JP2021129329A - Diagnostic device, system, control method, and program - Google Patents

Abstract

To more accurately make a diagnosis of abnormality in a roadbed by using signals from sensors installed in a movable body restricted in velocity.SOLUTION: A diagnostic device acquires a plurality of pieces of data on a plurality of physical quantities corresponding to a place on a roadbed which are determined on the basis of sensor data measured by sensors installed in a movable body, and which include at least two pieces of data out of respective pieces of data on acceleration in a plurality of different directions, respective pieces of data on angular velocities in a plurality of different rotation directions, and respective pieces of data on inclination angles in a plurality of different rotation directions. On the basis of whether there are, in the plurality of acquired pieces of data, at least a predetermined number of two or more pieces of data each having a value equal to or greater than a first threshold set for each piece of data, the diagnostic device makes a diagnosis of the presence or absence of abnormality in the place on the roadbed.SELECTED DRAWING: Figure 6

Description

The present invention relates to diagnostic devices, systems, control methods and programs.

When a moving body moves on the roadbed, an abnormality in the roadbed is diagnosed using data measured through a sensor such as an acceleration sensor installed on the moving body.
Patent Document 1 discloses a method of diagnosing an abnormality of track equipment or vehicle equipment of a track-based transportation system in which a vehicle travels on a predetermined track.
Further, Patent Document 2 discloses a system for grasping a state of a track on which a vehicle moves from the vibration of the vehicle.

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

Depending on the moving object, the speed may be limited. As the speed becomes lower, the data obtained through the sensor installed on the moving body may become weaker. In such a case, there is a problem that only weak data can be obtained through the sensor, and it may not be possible to diagnose an abnormality in the roadbed on which the moving body moves.
Therefore, an object of the present invention is to more accurately diagnose an abnormality of the roadbed by using a signal of a sensor installed in a moving body whose speed is limited.

The diagnostic apparatus of the present invention relates to a plurality of physical quantities corresponding to a location on the roadbed, which is determined based on sensor data measured by a sensor installed on the moving body when the moving body moves on the roadbed. Data relating to accelerations in a plurality of different directions with respect to the moving body, data relating to angular velocities in a plurality of different rotation directions of the moving body, and data relating to a plurality of different rotation directions of the moving body. An acquisition means for acquiring each of the data relating to the inclination angle, the plurality of data including at least two or more of the data, and the plurality of data acquired by the acquisition means for each of the plurality of data. As a diagnostic means for diagnosing whether or not there is an abnormality in the location on the roadbed based on whether or not there are two or more predetermined numbers of data that are equal to or greater than the set first threshold value. Has.

According to the present invention, it is possible to more accurately diagnose the abnormality of the roadbed by using the signal of the sensor installed in the moving body whose speed is limited.

FIG. 1 is a diagram showing an example of a 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 apparatus. FIG. 4 is a flowchart showing an example of the sensor data collection process. FIG. 5 is a flowchart showing an example of data acquisition processing. FIG. 6 is a flowchart showing an example of diagnostic processing. FIG. 7 is a diagram illustrating an experimental situation. FIG. 8 is a diagram showing data acquired in the experiment. FIG. 9 is a diagram showing the data acquired in the experiment. FIG. 10 is a diagram showing data acquired in the experiment. FIG. 11 is a diagram for explaining the experimental results.

<Embodiment>
Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

(Diagnostic system)
FIG. 1 is a diagram showing an example of a system configuration of the diagnostic system 100 of the present embodiment.
The diagnostic system 100 is a system installed on the moving body 110 and diagnosing whether or not there is an abnormality in the roadbed on which the moving body 110 moves. An abnormality in the roadbed is some deviation from a predetermined normal state that occurs in the roadbed. In the present embodiment, the abnormalities of the roadbed include not only serious ones such as broken rails and torn rails, but also minor ones such as wear and scratches on the rails. The moving body 110 is a railroad vehicle that moves on a track along a rail installed on the roadbed. However, as another example, the moving body 110 may be another moving body such as a vehicle moving on a road or an overhead crane moving on a rail. In the present embodiment, the moving body 110 may move along the rail in a speed range defined as a low speed (for example, less than 20 km / h, 10 km / h or less, etc.), and may 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 gauge 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 an acceleration sensor 102, an angular velocity sensor 103, and a gauge sensor 104. The diagnostic device 101 is, for example, a personal computer (PC), a computer incorporated in a mobile body, a server device, a tablet device, or the like.
The acceleration sensor 102 is a sensor used for measuring the acceleration applied to the moving body 110. The angular velocity sensor 103 is a sensor used for measuring the angular velocity of the moving body 110. The acceleration sensor 102 has a predetermined range (for example, the pair of wheels) from the pair of wheels so that the acceleration transmitted through the pair of wheels of the moving body 110 can be appropriately detected. It is installed at a position within the range of the distance from the middle position of the above to the predetermined threshold value or less. In the following, this predetermined pair of wheels will be referred to as a reference wheel pair.
Further, the angular velocity sensor 103 is predetermined from a predetermined range from the reference wheel pair (for example, from a position intermediate between the pair of wheels) so that the angular velocity generated by the contact between the reference wheel pair and the rail can be appropriately detected. It is installed at a position within the range of the distance below the specified threshold.

