the maximum values of acceleration data are sorted in order of size. In addition, a section having a large maximum value is picked up as a point of concern or integrated as data for each section. A table in the case of integration as acceleration data for each specific section is shown as an example in FIGS. 6A and 6B.

  Next, data analysis is performed in stage 3. In this case, for example, a location where there is a concern about abnormality due to a large maximum value is picked up, a frequency distribution diagram at that location is created, and the state of the frequency distribution is compared with a standard deviation (statistical processing). An example of the frequency distribution chart in this case is shown in FIG. For example, when the frequency distribution state in a specific section is larger than the standard deviation, it indicates that there is a possibility that an abnormality has occurred in that section.

  The current travel data is compared with the past travel data history, and the difference is obtained. An example of this case is shown in FIG. If the difference is large, there is a possibility that an abnormality has occurred. Further, the data of a certain section are integrated, an approximate line is obtained from the integrated data by the least square method, the tendency (slope) of the approximate line is seen, and the difference between the approximate line and the current travel data is seen. An example of this is shown in FIG. When the approximate line gradually increases as shown in FIG. 9, it indicates that an abnormality is occurring in the section. If the difference between the current travel data and the approximate line is large, it indicates that there is a possibility that an abnormality has occurred in the section.

  Next, at stage 4, the presence or absence of abnormality in the track facility is diagnosed from the analysis results up to stage 3. That is, an evaluation point is attached from the result of the statistical processing, difference, and approximate line performed in stage 3. That is, a high evaluation score is assigned to the maximum value of each measurement data in descending order of possibility of abnormality. Then, the evaluation based on the evaluation point integrated value (in this case, the history of the previous integrated value and the like is collated), the evaluation point is sorted in order, and the evaluation information is sorted. From these evaluation methods, it is determined whether or not there is an abnormality in the track facility. An example of this is shown in FIG.

In FIG. 10, for example, evaluation points of +1, +2, and +3 are assigned to the maximum value of each measurement data in order from the lowest possibility of occurrence of abnormality to the highest. If the evaluation score integrated value exceeds +8, an abnormality is diagnosed. When the evaluation point integrated value exceeds +6, the next run data is confirmed by looking at the situation. When the evaluation score is +3, it is determined that the abnormality is suddenly detected and the traveling data is confirmed. If any one of the evaluations based on the frequency distribution, the difference, or the approximate line is +3, the overall evaluation is +3.
And when it is diagnosed as abnormal, it discusses whether there is any problem with the operation regarding the abnormality detection result, or considers or plans maintenance work.

  In the diagnosis method shown in FIG. 3, taking into account performance variations for each probe vehicle 1, variations in travel speed, changes in travel environment (rain, wind, temperature, humidity), online / offline processing classification, and the like. Not.

  According to the present embodiment, since an inexpensive measuring device is mounted on the business vehicle 1 and the measurement data representing the running state of the business vehicle is captured while the business vehicle is traveling on the track, the periodic inspection by the inspection vehicle is performed. It reduces the burden, reduces the cost required for periodic inspections, and measures the running state while the business vehicle 1 is running, so it does not require special time for measurement, and enormous amounts of measurement data Accumulation can be easily performed, and analysis is performed based on this enormous amount of measurement data, so that it is possible to diagnose the presence or absence of abnormality with high predictability.

  As a result, maintenance work can be reduced, and measurement data can be easily analyzed, so that work costs can be suppressed. In addition, by centrally managing the measurement data with the in-vehicle database 11, it is easy for the maintenance company to conduct a detailed examination and preventive maintenance can be facilitated. In addition, by accumulating the measurement data every day, even if an abnormality occurs in the track facility, the situation can be easily judged, so that quick troubleshooting can be performed.

