JP7397704B2 - Diagnostic equipment, systems, control methods and programs - Google Patents

Description

The present invention relates to a diagnostic device, a system, a control method, and a program.

BACKGROUND ART When a moving body moves on a roadbed, abnormalities in the roadbed are diagnosed using data measured via sensors such as acceleration sensors installed on the moving body.
Patent Document 1 discloses a method for diagnosing an abnormality in track equipment or vehicle equipment of a track-based transportation system in which vehicles run on predetermined tracks.
Moreover, Patent Document 2 discloses a system that grasps the state of a track on which a vehicle moves from vibrations of the vehicle.

Japanese Patent Application Publication No. 2008-148466 Japanese Patent Application Publication No. 2005-231427

Depending on the moving object, the speed may be limited. As the speed becomes lower, data obtained through sensors installed on the moving body may become weaker. In such a case, there is a problem in that only weak data can be obtained through the sensor, making it impossible to diagnose abnormalities in the roadbed on which the mobile object moves.
Therefore, an object of the present invention is to more accurately diagnose abnormalities in the roadbed using signals from sensors installed on moving bodies whose speed is limited.

The diagnostic device of the present invention provides a plurality of physical quantities related to a plurality of physical quantities corresponding to a location on the roadbed determined based on sensor data measured by a sensor installed on the moving body when the moving body moves on the roadbed. data regarding the acceleration in a plurality of different directions applied to the moving body, each data regarding the angular velocity of the moving body in a plurality of different rotational directions, and data on the angular velocity of the moving body in a plurality of different rotational directions. an acquisition means for acquiring the plurality of data including at least two or more of the data regarding the inclination angle, and among the plurality of data acquired by the acquisition means, for each of the plurality of data; Whether or not there are two or more predetermined numbers of data that are equal to or greater than a first set threshold , and a threshold set for each of the plurality of data, and for each of the plurality of data. An abnormality is detected at the location on the roadbed based on whether there is one or more data among the plurality of data that is equal to or higher than a second threshold value that is larger than the first threshold value that has been set. and diagnostic means for diagnosing whether or not the present invention is present.

According to the present invention, abnormalities in the roadbed can be diagnosed with higher accuracy using signals from sensors installed on moving bodies whose speed is limited.

FIG. 1 is a diagram showing an example of the system configuration of a diagnostic system. FIG. 2 is a diagram showing an example of the hardware configuration of the diagnostic device. FIG. 3 is a diagram showing an example of the functional configuration of the diagnostic device. FIG. 4 is a flowchart illustrating an example of sensor data collection processing. FIG. 5 is a flowchart illustrating an example of data acquisition processing. FIG. 6 is a flowchart illustrating an example of diagnostic processing. FIG. 7 is a diagram explaining the experimental situation. FIG. 8 is a diagram showing data obtained in an experiment. FIG. 9 is a diagram showing data obtained in an experiment. FIG. 10 is a diagram showing data obtained in an experiment. FIG. 11 is a diagram illustrating the experimental results.

<Embodiment>
Hereinafter, one embodiment of the present invention will be described based on the drawings.

(Diagnostic system)
FIG. 1 is a diagram showing an example of the system configuration of a diagnostic system 100 of this embodiment.
The diagnostic system 100 is installed in a moving body 110 and is a system that diagnoses whether or not there is an abnormality in the roadbed on which the moving body 110 moves. An abnormality in the roadbed is any deviation from a predetermined normal state that occurs in the roadbed. In this embodiment, roadbed abnormalities include not only serious ones such as rail damage and rail rupture, but also minor ones such as rail wear and abrasions. The moving body 110 is a railway vehicle that moves on a track along a rail installed on a roadbed. However, as another example, the moving object 110 may be another moving object such as a vehicle that moves on a road or an overhead crane that moves on a rail. In the present embodiment, the moving object 110 moves along the rail in a speed range defined as low speed (for example, less than 20 km/h, 10 km/h or less, etc.), and cannot move at a speed higher than this speed range. Can not.
The diagnostic system 100 includes a diagnostic device 101, an acceleration sensor 102, an angular velocity sensor 103, a track sensor 104, and a positioning meter 105.

The diagnostic device 101 is an information processing device that diagnoses whether or not there is an abnormality in the roadbed based on data measured using at least one of the acceleration sensor 102, the angular velocity sensor 103, and the track sensor 104. The diagnostic device 101 is, for example, a personal computer (PC), a computer built into a mobile object, a server device, a tablet device, or the like.
Acceleration sensor 102 is a sensor used to measure acceleration applied to moving body 110. The angular velocity sensor 103 is a sensor used to measure the angular velocity of the moving body 110. The acceleration sensor 102 is arranged within a predetermined range from the pair of wheels (for example, within a predetermined range of (within a predetermined distance range of less than a predetermined threshold value). Hereinafter, this predetermined pair of wheels will be referred to as a reference wheel pair.
In addition, the angular velocity sensor 103 is configured to operate within a predetermined range from the reference wheel pair (for example, from a midpoint position between the pair of wheels) so as to appropriately detect the angular velocity caused by contact between the reference wheel pair and the rail. is installed at a position within a distance range (within a distance below the specified threshold).