The gauge sensor 104 is a sensor used for measuring the gauge, which is the distance between two rails in the orbit, including two two-dimensional laser measuring meters. Each of the two two-dimensional laser measuring meters of the track sensor 104 has the end 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 gravity direction is the downward direction at the measurement position. Measure the position of. Then, the gauge sensor 104 measures the gauge at the measurement position based on the position measured by each of the two two-dimensional laser measuring meters. Each of the two two-dimensional laser measuring meters of the gauge sensor 104 is a position of the rail at a position near the position where the reference wheel pair on the rail contacts (for example, a position advanced by 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 the present 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 in the rail.
In the following, the traveling direction of the moving body 110 will be the forward direction. Further, in the following, the direction of gravity is the downward direction.
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 speedometer. The positioning meter 105 obtains the position of the mobile body 110 by reading the signal from the RFID tag via the RFID tag reader. Further, the positioning meter 105 obtains the speed of the moving body 110 via the laser Doppler speedometer. In the present embodiment, the positioning meter 105 measures the position of the reference wheel pair on the roadbed (for example, the position 100 m from the start point of the moving body 110) as the position of the moving body 110. However, the positioning meter 105 may determine the position of the moving body 110 by using another method. For example, the positioning meter 105 may use GPS to determine the position of the moving body 110. Further, the positioning meter 105 may use a laser range finder to determine the position of the moving body 110. Further, the positioning meter 105 may determine the position of the moving body 110 based on the vehicle speed signal from the speedometer installed on the moving body 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 unit 205, and an output unit 206. The elements are communicably connected to each other via the system bus 207.
The CPU 201 is a central processing unit that controls the 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 of 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 connecting to an external device such as an acceleration sensor 102, an angular velocity sensor 103, a gauge sensor 104, and a positioning meter 105. The CPU 201 inputs / outputs information to / from the acceleration sensor 102, the angular velocity sensor 103, the gauge 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, and a display unit of a touch panel.
When the CPU 201 executes the process according to the program stored in the auxiliary storage device 203 or the like, the function of the diagnostic device 101 described later in FIG. 3 and the processing of the flowchart described later in FIGS. 4 to 6 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 unit 301 and a diagnostic unit 302.
The acquisition unit 301 acquires a predetermined type of data via the acceleration sensor 102, the angular velocity sensor 103, and the gauge sensor 104. In the present embodiment, the acquisition unit 301 acquires data related to some physical quantity obtained based on a signal output from at least one of the measuring instruments such as the acceleration sensor 102, the angular velocity sensor 103, and the gauge 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 with reference to FIG.
In the present embodiment, the diagnostic system 100 starts the process of FIG. 4 when the moving body 110 starts moving on the roadbed from the starting point. In the following, the data output from the sensor will be referred to as sensor data.
In S401, the acquisition unit 301 acquires the position of the moving body 110 (position of the reference wheel pair) at the time of processing and the speed of the moving body 110 via the positioning meter 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 sensor data indicating accelerations in the front-rear direction, accelerations in the vertical direction, and accelerations in the left-right direction as sensor data indicating accelerations in the plurality of directions.