In the first embodiment, the vibration or noise of the vehicle is measured to diagnose the presence or absence of the rail 2, but in addition to this, in the ATS system, the signal level of the point detection sensor (ground element) 6 is set. By measuring, it can be diagnosed whether the ground child 6 is abnormal. Further, by measuring the level of the current flowing in the track circuit, it is possible to diagnose whether or not the track circuit is abnormal. Further, by measuring the power level of the power supplied from the power line, it is possible to diagnose an abnormality from the load state applied to the power equipment.
(Embodiment 2)

  Next, a second embodiment of the present invention will be described with reference to FIG. In FIG. 11, in this embodiment, a plurality of probe vehicles A, B, and C are caused to travel on the same track, and the traveling state is measured by each probe vehicle. Measurement data measured by the probe vehicles A, B, and C is transmitted to the base station database 24 installed on the ground and stored in the database 24. Using the measurement data stored in the base station database 24, the same processing as in the first embodiment is performed from the filter device 12 to the processing line of the orbit abnormality determination device 15.

  As shown in FIG. 11, the database of measurement data measured by each probe vehicle is preferably constructed as an in-vehicle database for each probe vehicle 1, and the measurement data is transmitted from the database to the base station database 24. The database may be either mounted on the probe vehicle or provided on the ground base. The transmission of measurement data from each probe vehicle to the base station database 24 is preferably performed at each orbit of the track or when the vehicle stops at the vehicle base.

  According to the present embodiment, in addition to the operational effects obtained in the first embodiment, since measurement data constructed by a plurality of probe vehicles can be shared, the abnormal condition of the track facility can be diagnosed efficiently in a short time. Can do. Furthermore, the measurement data of the individual probe vehicles can be sent from the base station database 24 to the vehicle abnormality determination device 25 and compared to diagnose the abnormality of the vehicle equipment on the probe vehicle side.

  For example, if the diagnosis based on the measurement data of the probe vehicles A, B, and C both diagnoses an abnormality in a certain track section, it is understood that the track equipment is abnormal, but in the probe vehicle C compared to the probe vehicles A and B, When measurement data indicating that the brake braking distance is long is obtained, it is understood that the brake device of the probe vehicle C may be abnormal.

  According to the present embodiment, since the plurality of probe vehicles A, B, and C measure the traveling state in parallel, the abnormal state of the track facility and the vehicle facility can be efficiently monitored in a short period of time. Further, by comparing the measurement data of the probe vehicles A, B, and C, it is possible to diagnose abnormality of the vehicle equipment.

  ADVANTAGE OF THE INVENTION According to this invention, in the track-type traffic system which drive | works the track on which the wheel was defined, the diagnostic method which can easily diagnose the abnormality presence or absence of track equipment or vehicle equipment with an inexpensive measuring device, and its diagnostic system are implement | achieved. be able to.

It is a block diagram of the business vehicle of a 1st embodiment of the present invention. It is a block diagram of the abnormality diagnosis apparatus of the first embodiment. It is a flowchart of the abnormality diagnosis method of the first embodiment. It is the figure which plotted the vibration waveform remarkable value in the said 1st Embodiment. It is the table | surface which sorted and showed the acceleration data in the said 1st Embodiment in order of a magnitude | size. It is the table | surface which integrated the acceleration data for every specific area in the said 1st Embodiment. It is a frequency distribution figure of the place where abnormality is worried in the first embodiment. It is the figure which compared the present driving data with the past driving data log | history in the said 1st Embodiment. It is the figure which calculated | required the approximate line by the least square method from the data integrated | accumulated in the said 1st Embodiment. It is the table | surface which added the evaluation score to the result analyzed in the said 1st Embodiment. It is a block diagram of a 2nd embodiment of the present invention.

Explanation of symbols

1 Probe vehicle (business vehicle)
2 rail (track)
4 GPS receiver 5 Wheel rotation sensor 6 Point detection sensor (ground unit)
7 point detection sensor (car upper)
8, 9 Acceleration sensor 10 Noise sensor 11 In-vehicle database 12 Filter device 13 First computing device (first computing means)
14 Second arithmetic unit (second arithmetic means)
15 Orbit abnormality determination device (abnormality diagnosis means)
21 Database 22 of probe vehicle A Database 23 of probe vehicle B Database 24 of probe vehicle C 24 Base station database 25 Vehicle abnormality determination device

Claims (9)