The gauge sensor 104 is a sensor that includes two two-dimensional laser measuring meters and is used to measure the gauge, which is the distance between two rails on the track. At the measurement position, each of the two two-dimensional laser measuring meters of the track sensor 104 measures the ends of each of the two rails of the track in the left-right direction when the traveling direction of the moving body 110 is the forward direction and the direction of gravity is the downward direction. Measure the position of. Then, the gauge sensor 104 measures the gauge at the measurement position based on the positions measured by each of the two two-dimensional laser measuring meters. Each of the two two-dimensional laser measuring meters of the track sensor 104 measures the rail at a position near the position where the reference wheel pair on the track contacts (for example, a position a predetermined distance forward from the contact position). It is installed at the bottom of the housing of the moving body 110 so that the position can be measured. In this embodiment, it is assumed that the gauge measured by the gauge sensor 104 is the gauge at the position where the reference wheel pair contacts on the track.
In the following, the traveling direction of the moving body 110 will be referred to as the forward direction. Further, in the following, the direction of gravity is assumed to be downward.
The positioning meter 105 is a measuring instrument that measures the position of the moving body 110, including an RFID tag reader that reads an RFID tag installed at a predetermined position on the roadbed, and a laser Doppler speed meter. The positioning meter 105 determines the position of the mobile object 110 by reading the signal from the RFID tag via the RFID tag reader. Furthermore, the positioning meter 105 determines the speed of the moving object 110 via a laser Doppler velocimeter. In this embodiment, the positioning meter 105 measures the position of the reference wheel pair on the roadbed (for example, a position 100 m from the starting point of the moving body 110) as the position of the moving body 110. However, the positioning device 105 may determine the position of the moving object 110 using other methods. For example, the positioning device 105 may use GPS to determine the position of the mobile object 110. Furthermore, the positioning meter 105 may determine the position of the moving object 110 using a laser distance meter. Furthermore, the positioning device 105 may determine the position of the moving object 110 based on a vehicle speed signal from a speedometer installed in the moving object 110.

(Details of diagnostic equipment)
FIG. 2 is a diagram showing an example of the hardware configuration of the diagnostic device 101. The diagnostic device 101 includes a CPU 201 , a main storage device 202 , an auxiliary storage device 203 , a device I/F 204 , an input section 205 , and an output section 206 . Each element is communicably connected to each other via a system bus 207.
CPU 201 is a central processing unit that controls diagnostic device 101 . The main storage device 202 is a storage device such as a random access memory (RAM) that functions as a work area for the CPU 201 and a temporary storage area for data. The auxiliary storage device 203 is a storage device that stores various programs, various setting information, various measurement data, and the like. The auxiliary storage device 203 is a read only memory (ROM), a hard disk drive (HDD), a solid state drive (SSD), or the like.

The device I/F 204 is an interface used for connection with external devices such as the acceleration sensor 102, the angular velocity sensor 103, the track sensor 104, and the positioning meter 105. The CPU 201 inputs and outputs information to and from the acceleration sensor 102, the angular velocity sensor 103, the track sensor 104, and the positioning meter 105 via the device I/F 204.
The input unit 205 is an input device such as a mouse, a keyboard, a touch panel, a dedicated controller, and a microphone. The output unit 206 is an output device such as a monitor, a speaker, a touch panel display, or the like.
By the CPU 201 executing processes according to programs stored in the auxiliary storage device 203 and the like, functions of the diagnostic apparatus 101 described later in FIG. 3, processes in flowcharts described later in FIGS. 4 to 6, etc. are realized.

FIG. 3 is a diagram showing an example of the functional configuration of the diagnostic device 101. The diagnostic device 101 includes an acquisition section 301 and a diagnosis section 302.
The acquisition unit 301 acquires predetermined types of data via the acceleration sensor 102, the angular velocity sensor 103, and the track sensor 104. In this embodiment, the acquisition unit 301 acquires data regarding some physical quantity, which is determined based on a signal output from at least one of measuring instruments such as the acceleration sensor 102, the angular velocity sensor 103, and the track sensor 104.
The diagnosis unit 302 diagnoses whether or not an abnormality exists at a location set on the roadbed based on the data acquired by the acquisition unit 301.

(Processing of diagnostic system 100)
The sensor data collection process executed by the diagnostic system 100 will be described using FIG. 4.
In this embodiment, the diagnostic system 100 starts the process shown in FIG. 4 in conjunction with the mobile object 110 starting to move from the starting point on the roadbed. In the following, data output from a sensor will be referred to as sensor data.
In S<b>401 , the acquisition unit 301 acquires the position of the moving body 110 (the position of the reference wheel pair) and the speed of the moving body 110 at the time of processing via the positioning device 105 .
In S402, the acquisition unit 301 acquires sensor data indicating acceleration in a plurality of directions output from the acceleration sensor 102. In the present embodiment, the acquisition unit 301 acquires three pieces of sensor data each representing acceleration in the front-rear direction, acceleration in the vertical direction, and acceleration in the left-right direction, as sensor data representing acceleration in the plurality of directions.

