JP2016224042A - Temperature anomaly detection system, and temperature anomaly detection method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To accurately detect a temperature anomaly generated in an underfloor apparatus installed on the underfloor of a railway vehicle.SOLUTION: A temperature anomaly detection system acquires an apparatus temperature value which is a temperature value of an underfloor apparatus, and accumulates the acquired apparatus temperature value. The correlation between the underfloor apparatuses is acquired from the apparatus temperature value to be accumulated, and a correlation coefficient is calculated based on an acquired correlation. The correlation coefficient is acquired from the apparatus temperature value to be accumulated with the apparatus temperature value of a determination object apparatus which is the underfloor apparatus desiring for the determination of the temperature anomaly as a determination temperature value. A proper temperature value is predicted from the apparatus temperature value based on the acquired correlation. The proper temperature value is a proper temperature value when the temperature anomaly is not generated in the determination object apparatus. When a deviation which is a value in which the determination temperature value deviates from the proper temperature value is larger than an anomaly determination threshold value as a result of the comparison between the determination temperature value and the proper temperature value, the anomaly determination for determining that the anomaly temperature is generated in the determination object apparatus is performed. An anomaly determination threshold value is the threshold value for determining that the temperature anomaly is generated in the determination object apparatus.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a technique for accurately detecting a temperature abnormality occurring in an underfloor device installed under the floor of a railway vehicle.

  In a vehicle that travels on a track such as a railway vehicle, axle bearings that rotatably support the shaft are respectively attached to both ends of the shaft that supports the wheel. As this axle bearing, there are a sliding bearing depending on liquid lubrication such as lubricating oil and a rolling bearing utilizing solid lubrication. In addition, since such an axle bearing is also generally called a shaft box including the structure which supports it, in the following description, it is suitably described as a shaft box.

  By the way, in such a shaft box, a large amount of heat is generated in order to rotatably support a shaft that rotates at a high speed during traveling. And the temperature of an axle box rises abnormally at the time of vehicle travel with the generated heat, and accidents, such as overheating and burning, may occur. For this reason, in order to prevent such overheating and burnout of the axle box, a technique for detecting an abnormal temperature of the axle box has been proposed.

  For example, in Patent Document 1, the surface temperature of the axle box of a vehicle that is running is measured, and it is determined from the measured surface temperature value of the axle box that the two are under the same conditions and the temperature tendency thereof. Select the surface temperature value of the axle box that is the same, and calculate the evaluation value to determine whether or not an abnormality has occurred in the axle box from the median value in the knitting of the surface temperature value of the selected axle box Furthermore, a technology for comparing the measured surface temperature values and evaluation values of all the axle boxes in order, and determining whether or not an abnormality has occurred in the axle box corresponding to the surface temperature value based on the comparison result Is described.

JP 2010-179706 A

  However, the technique described in Patent Document 1 has the following problems. That is, in the technique described in Patent Document 1, since an evaluation value is calculated based on the measured surface temperature value of the axle box, there is an influence of disturbance caused by sunlight incident on the axle box, wind and rain, snowfall, and the like. In this case, the surface temperature value of the axle box is affected by this disturbance, but the evaluation value calculated based on the surface temperature value of the axle box is also affected by this disturbance. For this reason, if a temperature abnormality is determined using an evaluation value that is affected by a disturbance, even if the equipment is normal, it may be erroneously determined as a temperature abnormality. There was a problem that it could not be detected well.

  The present invention has been made in view of such problems, and an object of the present invention is to provide a technique for accurately detecting a temperature abnormality occurring in an underfloor device installed under the floor of a railway vehicle. .

  The temperature abnormality detection system of the present invention made to solve the above problems is a temperature abnormality detection system that detects a temperature abnormality that occurs in an underfloor device installed under the floor of a railway vehicle, the device temperature value acquiring unit, A device temperature value storage unit, a correlation coefficient calculation unit, a determination temperature value acquisition unit, an appropriate temperature value prediction unit, and a temperature abnormality determination unit.