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

In S405, the acquisition unit 301 integrates (totals) the values of the sensor data of the angular velocities acquired in S403 so far with respect to the roll direction to obtain the inclination angle of the moving body 110 in the roll direction at the position acquired in S401. Ask. Further, the acquisition unit 301 corrects the obtained inclination angle in the roll direction with the value of the acceleration sensor data acquired in S402 so far, and acquires the corrected inclination angle as the inclination angle in the roll direction of the moving body 110. do. Further, the acquisition unit 301 obtains the inclination angle of the moving body 110 in the pitch direction at the position acquired in S401 by integrating (totaling) the values of the sensor data of the angular velocities acquired in S403 so far in the pitch direction. .. Further, the acquisition unit 301 corrects the obtained inclination angle in the pitch direction with the value of the acceleration sensor data acquired in S402 so far, and acquires the corrected inclination angle as the inclination angle in the pitch direction of the moving body 110. do. However, as another example, the acquisition unit 301 may directly acquire the tilt angle data by using a sensor capable of directly detecting 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 associates the auxiliary storage device. Store in 203.
In S407, the acquisition unit 301 waits for a predetermined period. In the present 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 wait for an arbitrary period such as 1 msec, 5 msec, 1 sec, etc. in S407.

In S408, the acquisition unit 301 determines whether or not the movement of the moving body 110 is completed. In the present embodiment, the acquisition unit 301 determines that the movement of the moving body 110 has been completed when the position of the moving body 110 acquired in the latest processing of S401 is a point after the goal point on the roadbed. Further, the acquisition unit 301 determines that the movement of the moving body 110 is not completed when the position of the moving body 110 acquired in the latest processing of S401 is not a point after the goal point on the roadbed.
When it is determined that the movement of the moving body 110 is completed, the acquisition unit 301 ends the process of FIG. 4, and when it is determined that the movement of the moving body 110 is not completed, the acquisition unit 301 proceeds to the process of S401.
By the above processing of FIG. 4, 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 by the process of FIG. 4 will be referred to as a raw data group.

The data acquisition process executed by the diagnostic system 100 will be described with reference to FIG.
In S501, the acquisition unit 301 divides each data of the raw data group stored in the process of FIG. 4 into a data group corresponding to each of a plurality of predetermined locations on the roadbed. In the present embodiment, a plurality of places are set on the roadbed from the start point to the goal point along the rail at predetermined intervals (for example, 1 m, 25 cm, etc.). That is, the acquisition unit 301 distributes each data of the raw data group as data corresponding to a place including a position on the roadbed corresponding to each data.
In S502, the acquisition unit 301 acquires data on the acceleration applied to the moving body 110 at each of the 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 the sensor data of the acceleration in each direction (front-back direction, up-down direction, left-right direction) output from the acceleration sensor 102 from the data group distributed as the data corresponding to the location in S501. Then, the acquisition unit 301 obtains the root mean square of the acceleration sensor data corresponding to the place (the average value of the squares of each sensor data) for each direction, and applies the obtained value to the moving body 110 at the place. The data is related to acceleration in the direction. By taking the root mean square, the acquisition unit 301 can acquire more emphasized data on acceleration even when the moving body 110 moves in the low speed band and can acquire only weak sensor data. However, as another example, the acquisition unit 301 obtains the average value, maximum value, minimum value, amplitude, average of absolute values, etc. of the acceleration sensor data corresponding to the location for each direction, and data related to the acceleration in each direction. May be obtained as. Further, the acquisition unit 301 may acquire a plurality of data of these data and the root mean square data of the acceleration sensor data corresponding to the location, respectively, as data related to the acceleration.
As a result, the acquisition unit 301 can acquire data regarding the acceleration of the moving body 110 in each direction at each of the plurality of locations on the roadbed.

In S503, the acquisition unit 301 acquires data on the angular velocity of the moving body 110 at each of the 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 the sensor data of the angular velocity in each rotation direction (roll direction, pitch direction, yaw direction) output from the angular velocity sensor 103 from the data group distributed as the data corresponding to the location in S501. .. Then, the acquisition unit 301 obtains the root mean square of the sensor data corresponding to the place (the average value of the squares of each sensor data) for each rotation direction, and obtains the obtained value in each rotation direction of the moving body 110 at the place. It is the data related to the angular velocity of. However, as another example, the acquisition unit 301 obtains the average value, maximum value, minimum value, amplitude, average of absolute values, etc. of the sensor data of the angular velocity corresponding to the location for each rotation direction, and the angular velocity in each rotation direction. It may be acquired as data about. Further, the acquisition unit 301 may acquire a plurality of data of these data and the root mean square data of the sensor data of the angular velocity corresponding to the location, respectively, as data relating to the angular velocity.
As a result, the acquisition unit 301 can acquire data regarding the angular velocities of the moving body 110 in each rotation direction for each of the 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 the 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 the data of the inclination angle in each rotation direction (roll direction, pitch direction) from the data group distributed as the data corresponding to the location in S501. Then, the acquisition unit 301 obtains the average of the inclination angles corresponding to the place for each rotation direction, and obtains the obtained value with respect to the inclination angle of each rotation direction (roll direction, pitch direction) of the moving body 110 at the place. Let it be data. However, as another example, the acquisition unit 301 sets the maximum value, the minimum value, the amplitude, the average of the absolute values, the root mean square, and the like of the inclination angle data corresponding to the location in each rotation direction. It may be acquired as data on the corner. Further, the acquisition unit 301 may acquire a plurality of data of these data and the average data of the inclination angle corresponding to the place as the data related to the inclination angle.
As a result, the acquisition unit 301 can acquire data regarding the inclination angles of the moving body 110 in each rotation direction for each of the plurality of locations on the roadbed.