In S403, the acquisition unit 301 acquires sensor data indicating angular velocities in a plurality of rotational directions output from the angular velocity sensor 103. In the present embodiment, the acquisition unit 301 acquires three pieces of sensor data each representing an angular velocity in a roll direction, an angular velocity in a pitch direction, and an angular velocity in a yaw direction, as sensor data representing angular velocities in the plurality of rotational directions.
The roll direction is the rotational direction of the axis passing through the angular velocity sensor 103 in the traveling direction (forward direction) of the moving body 110. The pitch direction is the rotation direction of the left-right axis of the moving body 110 through the angular velocity sensor 103. The yaw direction is the rotation direction of the vertical axis of the moving body 110 through the angular velocity sensor 103.
In the following, a concept combining the direction and the rotation direction will be referred to as a direction/rotation direction.
In S404, the acquisition unit 301 acquires sensor data output from the gauge sensor 104 and indicating the gauge, which is the width of the rail at the position where the reference wheel pair contacts the rail.

In S405, the acquisition unit 301 calculates the inclination angle of the moving body 110 in the roll direction at the position acquired in S401 by integrating (totaling) the values of the angular velocity sensor data acquired in S403 so far in the roll direction. demand. Further, the acquisition unit 301 corrects the obtained tilt angle in the roll direction using the value of the acceleration sensor data acquired so far in S402, and acquires the corrected tilt angle as the tilt angle in the roll direction of the moving body 110. do. In addition, the acquisition unit 301 calculates the inclination angle in the pitch direction of the moving body 110 at the position acquired in S401 by integrating (totaling) the values of the angular velocity sensor data acquired in S403 so far in the pitch direction. . Further, the acquisition unit 301 corrects the obtained tilt angle in the pitch direction using the value of the acceleration sensor data acquired so far in S402, and acquires the corrected tilt angle as the tilt angle in the pitch direction of the moving body 110. do. However, as another example, the acquisition unit 301 may directly acquire data on the tilt angle using a sensor that can directly detect the tilt angle.
In S406, the acquisition unit 301 associates the speed acquired in S401, the sensor data acquired in S402 to S404, and the tilt angle data acquired in S405 with the position acquired in S401, and stores them in the auxiliary storage device. 203.
In S407, the acquisition unit 301 waits for a predetermined period. In this embodiment, the acquisition unit 301 waits for 2 msec. As a result, the acquisition unit 301 acquires each data in S401 to S405 every waiting period (2 msec) in S407. However, the acquisition unit 301 may stand by for an arbitrary period of time such as 1 msec, 5 msec, 1 sec, etc. in S407.

In S408, the acquisition unit 301 determines whether the movement of the mobile object 110 is completed. In this embodiment, the acquisition unit 301 determines that the movement of the mobile body 110 has been completed when the position of the mobile body 110 acquired in the most recent process of S401 is a point after the goal point on the roadbed. Furthermore, when the position of the moving object 110 obtained in the most recent process of S401 is not a point after the goal point on the roadbed, the acquisition unit 301 determines that the movement of the moving object 110 is not completed.
If the acquisition unit 301 determines that the movement of the mobile body 110 is completed, it ends the process of FIG. 4, and if it determines that the movement of the mobile body 110 is not completed, it advances the process to S401.
Through the process shown in FIG. 4 described above, the diagnostic system 100 can acquire each data every 2 msec when the moving body 110 moves on the roadbed. In the following, the data group stored in the auxiliary storage device 203 in association with the position on the roadbed through the process shown in FIG. 4 will be referred to as a raw data group.

The data acquisition process executed by the diagnostic system 100 will be explained using FIG. 5.
In S501, the acquisition unit 301 divides each data of the raw data group stored in the process of FIG. 4 into data groups corresponding to each of a plurality of predetermined locations on the roadbed. In this embodiment, a plurality of locations are set on the roadbed, separated by predetermined intervals (for example, 1 m, 25 cm, etc.) from the start point to the goal point along the rail. That is, the acquisition unit 301 sorts each piece of data in the raw data group as data corresponding to a location including a position on the roadbed corresponding to each piece of data.
In S502, the acquisition unit 301 acquires data regarding the acceleration applied to the moving body 110 at each of a plurality of locations set on the roadbed. More specifically, the acquisition unit 301 performs the following processing for each of the plurality of locations.
That is, the acquisition unit 301 acquires sensor data of acceleration in each direction (front-back direction, up-down direction, left-right direction) output from the acceleration sensor 102 from the data group sorted as data corresponding to the location in S501. Then, the acquisition unit 301 calculates the root mean square of the sensor data of the acceleration corresponding to that location (the average value of the squares of each sensor data) for each direction, and uses the calculated value for each direction applied to the moving body 110 at that location. The data is related to the acceleration in the direction. By taking the root mean square, the acquisition unit 301 can acquire more emphasized data regarding acceleration even when the moving object 110 moves in a low speed zone and only weak sensor data can be acquired. However, as another example, the acquisition unit 301 may obtain, for each direction, the average value, maximum value, minimum value, amplitude, average absolute value, etc. of acceleration sensor data corresponding to that location, and data regarding acceleration in each direction. You can also obtain it as Further, the acquisition unit 301 may acquire a plurality of data among these data and data of the root mean square of acceleration sensor data corresponding to the location, respectively, as data related to acceleration.
Thereby, the acquisition unit 301 can acquire data regarding the acceleration in each direction applied to the moving body 110 for each of a plurality of locations on the roadbed.