The device temperature value acquisition unit acquires a device temperature value that is a temperature value of the underfloor device.
The device temperature value storage unit stores the device temperature value acquired by the device temperature value acquisition unit.
The correlation coefficient calculation unit acquires the correlation between the underfloor devices from the device temperature value stored by the device temperature value storage unit, and calculates the correlation coefficient based on the acquired correlation.

The determination temperature value acquisition unit acquires, as a determination temperature value, the device temperature value of the determination target device that is an underfloor device that is desired to determine a temperature abnormality from the device temperature value stored in the device temperature storage unit.
The appropriate temperature value prediction unit predicts an appropriate temperature value from the device temperature value using the correlation coefficient calculated by the correlation coefficient calculation unit. The appropriate temperature value is an appropriate temperature value when no temperature abnormality has occurred in the determination target device.

  The temperature abnormality determination unit divides the determination temperature value from the appropriate temperature value by comparing the determination temperature value acquired by the determination temperature value acquisition unit and the appropriate temperature value predicted by the appropriate temperature value prediction unit. When the divergence value, which is a value, is greater than the abnormality determination threshold value, abnormality determination is performed to determine that a temperature abnormality has occurred in the determination target device. The abnormality determination threshold is a threshold for determining that a temperature abnormality has occurred in the determination target device.

  According to the temperature abnormality detection system of the present invention configured as described above, a temperature abnormality occurs in the determination target device that is the underfloor device for which it is desired to determine the temperature abnormality by a statistical method from the accumulated temperature value of the underfloor device. An appropriate temperature value when not being used is predicted as an appropriate temperature value, and abnormality determination is performed based on the predicted appropriate temperature value to determine that a temperature abnormality has occurred in the determination target device. For this reason, the appropriate temperature value used for determining the temperature abnormality is not easily affected by the disturbance, and when the abnormality determination is performed using such an appropriate temperature value, the temperature error is erroneously detected even though the device is normal. There is no risk of being judged.

Therefore, it is possible to accurately detect a temperature abnormality occurring in an underfloor device installed under the railcar.
The present invention can also be realized as a temperature abnormality detection method.

1 is a schematic configuration diagram showing a temperature abnormality detection system 1. FIG.

Embodiments of the present invention will be described below with reference to the drawings.
A temperature abnormality detection system 1 shown in FIG. 1 has a function of detecting a temperature abnormality that occurs in an underfloor device installed under the floor of a railway vehicle.

[1. Description of Configuration of Temperature Abnormality Detection System 1]
The temperature abnormality detection system 1 includes a device temperature sensor 11, a train (knitting) detection unit 12, a device part detection unit 13, a data processing unit 14, a storage unit 15, an inter-part correlation coefficient calculation unit 16, A temperature regression site selection unit 17, each site optimum regression temperature calculation unit 18, a comparison unit 19, and a temperature abnormality detection unit 20 are provided.

  The temperature abnormality detection system 1 is equipped with a known CPU, ROM, RAM, I / O that is an input / output circuit, and a bus line that connects these components, among which a CPU Functions as the data processing unit 14, the inter-part correlation coefficient calculation unit 16, the temperature regression part selection part 17, each part optimum regression temperature calculation part 18, the comparison part 19, and the temperature abnormality detection part 20, and the RAM is the storage part 15. Function as.

  Further, the CPU performs control according to control programs and data stored in the ROM and RAM. The ROM has a program storage area and a data storage area. A control program is stored in the program storage area, and data necessary for the operation of the control program is stored in the data storage area. In addition, the control program operates on the RAM in a form in which the work memory is a work area.