In S505, the acquisition unit 301 acquires data on the gauge deviation width at each of the 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 the gauge data from the data group distributed as the data corresponding to the location in S501. Then, the acquisition unit 301 obtains the average of the difference between the gauge data corresponding to the location and the value predetermined as the gauge width, and uses the data regarding the gauge deviation width at the location. However, as another example, the acquisition unit 301 has a maximum value, a minimum value, an amplitude, an average of absolute values, a 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 deviation width of the gauge. Further, the acquisition unit 301 relates a plurality of data of these data, the data of the gauge corresponding to the location, and the average data of the difference between the predetermined values as the width of the gauge, with respect to the inclination angle, respectively. It may be acquired as data.
As a result, the acquisition unit 301 can acquire data regarding the gauge deviation width for each of the plurality of locations on the roadbed.
In S506, the acquisition unit 301 stores various data acquired in each of S502 to S505 in the auxiliary storage device 203. That is, the acquisition unit 301 relates to data on accelerations in three directions corresponding to each location set on the roadbed, data on angular velocities in three rotation directions, data on inclination angles in two rotation directions, and inter-track deviation width. Stores 9 types of data.

Here, an outline of the diagnostic process executed by the diagnostic system 100 will be described.
When the moving body 110 moves on the roadbed, the moving body 110 fluctuates due to an abnormality in the roadbed (deformation of the roadbed, deformation of the rail, etc.). This fluctuation does not always appear as one kind of physical quantity in one direction / rotation direction with respect to the moving body 110. There are various types of physical quantities such as acceleration, velocity, angular velocity, inclination angle, and width (length). It is considered that this fluctuation may appear as a composite of various kinds of physical quantities in various directions and rotation directions with respect to the moving body 110. Therefore, it is considered that the abnormality of the roadbed appears as a plurality of data relating to the direction / rotation direction with respect to the moving body 110 or a plurality of different types of physical quantities.
Further, since the moving body 110 moves in the low speed band, each sensor data is weaker than the sensor data output from the sensor installed in the moving body moving at a speed higher than the low speed band. Become. Therefore, even if attention is paid to only one data, it may not be possible to determine whether or not the influence of the roadbed abnormality appears.
Therefore, in the present embodiment, the diagnostic system 100 includes data on acceleration in three directions, data on angular velocity in three rotation directions, and data on inclination angles in two rotation 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 used for diagnosing roadbed abnormalities. Then, the diagnostic system 100 performs a threshold value determination on each of the determined data, and performs an abnormality diagnosis of the roadbed. The above is the outline of the diagnostic process.

The details of the diagnostic process executed by the diagnostic system 100 will be described with reference to FIG.
In S601, the diagnostic unit 302 has eight types of data from nine types of data stored in S506, data on acceleration in three directions, data on angular velocity in three rotation directions, and data on two inclination angles in the rotation direction. The data used for diagnosing roadbed abnormalities is determined so as to include at least two or more of the data in. In the following, the data used for diagnosing abnormalities in the roadbed will be referred to as diagnostic data.
The diagnostic unit 302 determines the data designated by the operation via the input unit 205 as the diagnostic data. However, as another example, the diagnostic unit 302 may determine a predetermined type of data as diagnostic data.
In the present embodiment, the diagnostic unit 302 includes data on acceleration in the left-right direction, data on acceleration in the vertical direction, data on angular velocity in the roll direction, data on angular velocity in the yaw direction, data on angular velocity in the pitch direction, and tilt angle in the roll direction. It is assumed that seven types of data, data relating to data and data relating to the inclination 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 selection location.
In S603, the diagnostic unit 302 specifies, for each of the diagnostic data corresponding to the selected location, whether or not the value of the diagnostic data is equal to or greater than a predetermined first threshold value for each of the diagnostic data. Each of the first threshold values is predetermined for each type of diagnostic data.
In S604, whether or not the number of diagnostic data specified by the diagnostic unit 302 to be equal to or greater than the first threshold value in S603 is 2 or more and a predetermined number (for example, 3, 4, etc.) or more. Is determined. When the diagnosis unit 302 determines that the number of diagnostic data specified to be equal to or greater than the first threshold value in S603 is equal to or greater than a predetermined number, the process proceeds to S607. When the diagnosis unit 302 determines that the number of diagnostic data specified to be equal to or greater than the first threshold value in S603 is less than the predetermined number, the process proceeds to S605.