In S503, the acquisition unit 301 acquires data regarding the angular velocity of the moving body 110 at each of a plurality of locations set on the roadbed. More specifically, the acquisition unit 301 performs the following processing for each of the plurality of locations.
That is, the acquisition unit 301 acquires sensor data of angular velocity in each rotational direction (roll direction, pitch direction, yaw direction) output from the angular velocity sensor 103 from the data group distributed as data corresponding to the location in S501. . Then, the acquisition unit 301 calculates the root mean square of the sensor data corresponding to that location (the average value of the squares of each sensor data) for each rotation direction, and uses the obtained value for each rotation direction of the moving body 110 at that location. Let the data be about the angular velocity of However, as another example, the acquisition unit 301 may obtain the average value, maximum value, minimum value, amplitude, average absolute value, etc. of the sensor data of the angular velocity corresponding to the location for each rotation direction, and obtain the angular velocity of each rotation direction. It may also be acquired as data related to. Further, the acquisition unit 301 may acquire a plurality of data out of these data and data of the root mean square of the sensor data of the angular velocity corresponding to the location, respectively, as data regarding the angular velocity.
Thereby, the acquisition unit 301 can acquire data regarding the angular velocity of the moving body 110 in each rotational direction for each of a plurality of locations on the roadbed.

In S504, the acquisition unit 301 acquires data regarding the inclination angle of the moving body 110 at each of a plurality of locations set on the roadbed. More specifically, the acquisition unit 301 performs the following processing for each of the plurality of locations.
That is, the acquisition unit 301 acquires data on the inclination angle in each rotation direction (roll direction, pitch direction) from the data group sorted as data corresponding to the location in S501. Then, the acquisition unit 301 calculates the average of the inclination angles corresponding to the location for each rotation direction, and uses the obtained value regarding the inclination angle in each rotation direction (roll direction, pitch direction) of the moving body 110 at that location. Data. However, as another example, the acquisition unit 301 may calculate the maximum value, minimum value, amplitude, average absolute value, root mean square, etc. of the tilt angle data corresponding to the location for each rotation direction. It may also be acquired as data related to corners. Further, the acquisition unit 301 may acquire a plurality of data among these data and the average data of the tilt angle corresponding to the location, respectively, as data regarding the tilt angle.
Thereby, the acquisition unit 301 can acquire data regarding the inclination angle of each rotation direction of the moving body 110 for each of a plurality of locations on the roadbed.

In S505, the acquisition unit 301 acquires data regarding the gauge deviation width at each of a plurality of locations set on the roadbed. More specifically, the acquisition unit 301 performs the following processing for each of the plurality of locations.
That is, the acquisition unit 301 acquires gauge data from the data group sorted as data corresponding to the location in S501. Then, the acquisition unit 301 calculates the average of the differences between the gauge data corresponding to that location and a predetermined value as the gauge width, and uses this as data regarding the gauge deviation width at that location. However, as another example, the acquisition unit 301 may acquire the maximum value, minimum value, amplitude, average absolute value, root mean square, etc. of the difference between the gauge data corresponding to the location and a predetermined value as the gauge width. may be acquired as data regarding the gauge deviation width. In addition, the acquisition unit 301 acquires a plurality of data among these data and data on the average difference between the gauge data corresponding to the location and a predetermined value as the gauge width, respectively, regarding the inclination angle. It may also be acquired as data.
Thereby, the acquisition unit 301 can acquire data regarding the gauge deviation width for each of a plurality of locations on the roadbed.
In S506, the acquisition unit 301 stores the various data acquired in each of S502 to S505 in the auxiliary storage device 203. That is, the acquisition unit 301 obtains data regarding acceleration in three directions corresponding to each location set on the roadbed, data regarding angular velocity in three rotational directions, data regarding inclination angle in two rotational directions, and data regarding gauge deviation width. Stores nine types of data.

Here, an overview of the diagnostic processing executed by the diagnostic system 100 will be explained.
When the moving body 110 moves on the roadbed, fluctuations occur in the moving body 110 due to abnormalities in the roadbed (deformation of the roadbed, deformation of the rails, etc.). This variation does not necessarily appear as one type of physical quantity in one direction/rotation direction with respect to the moving body 110. Physical quantities include acceleration, velocity, angular velocity, inclination angle, width (length), and the like. It is considered that this variation may appear as a composite of various types of physical quantities in various directions and rotational directions with respect to the moving body 110. Therefore, an abnormality in the roadbed is considered to appear as a plurality of pieces of data regarding a direction/rotation direction with respect to the moving body 110 or a plurality of different types of physical quantities.
Furthermore, since the moving object 110 moves in a low speed zone, each sensor data is weaker than sensor data output from a sensor installed on a moving object that is moving at a speed higher than the low speed zone. Become. Therefore, even if only one piece of data is focused on, it may not be possible to determine whether or not the influence of an abnormality in the roadbed has appeared.
Therefore, in the present embodiment, the diagnostic system 100 stores data related to acceleration in three directions, data related to angular velocities in three rotational directions, and data related to tilt angles in two rotational directions among the data stored in the process of FIG. At least two or more of the eight types of data are determined as data to be used for diagnosing abnormalities in the roadbed. Then, the diagnostic system 100 performs a threshold value determination on each of the determined data, and performs abnormality diagnosis of the roadbed. The above is an overview of the diagnostic processing.