In a method for diagnosing an abnormality in a track facility or a vehicle facility of a track-type transportation system in which a vehicle travels on a predetermined track,
A first step of loading a business vehicle with a measurement device and capturing the position data of the business vehicle, time data, and measurement data representing a travel state of the business vehicle while the business vehicle is traveling on the track;
After removing noise of the measurement data, linking the measurement data with the time data and the position data, and obtaining a maximum value of the measurement data for each predetermined section of the trajectory;
Based on the determined maximum value, a section of the predetermined section that is likely to be abnormal is picked up, and the frequency distribution of the maximum value, the time-series change of the maximum value, or the maximum value for the picked-up section A third step for obtaining a difference with respect to the past measurement data history of
A fourth step of diagnosing an abnormality of the track facility in the section from the value obtained in the third step, and a method for diagnosing an abnormality in the track-type traffic system.   In the fourth step, an evaluation score corresponding to the possibility of occurrence of an abnormality is assigned to each value obtained in the third step, and the interval in which the integrated value of the evaluation score exceeds a predetermined value The abnormality diagnosis method for an orbital transportation system according to claim 1, wherein the abnormality is diagnosed in the orbital facility.   A specific sales vehicle can be obtained by running a plurality of sales vehicles on the same track and capturing measurement data and performing the processing from the first step to the third step or by comparing the measurement data captured by individual sales vehicles. An abnormality diagnosis method for a track-type traffic system according to claim 1, wherein an abnormality of the vehicle equipment is diagnosed.   The said 2nd step WHEREIN: When this measurement data exhibits a high frequency waveform, the waveform is Fourier-transformed and a remarkable value is calculated | required and the maximum value is calculated | required from this remarkable value. Abnormality diagnosis method for track traffic system in Japan.   The measurement device according to claim 1, wherein the measurement device is an acceleration sensor and a noise sensor for detecting vibration of a traveling wheel, and the measurement data is measurement data measured by the acceleration sensor and the noise sensor. Abnormality diagnosis method for track system. A time series change of the maximum value or a past measurement data history of the specific measurement data which is the maximum value of the measurement data is used as an approximate line obtained from the measurement data by a least square method. The abnormality diagnosis method for a track-type traffic system according to claim 1. In a system for diagnosing an abnormality in a track facility or a vehicle facility of a track-type transportation system in which a vehicle travels on a specified track,
A measuring device that is attached to a business vehicle traveling on the track and that measures position data of the business vehicle, time data, and measurement data representing a travel state of the business vehicle;
A maximum value is obtained for each predetermined section of the trajectory from the measurement data measured by the measuring device, a section in which the abnormality is a concern among the predetermined sections is picked up based on the determined maximum value, and the picked section A frequency distribution of the maximum value, a time series change of the maximum value, and a difference with respect to the past measurement data history of the specific measurement data that is the maximum value of the measurement data are obtained, and a trajectory is obtained for each predetermined section from the obtained value. a determination unit having means for determine a constant abnormal equipment,
An abnormality diagnosis system for an orbital transportation system, characterized by comprising: The determination device is
Filter means for removing noise in the measurement data;
First calculation means for linking the measurement data with the time data and position data to obtain a maximum value of the measurement data for each predetermined section of the trajectory;
A second calculation means for obtaining a frequency distribution for each section of the maximum value, a time-series change for each section of the maximum value, or a difference of the maximum value with respect to past measurement data history;
Means for assigning an evaluation score to each measurement data from the value obtained by the second calculation means, and determining that there is an abnormality in the track facility for a section in which the integrated value of the evaluation points exceeds a predetermined value. The abnormality diagnosis system for an orbital traffic system according to claim 7.   A plurality of business vehicles equipped with the measuring device are caused to travel on the same track, and the database of each business vehicle is mounted on each business vehicle or provided collectively on the ground, and each business vehicle stored in the database is stored. 8. An abnormality diagnosis system for a track-type traffic system according to claim 7, further comprising vehicle abnormality determination means capable of comparing and calculating the measurement data of the vehicle to determine abnormality of the vehicle equipment of the business vehicle.

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