[1.1. Description of Configuration of Device Temperature Sensor 11]
The device temperature sensor 11 is disposed on the ground side where the track (track) is laid, corresponding to each side of the rail vehicle that is a train traveling on the track, and radiates underfloor device installed under the floor of the rail vehicle. Detects heat and measures the device temperature value, which is the temperature value of the underfloor device. More specifically, the device temperature sensor 11 has a configuration in which an infrared radiation thermometer is built in the housing, and the infrared radiation thermometer is adapted to the intensity of infrared rays (energy) emitted from an object having temperature. By detecting the amount), the temperature of the underfloor equipment of each vehicle of the railway vehicle passing through the track is measured without contact. Specific examples of the underfloor device include a shaft box and an axle.

  The device temperature sensor 11 starts the above-described measurement when a preset measurement start time is reached according to an instruction from the CPU, and ends the measurement when the preset measurement end time is reached. Then, the device temperature sensor 11 outputs the measured device temperature value to the data processing unit 14.

The device temperature sensor 11 corresponds to a device temperature value acquisition unit.
[1.2. Description of configuration of train (composition) detection unit 12]
The train (train) detection unit 12 detects the train number of the train passing through the track. Specifically, the train name is registered in an IC card affixed to the train body, information is received by an antenna (not shown) provided on the ground when the train passes, and the train name is detected by the train (train) detection unit. 12 is read.

[1.3. Description of Configuration of Device Part Detection Unit 13]
The device part detection unit 13 outputs a signal when the flange portion of the wheel comes close, and can accurately detect the passing timing of the wheel without contact. Examples of such a non-contact detection method include a high frequency induction method and a photoelectric method.

[1.4. Description of the configuration of the data processing unit 14]
The data processing unit 14 executes various calculations based on the output signal from the equipment temperature sensor 11, the output signal from the train detection part 12, and the output signal from the equipment part detection part 13, and the vehicle type of the passing train, the axle box Vehicle number, the axial position of the axle box, and whether the side on which the axle box is installed is discriminated between the sea side and the mountain side, and the judgment result is the inter-site correlation coefficient calculating unit 16 and the temperature regression site selecting unit 17. And it outputs to each part optimal regression temperature calculation part 18.

  In addition, the data processing unit 14 can exchange data with the storage unit 15, and outputs the device temperature value acquired by the device temperature sensor 11 to the storage unit 15 for storage, or the storage unit 15 stores the data. Various data to be read out and output to the inter-part correlation coefficient calculation unit 16, the temperature regression part selection unit 17, and each part optimal regression temperature calculation unit 18.

  Further, the data processing unit 14 acquires the device temperature value of the determination target device that is an underfloor device for which a determination of a temperature abnormality is desired from the device temperature value stored in the storage unit 15 as a determination temperature value. Then, the data processing unit 14 outputs the acquired determination temperature value to the comparison unit 19.

The data processing unit 14 corresponds to a determination temperature value acquisition unit.
[1.5. Description of Configuration of Storage Unit 15]
The storage unit 15 is used to store various information. For example, the storage unit 15 is used to accumulate device temperature values acquired by the temperature sensor 11. This device temperature value is stored in the form of a temperature vector (M ₁ , M ₂ ,... M _N ) in which device temperature values acquired at the same acquisition timing for the same vehicle are arranged. The device temperature value of each part from which the device temperature value is acquired is represented by M _i (i = 1 to N).

The storage unit 15 corresponds to a device temperature value storage unit.
[1.6. Explanation of Configuration of Inter-Part Correlation Coefficient Calculation Unit 16]
The inter-part correlation coefficient calculation unit 16 acquires the correlation between the underfloor devices from the device temperature values accumulated in the storage unit 15, and calculates the correlation coefficient based on the acquired correlation. That is, the inter-part correlation coefficient calculation unit 16 obtains the correlation by obtaining an N-dimensional straight line by performing regression analysis using the temperature vector stored in the storage unit 15 as a point in the N-dimensional space. The inter-part correlation coefficient calculation unit 16 obtains the coefficient a _i of the equipment temperature value Mi of each part and the offset temperature b _i of the equipment temperature value Mi of each part from the expression representing the N-dimensional straight line, and stores the storage part 15. To remember. This calculation is executed every time a predetermined number of data is accumulated or at a predetermined period.