The details of the diagnostic processing executed by the diagnostic system 100 will be explained using FIG. 6.
In S601, the diagnosis unit 302 extracts eight types of data from the nine types of data stored in S506: data related to acceleration in three directions, data related to angular velocities in three rotational directions, and data related to tilt angles in two rotational directions. The data to be used for diagnosing the roadbed abnormality is determined so as to include at least two or more of the data. In the following, data used for diagnosing roadbed abnormalities will be referred to as diagnostic data.
The diagnostic unit 302 determines data specified by an operation via the input unit 205 as diagnostic data. However, as another example, the diagnostic unit 302 may determine a predetermined type of data as the diagnostic data.
In the present embodiment, the diagnosis unit 302 includes data related to acceleration in the left-right direction, data related to acceleration in the vertical direction, data related to angular velocity in the roll direction, data related to angular velocity in the yaw direction, data related to angular velocity in the pitch direction, and data related to the tilt angle in the roll direction. It is assumed that seven types of data, including data regarding the pitch direction and data regarding the tilt angle in the pitch direction, are determined as diagnostic data.

In S602, the diagnosis unit 302 selects one of the plurality of locations set on the roadbed as the location to be diagnosed. In the following, the location selected in S602 will be referred to as the selected location.
In S603, the diagnostic unit 302 specifies, for each piece of diagnostic data corresponding to the selected location, whether the value of the diagnostic data is equal to or greater than a first threshold value predetermined for each piece of diagnostic data. Each of the first threshold values is predetermined for each type of diagnostic data.
In S604, the diagnosis unit 302 determines whether the number of pieces of diagnostic data identified as being equal to or greater than the first threshold in S603 is equal to or greater than a predetermined number of 2 or more (for example, 3, 4, etc.). Determine whether If the diagnostic unit 302 determines that the number of pieces of diagnostic data identified as being equal to or greater than the first threshold in S603 is equal to or greater than a predetermined number, the diagnostic unit 302 advances the process to S607. If the diagnostic unit 302 determines that the number of pieces of diagnostic data identified as equal to or greater than the first threshold in S603 is less than the predetermined number, the diagnostic unit 302 advances the process to S605.

In S605, the diagnosis unit 302 determines, for each piece of diagnostic data corresponding to the selected location, that the value of the diagnostic data is a predetermined threshold value for each piece of diagnostic data and is larger than the first threshold value used in the process of S603. It is determined whether or not the value is equal to or greater than the threshold value of 2. This second threshold value is predetermined for each type of diagnostic data.
In S606, the diagnostic unit 302 determines whether the number of pieces of diagnostic data identified as being equal to or greater than the second threshold in S605 is one or more. If the diagnostic unit 302 determines that the number of pieces of diagnostic data identified as equal to or greater than the second threshold in S605 is one or more, the process proceeds to S607. If the diagnostic unit 302 determines that the number of pieces of diagnostic data identified as equal to or greater than the second threshold in S605 is 0, the diagnostic unit 302 advances the process to S608.
Here, the significance of the processing in S605 and S606 will be explained. If there is a significant abnormality in the roadbed, it may be possible to obtain sensor data with a significant value even when the moving object 110 moves in a low speed zone. Therefore, the diagnostic unit 302 executes S605 to S606 in order to diagnose that there is an abnormality if there is even one data in the diagnostic data that is equal to or greater than the threshold value that is larger than the threshold value used in S603.

In S607, the diagnostic unit 302 diagnoses that there is an abnormality at the selected location on the roadbed.
In S608, the diagnostic unit 302 diagnoses that the selected location on the roadbed has no abnormality (is normal).
In S609, the diagnosis unit 302 determines whether diagnosis has been completed for all of the plurality of locations set on the roadbed. When the diagnosis unit 302 determines that diagnosis has been completed for all of the plurality of locations set on the roadbed, the process proceeds to S610. Furthermore, when the diagnosis unit 302 determines that there is a location where diagnosis has not been completed among the plurality of locations set on the roadbed, the process proceeds to S602.

In S610, the diagnosis unit 302 outputs diagnosis results (results of S607 or S608) for each of the plurality of locations set on the roadbed. However, as another example, the diagnosis unit 302 may output only the diagnosis results for locations diagnosed as abnormal.
The diagnostic unit 302 outputs the diagnostic results by displaying them on the monitor of the output unit 206. However, as another example, the diagnostic unit 302 may output the diagnostic results by storing them in the auxiliary storage device 203. Furthermore, the diagnosis unit 302 may output the diagnosis result by transmitting it to a predetermined destination.

(effect)
As described above, with the processing of this embodiment, even when the moving object 110 moves in a low speed zone, sensor data from the sensor installed on the moving object 110 can be used to more accurately determine whether there is an abnormality in the roadbed. It is possible to diagnose whether

(Effectiveness verification experiment)
The inventors conducted an experiment to verify the effectiveness of the processing of this embodiment. In the following, this experiment will be referred to as the main experiment. This experiment and its experimental results will be explained.
FIG. 7 is a diagram explaining the situation of this experiment.
In this experiment, an overhead crane device 700 was used as the moving body 110. Overhead crane device 700 moves along rails 710.
The overhead crane device 700 includes a girder 701, a trolley 702, a crane section 703, and a running section 704. The total weight of the overhead crane device 700 is 155.2 tons. The girder 701 is a columnar structure that supports the trolley 702 and the like. The trolley 702 is a cart that moves on the girder by lifting a load via a crane section 703. In this experiment, the trolley 702 is placed at the center of the girder 701. The crane section 703 is a mechanism for suspending a load, and moves in conjunction with the trolley 702. The traveling section 704 has wheels, is installed on a rail 710, and is used to move the overhead crane device 700 on the rail 710.