In S605, in the diagnostic unit 302, for each of the diagnostic data corresponding to the selected location, the value of the diagnostic data is a predetermined threshold value for each of the diagnostic data, and is larger than the first threshold value used in the process of S603. It is specified whether or not the threshold value of 2 is exceeded. This second threshold value is predetermined for each type of diagnostic data.
In S606, the diagnostic unit 302 determines whether or not the number of diagnostic data specified to be equal to or greater than the second threshold value in S605 is 1 or more. When the diagnosis unit 302 determines that the number of the diagnostic data specified to be equal to or greater than the second threshold value in S605 is 1 or more, the process proceeds to S607. When the diagnosis unit 302 determines that the number of diagnostic data specified to be equal to or greater than the second threshold value in S605 is 0, the diagnosis unit 302 proceeds to S608.
Here, the significance of the processing of S605 to S606 will be described. When there is a remarkable abnormality in the roadbed, it is conceivable that the sensor data of a remarkable value can be obtained even when the moving body 110 moves in the low speed zone. Therefore, the diagnostic unit 302 executes S605 to S606 in order to diagnose that it is abnormal if there is at least one data in the diagnostic data that is greater than or equal to the threshold value used in S603.

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

In S610, the diagnosis unit 302 outputs the diagnosis result (result of S607 or S608) for each of the plurality of places set on the roadbed. However, as another example, the diagnosis unit 302 may output only the diagnosis result for the place where the abnormality is diagnosed.
The diagnosis unit 302 outputs the diagnosis result by displaying it on the monitor of the output unit 206. However, as another example, the diagnosis unit 302 may output the diagnosis result by storing it in the auxiliary storage device 203. Further, the diagnosis unit 302 may output the diagnosis result by transmitting it to a predetermined destination.

(effect)
As described above, even when the moving body 110 moves in the low speed band by the processing of the present embodiment, whether or not there is an abnormality in the roadbed more accurately by using the sensor data of the sensor installed in the moving body 110. Can be diagnosed.

(Effectiveness verification experiment)
The inventors conducted an experiment to verify the effectiveness of the treatment of the present embodiment. In the following, this experiment will be referred to as this experiment. This experiment and its experimental results will be described.
FIG. 7 is a diagram illustrating the situation of this experiment.
In this experiment, an overhead crane device 700 was used as the moving body 110. The overhead crane device 700 moves along the rail 710.
The overhead crane device 700 includes a girder 701, a trolley 702, a crane section 703, and a traveling section 704. The overhead crane device 700 has a total weight of 155.2 tons. The girder 701 is a columnar structure that supports the trolley 702 and the like. The trolley 702 is a trolley that moves on the girder with a load via the crane unit 703. In this experiment, the trolley 702 is located in the center of the girder 701. The crane unit 703 is a mechanism for suspending a load and moves in conjunction with the trolley 702. The traveling unit 704 has wheels, is installed on the rail 710, and is used for moving 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, among the rails 710, the rails arranged on the north side will be referred to as rails 710A. Further, in the following, among the rails 710, the rails arranged on the south side will be referred to as rails 710B.
Further, in the following, the traveling unit 704 installed on the rail 710A will be referred to as a traveling unit 704A. Further, in the following, the traveling unit 704 installed on the rail 710B will be referred to as a traveling unit 704B.

The traveling portions 704A and 704B each move so as to be at the same position in the east-west direction. Therefore, the girder 701 is horizontal in the longitudinal direction in the north-south direction. The width between the rails of the rails 710 (distance between the rails 710A and the rails 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 in the overhead crane device 700, which is a mobile body 110. The acceleration sensor 102 and the angular velocity sensor 103 are arranged directly above the traveling portion 704A on the girder 701.