The rail 710 is a rail arranged horizontally in the east-west direction, and has a length of 360 m. In the following description, the rail placed on the north side of the rails 710 will be referred to as rail 710A. Furthermore, in the following description, the rail disposed on the south side of the rails 710 is referred to as a rail 710B.
Further, in the following, the running section 704 installed on the rail 710A will be referred to as a running section 704A. Furthermore, hereinafter, the running section 704 installed on the rail 710B will be referred to as a running section 704B.

Each of the running sections 704A and 704B moves to the same position in the east-west direction. Therefore, the longitudinal direction of the girder 701 is parallel to the north-south direction. Further, the width between the rails of the rail 710 (the distance between the rail 710A and the rail 710B) is 38.5 m.
The roadbed to be diagnosed in this experiment is the rail 710A and the installation surface of the rail 710A.
In this experiment, the diagnostic system 100 is installed on an overhead crane device 700, which is a moving body 110. The acceleration sensor 102 and the angular velocity sensor 103 are arranged on the girder 701 directly above the running section 704A.

In this experiment, the overhead crane apparatus 700 was started from point A in FIG. 7 and was run eastward to point D. The overhead crane device 700 moved at a constant speed of 7.2 km/h in the section from point B to point C. The diagnostic device 101 acquired the raw data group by performing the same processing as in FIG. 4 while the overhead crane device 700 was moving at low speed in the section from point B to point C. However, in this experiment, the diagnostic device 101 included a tilt angle sensor, and in S405, the tilt angles in the roll direction and the pitch direction were acquired via this tilt angle sensor.
Then, the diagnostic device 101 generated data serving as diagnostic data candidates by performing the same processing as in FIG. 5 based on the acquired raw data group. However, in this experiment, the diagnostic device 101 did not use the track sensor 104, so it did not execute the process of S505. Furthermore, in this experiment, the plurality of predetermined locations on the roadbed were defined as a plurality of locations separated by intervals of 0.25 m from point B to point C on the rail 710.
Furthermore, in S502, the diagnostic apparatus 101 acquired average data and root mean square data of acceleration sensor data corresponding to each location as data regarding acceleration corresponding to each location. In the following, the average data of acceleration sensor data corresponding to each location will be referred to as acceleration (average) data. Furthermore, hereinafter, data of the root mean square of acceleration sensor data corresponding to each location will be referred to as acceleration (root mean square) data.

Furthermore, in S503, the diagnostic apparatus 101 acquired average data and root mean square data of the sensor data of the angular velocity corresponding to each location as data regarding the angular velocity corresponding to each location. In the following, the average data of the angular velocity sensor data corresponding to each location will be referred to as angular velocity (average) data. Further, hereinafter, data of the root mean square of sensor data of angular velocity corresponding to each location will be referred to as angular velocity (root mean square) data.
Furthermore, in S504, the diagnostic apparatus 101 acquired data similar to the data described in FIG. 5.
The data generated in the process shown in FIG. 5 executed here is assumed to be candidate data.

Among the generated candidate data, horizontal acceleration (average) data, longitudinal acceleration (average) data, vertical acceleration (average) data, roll direction angular velocity (average) data, and yaw direction angular velocity (average) ) The data are shown in FIG.
Among the generated candidate data, data on the tilt angle in the roll direction and data on the tilt angle in the pitch direction are shown in FIG.
Among the generated candidate data, horizontal acceleration (square mean) data, longitudinal acceleration (square mean) data, vertical acceleration (square mean) data, and roll direction angular velocity (square mean) The angular velocity (root mean square) data in the yaw direction is shown in FIG.

The horizontal axis of each graph in FIGS. 8 to 10 indicates the position within the 330 m section from point B to point C on the rail 710. Below, the section shown on the horizontal axis of each graph in FIGS. 8 to 10 is a 185 m section within this 330 m section. In the following, this 185 m section will be defined as a set section. The vertical axis indicates data values. Each graph is a graph showing the value of data corresponding to the position indicated by the corresponding horizontal axis.

Subsequently, the diagnostic device 101 diagnosed whether or not there was an abnormality in the roadbed by performing the process shown in FIG. However, in this experiment, the diagnostic apparatus 101 determined the plurality of data shown in FIGS. 9 and 10 as diagnostic data in S601. That is, in this experiment, the diagnostic data includes data on the tilt angle in the roll direction, data on the tilt angle in the pitch direction, acceleration (mean squared) data in the left-right direction, acceleration (mean squared) data in the longitudinal direction, and data in the vertical direction. There are seven types of data: acceleration (square mean) data, roll direction angular velocity (square mean) data, and yaw direction angular velocity (square mean) data.
Further, the first threshold value for the data of the tilt angle in the roll direction was set to 0.3. Further, the first threshold value for data on the slope angle in the pitch direction was set to 1. Further, the first threshold value for the left-right direction acceleration (root mean square) data was set to 2. Further, the first threshold value for longitudinal acceleration (root mean square) data was set to 0.5. Further, the first threshold value for vertical acceleration (root mean square) data was set to 3. Further, the first threshold value for the angular velocity (root mean square) data in the roll direction was set to 0.05. Further, the first threshold value for the angular velocity (root mean square) data in the yaw direction was set to 0.02.

Further, the predetermined number used for the determination process in S604 is three.
Further, the second threshold value for the data of the inclination angle in the roll direction was set to 1. Further, the second threshold value for data on the slope angle in the pitch direction was set to 1.5. Further, the second threshold value for the left-right direction acceleration (root mean square) data was set to 5. Further, the second threshold value for the acceleration (root mean square) data in the longitudinal direction was set to 1. Further, the second threshold value for vertical acceleration (root mean square) data was set to 6. Further, the second threshold value for the angular velocity (root mean square) data in the roll direction was set to 0.2. Further, the second threshold value for the angular velocity (root mean square) data in the yaw direction was set to 0.05.

The diagnosis results will be explained using FIG. 11.
The graph in FIG. 11A is a graph showing, for each location in the set section, the number of diagnostic data that exceeds the first threshold value among the diagnostic data corresponding to each location. The horizontal axis indicates the position in the set interval. The vertical axis indicates the number of pieces of diagnostic data that is equal to or greater than the first threshold. Moreover, the dotted line in the graph indicates the value 3 (the predetermined number used in S604) on the vertical axis. That is, in S604, the diagnostic device 101 determines that three or more pieces of diagnostic data are equal to or greater than the first threshold value in the graph, and diagnoses the location as abnormal in S607.
The graph in FIG. 11(b) is a graph showing, for each location in the set section, the number of diagnostic data that exceeds the second threshold value among the diagnostic data corresponding to each location. The horizontal axis indicates the position in the set interval. The vertical axis indicates the number of pieces of diagnostic data that is equal to or greater than the second threshold. Moreover, the dotted line in the graph indicates the value 1 on the vertical axis. That is, in S606, the diagnostic device 101 determines that there is one or more pieces of diagnostic data that are equal to or greater than the second threshold for locations in the graph that are equal to or greater than this dotted line, and diagnoses that there is an abnormality in S607.

Looking at the graph of FIG. 11(a), it can be seen that based on the first threshold value, abnormalities are diagnosed at locations around 103 m, around 116 m, and around 117.5 m in the set section.
Furthermore, looking at the graph in FIG. 11(b), it can be seen that based on the second threshold, abnormalities are diagnosed at locations around 113 m and around 117.5 m.
As a result of the process shown in FIG. 6, the diagnostic device 101 diagnosed that there is an abnormality in each of the locations near 103 m, 113 m, 116 m, and 117.5 m in the set section.

The inventors investigated whether there was actually an abnormality at each of the locations near 103 m, 113 m, 116 m, and 117.5 m in the set section on the rail 710A. Then, the inventors confirmed that abnormalities (rail wear, scratches) actually occurred at each location.
From this, the inventors confirmed that the process of this embodiment is effective for detecting abnormalities in the roadbed on which the overhead crane device 700 moves.
Moreover, as shown in FIG. 11, an abnormality at a location near 113 m of the set section cannot be detected based on the first threshold value. Furthermore, abnormalities at locations near 103 m and 116 m within the set section cannot be detected based on the second threshold. Therefore, the inventors have confirmed that by using both the first threshold value and the second threshold value, it is possible to diagnose the presence or absence of abnormality in the roadbed with higher accuracy.

(Modification 1)
In this embodiment, the diagnosis unit 302 includes at least two or more of eight types of data: data regarding acceleration in three directions, data regarding angular velocity in three rotational directions, and data regarding tilt angles in two rotational directions. We decided to determine the diagnostic data as follows. However, as another example, the diagnostic unit 302 may determine the diagnostic data to include data regarding two or more (plural) physical quantities having different directions and directions of rotation. The diagnosis unit 302 performs diagnosis using a plurality of pieces of data regarding physical quantities in different directions and rotational directions. Abnormalities in the roadbed can be diagnosed with high accuracy.
(Modification 2)
In the present embodiment, in the process of FIG. 6, the diagnosis system 100 outputs the diagnosis results in S610 after the diagnosis for all diagnosis locations is completed in the processes of S602 to S609. However, as another example, in the process of FIG. 6, the diagnostic system 100 may output the diagnosis result for one diagnostic location each time the diagnosis is completed in the processes of S602 to S608 for that diagnostic location. In that case, the diagnostic system 100 may output diagnostic results for all diagnostic locations where diagnosis has been completed up to that point.

(Modification 3)
In the present embodiment, in the process of FIG. 6, the diagnostic system 100 performs diagnostic processing using the first threshold value in S603 to S604 for each piece of diagnostic data, and if it cannot be diagnosed as abnormal, the diagnostic system 100 performs diagnostic processing in S605 to S606. Diagnostic processing using the second threshold was performed. However, as another example, the diagnostic system 100 may not execute the diagnostic process using the second threshold. In this case, if the number of data equal to or greater than the first threshold value is less than the predetermined number in S604, the diagnostic system 100 advances the process to S608 and diagnoses the selected location as having no abnormality.
Furthermore, the diagnostic system 100 may execute the diagnostic process using the second threshold value without executing the diagnostic process using the first threshold value. In that case, the diagnostic system 100 advances the process to S605 after the process of S602.
Furthermore, the diagnostic system 100 may obtain the final diagnostic result based on the result of the diagnostic process using the first threshold and the result of the diagnostic process using the second threshold. For example, if the diagnostic system 100 diagnoses that there is an abnormality in the diagnostic processing using the first threshold value and also diagnoses that there is an abnormality in the diagnostic processing using the second threshold value, the diagnostic system 100 ultimately determines that the selected location is abnormal. It can be diagnosed that there is.