In this experiment, the overhead crane device 700 was driven eastward from point A in FIG. 7 to point D as a starting position. The overhead crane device 700 moved at a constant speed of 7.2 km / h in the section from the point B to the point C. The diagnostic device 101 acquired a raw data group by performing the same processing as in FIG. 4 while the overhead crane device 700 was moving at a low speed in the section from the point B to the point C. However, in this experiment, the diagnostic apparatus 101 includes an inclination angle sensor, and in S405, the inclination angles in the roll direction and the pitch direction are acquired through the inclination angle sensor.
Then, the diagnostic apparatus 101 generated data as a candidate for diagnostic data by performing the same processing as in FIG. 5 based on the acquired raw data group. However, since the diagnostic device 101 did not use the gauge sensor 104 in this experiment, the processing of S505 was not executed. Further, in this experiment, it was assumed that the plurality of predetermined locations on the roadbed were a plurality of locations where the section from the point B to the point C on the rail 710 was divided at intervals of 0.25 m.
Further, in S502, the diagnostic apparatus 101 acquired the average data of the sensor data of the acceleration corresponding to each place and the squared average data as the data related to the acceleration corresponding to each place. In the following, the average data of the acceleration sensor data corresponding to each location will be referred to as acceleration (average) data. Further, in the following, the squared average data of the acceleration sensor data corresponding to each location will be referred to as the acceleration (squared average) data.

Further, in S503, the diagnostic apparatus 101 acquired the average data of the sensor data of the angular velocity corresponding to each place and the squared average data as the data related to the angular velocity corresponding to each place. In the following, the average data of the sensor data of the angular velocity corresponding to each place will be referred to as the angular velocity (average) data. Further, in the following, the squared average data of the sensor data of the angular velocity corresponding to each place will be referred to as the angular velocity (squared average) data.
Further, in S504, the diagnostic apparatus 101 acquired the same data as the data described with reference to FIG.
The data generated by the process of FIG. 5 executed here is used as candidate data.

Of the generated candidate data, left-right acceleration (average) data, front-back acceleration (average) data, vertical acceleration (average) data, roll-direction angular velocity (average) data, and yaw-direction angular velocity (average). ) Data is shown in FIG.
Among the generated candidate data, the data of the inclination angle in the roll direction and the data of the inclination angle in the pitch direction are shown in FIG.
Among the generated candidate data, left-right acceleration (square average) data, front-back acceleration (square average) data, vertical acceleration (square average) data, and angular velocity in the roll direction (square average). The data and the angular velocity (square average) data in the yaw direction are shown in FIG.

The horizontal axis of each graph of FIGS. 8 to 10 indicates a position within a 330 m section from point B to point C on the rail 710. In the following, the section shown on the horizontal axis of each graph of FIGS. 8 to 10 is a section of 185 m within this section of 330 m. In the following, this 185 m section will be referred to as a set section. The vertical axis shows the value of the data. Each graph is a graph showing the value of data corresponding to the location existing at 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 in S601 the plurality of data shown in FIGS. 9 and 10 as diagnostic data. That is, in this experiment, the diagnostic data includes tilt angle data in the roll direction, tilt angle data in the pitch direction, acceleration (square average) data in the left-right direction, acceleration (square average) data in the front-back direction, and vertical direction. There are seven types of data: acceleration (squared average) data, angular velocity (squared average) data in the roll direction, and angular velocity (squared average) data in the yaw direction.
The first threshold value for the tilt angle data in the roll direction was set to 0.3. Further, the first threshold value for the tilt angle data in the pitch direction was set to 1. Further, the first threshold value for the acceleration (square average) data in the left-right direction was set to 2. The first threshold value for the acceleration (square average) data in the front-rear direction was set to 0.5. Further, the first threshold value for the acceleration (square average) data in the vertical direction was set to 3. The first threshold value for the angular velocity (square average) data in the roll direction was set to 0.05. The first threshold value for the angular velocity (square average) data in the yaw direction was 0.02.

Further, it is assumed that the predetermined number used for the determination process in S604 is three.
The second threshold value for the tilt angle data in the roll direction was set to 1. The second threshold value for the tilt angle data in the pitch direction was set to 1.5. The second threshold value for the acceleration (square average) data in the left-right direction was set to 5. The second threshold value for the acceleration (square average) data in the front-back direction was set to 1. The second threshold value for the acceleration (square average) data in the vertical direction was set to 6. The second threshold value for the angular velocity (square average) data in the roll direction was set to 0.2. The second threshold value for the angular velocity (square average) data in the yaw direction was set to 0.05.