(Modification 4)
In the present embodiment, the diagnostic device 101 acquires sensor data from each sensor installed on the moving body 110, acquires data regarding each physical quantity, and based on the diagnostic data determined from the acquired data, determines the condition of the roadbed. An abnormality was diagnosed. However, as another example, the diagnostic device 101 may not perform the process of diagnosing abnormalities in the roadbed. In that case, for example, the external diagnostic device acquires data regarding each physical quantity from sensor data acquired from each sensor of the moving body 110 that the diagnostic device 101 moves, determines diagnostic data from the acquired data, and diagnoses the determined Based on the data, abnormalities in the roadbed may be diagnosed.
(Modification 5)
In this embodiment, the diagnostic system 100 does not determine data regarding the gauge deviation width as diagnostic data in S602. However, as another example, the diagnostic system 100 may determine data regarding the gauge deviation width as the diagnostic data in S602.
(Other variations)
Part or all of the functional configuration of the diagnostic system of this embodiment may be implemented as hardware in the diagnostic device 101 or the like.

100 diagnostic system, 101 diagnostic device, 201 CPU

Claims (9)

A plurality of data regarding a plurality of physical quantities corresponding to a location on the roadbed determined based on sensor data measured by a sensor installed on the moving body when the moving body moves on the roadbed, the Each of data regarding the acceleration in a plurality of different directions applied to the moving body, each of the data regarding the angular velocity of the moving body in a plurality of different rotational directions, and each of the data regarding the inclination angle of the mobile body in a plurality of different rotational directions, acquisition means for acquiring the plurality of data including at least two or more of the data;
and , one data among the plurality of data is equal to or higher than a second threshold, which is a threshold set for each of the plurality of data and is larger than the first threshold set for each of the plurality of data. As described above, a diagnostic means for diagnosing whether or not there is an abnormality at the location on the roadbed based on whether or not it exists ;
A diagnostic device with 2. The acquisition means acquires the plurality of data including root mean square data of respective accelerations in one or more directions applied to the moving body, measured by an acceleration sensor installed on the moving body. Diagnostic equipment as described. 2. The acquisition means acquires the plurality of data including root mean square data of each of the angular velocities in one or more rotational directions of the movable body, measured by an angular velocity sensor installed on the movable body. Or the diagnostic device according to 2. The diagnostic device according to any one of claims 1 to 3 , wherein the plurality of physical quantities include two physical quantities in corresponding directions or in different rotational directions. The mobile object is a vehicle that moves along a rail on the roadbed,
The diagnosis according to any one of claims 1 to 4 , wherein the acquisition means acquires the plurality of data including data regarding the width of a gauge deviation of the rail acquired via a gauge sensor included in the sensor. Device. The diagnostic apparatus according to any one of claims 1 to 5 , further comprising an output means for outputting information indicating a result of diagnosis by the diagnostic means. A plurality of data regarding a plurality of physical quantities corresponding to a location on the roadbed determined based on sensor data measured by a sensor installed on the moving body when the moving body moves on the roadbed, the Each of data regarding the acceleration in a plurality of different directions applied to the moving body, each of the data regarding the angular velocity of the moving body in a plurality of different rotational directions, and each of the data regarding the inclination angle of the mobile body in a plurality of different rotational directions, acquisition means for acquiring the plurality of data including at least two or more of the data;
and , one data among the plurality of data is equal to or higher than a second threshold, which is a threshold set for each of the plurality of data and is larger than the first threshold set for each of the plurality of data. As described above, a diagnostic means for diagnosing whether or not there is an abnormality at the location on the roadbed based on whether or not it exists ;
A system with A method for controlling a diagnostic device, the method comprising :
A plurality of data regarding a plurality of physical quantities corresponding to a location on the roadbed determined based on sensor data measured by a sensor installed on the moving body when the moving body moves on the roadbed, the Each of data regarding the acceleration in a plurality of different directions applied to the moving body, each of the data regarding the angular velocity of the moving body in a plurality of different rotational directions, and each of the data regarding the inclination angle of the mobile body in a plurality of different rotational directions, an acquisition step of acquiring the plurality of data including at least two or more of the data;
whether or not there is a predetermined number of two or more pieces of data that is equal to or greater than a first threshold value set for each of the plurality of data among the plurality of data acquired in the acquisition step ; , one data among the plurality of data is equal to or higher than a second threshold, which is a threshold set for each of the plurality of data and is larger than the first threshold set for each of the plurality of data. As described above, a diagnosis step of diagnosing whether or not there is an abnormality at the location on the roadbed based on whether or not it exists ;
control methods including. A program for causing a computer to function as each means of the diagnostic device according to any one of claims 1 to 6 .

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