The diagnosis result will be described with reference to FIG.
The graph of FIG. 11A is a graph showing the number of diagnostic data having become equal to or more than the first threshold value among the diagnostic data corresponding to each place for each place in the set section. The horizontal axis indicates the position in the set section. The vertical axis indicates the number of diagnostic data that is equal to or greater than the first threshold value. The dotted line in the graph indicates the value 3 (predetermined number used in S604) on the vertical axis. That is, in the graph, the diagnostic device 101 determines that there are three or more diagnostic data that are equal to or greater than the first threshold value in S604, and diagnoses that there is an abnormality in S607 for the locations that are equal to or greater than the dotted line.
The graph of FIG. 11B is a graph showing the number of diagnostic data having become equal to or more than the second threshold value among the diagnostic data corresponding to each place for each place in the set section. The horizontal axis indicates the position in the set section. The vertical axis indicates the number of diagnostic data that is equal to or greater than the second threshold value. The dotted line in the graph indicates the value 1 on the vertical axis. That is, in the graph, the diagnostic apparatus 101 determines that there is one or more diagnostic data that is equal to or greater than the second threshold value in S606, and diagnoses that there is an abnormality in S607 at the location that is equal to or greater than the dotted line.

Looking at the graph of FIG. 11A, it can be seen that, based on the first threshold value, it is diagnosed that there is an abnormality in the vicinity of 103 m, 116 m, and 117.5 m in the set section.
Further, looking at the graph of FIG. 11B, it can be seen that it is diagnosed that there is an abnormality in the vicinity of 113 m and the vicinity of 117.5 m based on the second threshold value.
As a result of the processing of FIG. 6, the diagnostic apparatus 101 diagnosed that there was an abnormality in each of the places near 103 m, 113 m, 116 m, and 117.5 m in the set section.

The inventors investigated whether or not there was an actual abnormality in each of the places 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 in each place.
From this, the inventors have confirmed that the process of the present embodiment is effective for detecting the abnormality of the moving roadbed of the overhead crane device 700.
Further, as shown in FIG. 11, the abnormality at the place near 113 m in the set section cannot be detected based on the first threshold value. Further, the abnormality at the place near 103 m and 116 m in the set section cannot be detected based on the second threshold value. Therefore, the inventors have confirmed that the presence or absence of an abnormality in the roadbed can be diagnosed more accurately by using both the first threshold value and the second threshold value.

(Modification example 1)
In the present embodiment, the diagnostic unit 302 includes at least two or more of eight types of data, data relating to acceleration in three directions, data relating to angular velocity in three rotation directions, and data relating to tilt angle in two rotation directions. Therefore, it was decided to determine the diagnostic data. However, as another example, the diagnostic unit 302 may determine the diagnostic data so as to include data relating to two or more (plurality) physical quantities having different directions and rotation directions. The diagnosis unit 302 performs diagnosis using a plurality of data for physical quantities having different directions and rotation directions, so that even if the influence of an abnormality in the roadbed appears as physical quantities in a plurality of directions and rotation directions, it can be further improved. It is possible to accurately diagnose abnormalities in the roadbed.
(Modification 2)
In the present embodiment, in the process of FIG. 6, the diagnostic system 100 is determined to output the diagnosis result in S610 after the diagnosis of all the diagnosis locations is completed in the processes of S602 to S609. However, as another example, in the process of FIG. 6, the diagnosis system 100 may output the diagnosis result for one diagnosis place each time the diagnosis is completed in the processes of S602 to S608. In that case, the diagnostic system 100 may output the diagnostic results for all the diagnostic locations for which the diagnosis has been completed so far.

(Modification example 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 of the diagnostic data, and when an abnormality cannot be diagnosed, in S605 to S606. A diagnostic process using the second threshold was performed. However, as another example, the diagnostic system 100 does not have to execute the diagnostic process using the second threshold value. In that case, when the number of data that is equal to or greater than the first threshold value is less than the predetermined number in S604, the diagnostic system 100 proceeds to S608 and diagnoses that the selected location is normal.
Further, 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.
Further, the diagnostic system 100 may obtain the final diagnostic result based on the result of the diagnostic process using the first threshold value and the result of the diagnostic process using the second threshold value. For example, when the diagnostic system 100 diagnoses that there is an abnormality in the diagnostic process using the first threshold value and diagnoses that there is an abnormality in the diagnostic process using the second threshold value, the diagnosis system 100 finally makes an abnormality in the selected place. You may diagnose that there is.

(Modification example 4)
In the present embodiment, the diagnostic device 101 acquires sensor data from each sensor installed in the moving moving body 110, acquires data related to each physical quantity, and based on the diagnostic data determined from the acquired data, the roadbed An abnormality was diagnosed. However, as another example, the diagnostic device 101 may not perform the process of diagnosing the abnormality of the roadbed. In that case, for example, an external diagnostic device acquires data on each physical quantity from the sensor data acquired from each sensor of the moving body 110 to which the diagnostic device 101 moves, determines diagnostic data from the acquired data, and makes a determined diagnosis. Based on the data, the roadbed abnormality may be diagnosed.
(Modification 5)
In the present embodiment, the diagnostic system 100 does not determine the data regarding the gauge deviation width as the diagnostic data in S602. However, as another example, the diagnostic system 100 may determine in S602 data on the gauge deviation width as diagnostic data.
(Other variants)
A part or all of the functional configuration of the diagnostic system of the present embodiment may be mounted on the diagnostic apparatus 101 or the like as hardware.

100 diagnostic system, 101 diagnostic device, 201 CPU

Claims (10)

A plurality of data relating to a plurality of physical quantities corresponding to a location on the roadbed, which is determined based on sensor data measured by a sensor installed on the moving body when the moving body moves on the roadbed. Each of the data on the accelerations in different directions with respect to the moving body, the data on the angular velocities in the plurality of different rotation directions of the moving body, and the data on the tilt angles in the different rotation directions of the moving body, respectively. An acquisition means for acquiring the plurality of data including at least two or more of the data, and
Based on whether or not there are two or more predetermined numbers of data having a first threshold value or more set for each of the plurality of data among the plurality of data acquired by the acquisition means. As a diagnostic means for diagnosing whether or not there is an abnormality in the place on the roadbed,
Diagnostic device with. The acquisition means according to claim 1, wherein the acquisition means acquires the plurality of data including the data of the root mean square of each of the accelerations in one or more directions on the moving body, which is measured by the acceleration sensor installed on the moving body. The diagnostic device described. The acquisition means acquires the plurality of data including the mean square data of each of one or more angular velocities in the rotation direction of the moving body, which is measured by an angular velocity sensor installed on the moving body. Or the diagnostic device according to 2. The diagnostic means determines whether or not, in the plurality of data acquired by the acquisition means, the number of data having the first threshold value or more is equal to or more than the predetermined number, and each of the plurality of data. Whether or not one or more data having a threshold value set for and equal to or higher than a second threshold value larger than the first threshold value set for each of the plurality of data exists in the plurality of data. The diagnostic device according to any one of claims 1 to 3, which diagnoses whether or not there is an abnormality in the location on the roadbed based on the above. The diagnostic device according to any one of claims 1 to 4, wherein the plurality of physical quantities include two physical quantities having different directions or rotation directions. The moving body is a vehicle that moves along the rail on the roadbed.
The diagnosis according to any one of claims 1 to 5, wherein the acquisition means acquires the plurality of data including data relating to the gauge deviation width of the rail acquired via the gauge sensor included in the sensor. Device. The diagnostic apparatus according to any one of claims 1 to 6, further comprising an output means for outputting information indicating the result of diagnosis by the diagnostic means. A plurality of data relating to a plurality of physical quantities corresponding to a location on the roadbed, which is determined based on sensor data measured by a sensor installed on the moving body when the moving body moves on the roadbed. Each of the data on the accelerations in different directions with respect to the moving body, the data on the angular velocities in the plurality of different rotation directions of the moving body, and the data on the tilt angles in the different rotation directions of the moving body, respectively. An acquisition means for acquiring the plurality of data including at least two or more of the data, and
Based on whether or not there are two or more predetermined numbers of data having a first threshold value or more set for each of the plurality of data among the plurality of data acquired by the acquisition means. As a diagnostic means for diagnosing whether or not there is an abnormality in the place on the roadbed,
System with. A method of controlling a diagnostic device relating 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. A plurality of data, each of data relating to accelerations in a plurality of different directions with respect to the moving body, data relating to angular velocities of a plurality of different rotation directions of the moving body, and a plurality of different rotation directions of the moving body. And the acquisition step of acquiring the plurality of data including at least two or more data of each of the data related to the inclination angle of
Based on whether or not there are two or more predetermined numbers of data that are equal to or greater than the first threshold value set for each of the plurality of data among the plurality of data acquired in the acquisition step. Then, a diagnostic step for diagnosing whether or not there is an abnormality in the location on the roadbed,
Control methods including. A program for causing a computer to function as each means of the diagnostic apparatus according to any one of claims 1 to 7.

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