Note that the inter-part correlation coefficient calculation unit 16 corresponds to a correlation coefficient calculation unit.
[1.7. Description of Configuration of Temperature Regression Site Selection Unit 17]
The temperature regression region selection unit 17 selects a region i where each region optimum regression temperature calculation unit 18 calculates the optimum regression temperature.

[1.8. Explanation of Configuration of Each Site Optimal Regression Temperature Calculation Unit 18]
Each part optimal regression temperature calculation unit 18 includes coefficients a _{1 to} a _N and offset temperatures b _{1 to} b _N obtained by the inter-part correlation coefficient calculation unit 16, and a determination temperature output from the data processing unit 14. Based on the temperature vectors (t ₁ , t ₂ ,... T _N ) as values, the optimal regression temperature, that is, the appropriate temperature values T _{1 to} T _N are calculated using the following equation (1). The appropriate temperature value is an appropriate temperature value when no temperature abnormality has occurred in the determination target device.

但し、
Ti：i位の最適回帰温度
a：部位毎の回帰係数
b：部位毎のパラメータ
N：軸数
t：当該部位の計測温度
である。軸数とは、機器温度値を取得する部位の数量（ここではＮ箇所）を示す。計測温度は、機器温度値を示す。

However,
Ti: Optimal regression temperature at position i
a: Regression coefficient for each part
b: Parameter for each part
N: Number of axes
t: The measured temperature of the relevant part. The number of axes indicates the number of parts from which the device temperature value is acquired (N places in this case). The measured temperature indicates a device temperature value.

Each region optimum regression temperature calculation unit 18 corresponds to an appropriate temperature value prediction unit.
[1.9. Description of Configuration of Comparison Unit 19]
Comparing unit 19 compares the temperature value t _i for determination output from the data processing unit 14, and a proper temperature value T _i predicted by respective portions optimal regression temperature calculation section 18.

The comparison unit 19 corresponds to a temperature abnormality determination unit.
[1.10. Description of Configuration of Temperature Abnormality Detection Unit 20]
The temperature abnormality detection unit 20 compares the determination temperature value t _i with the appropriate temperature value T _i by the comparison unit 19, and a deviation value that is a value at which the determination temperature value t _i deviates from the appropriate temperature value T _i is: If it is greater than the abnormality determination threshold, an abnormality determination is made to determine that a temperature abnormality has occurred in the determination target device. The abnormality determination threshold is a threshold for determining that a temperature abnormality has occurred in the determination target device. The temperature abnormality detection unit 20 performs the same comparison and abnormality determination for all the determination temperature values t _{1 to} t _N.

  When the temperature abnormality detection unit 20 determines that a temperature abnormality has occurred in the determination target device, the inter-part correlation coefficient calculation unit 16 thereafter stores the device temperature value accumulated in the storage unit 15. The correlation may be acquired by excluding the device temperature value acquired from the determination target device, and the correlation coefficient may be calculated based on the acquired correlation.

Further, the temperature abnormality detection unit 20 displays the determination result on a display device (not shown).
The temperature abnormality detection unit 20 corresponds to a temperature abnormality determination unit.
[2. Effects of the embodiment]
(1) As described above, according to the temperature abnormality detection system 1 of the present embodiment, the temperature abnormality is detected in the determination target device that is the underfloor device desired to determine the temperature abnormality by using a statistical method from the accumulated temperature value of the underfloor device. An appropriate temperature value when no occurrence has occurred is predicted as an appropriate temperature value, and an abnormality determination is made based on the predicted appropriate temperature value to determine that a temperature abnormality has occurred in the determination target device. For this reason, the appropriate temperature value used for determining the temperature abnormality is not easily affected by the disturbance, and when the abnormality determination is performed using such an appropriate temperature value, the temperature error is erroneously detected even though the device is normal. There is no risk of being judged.

Therefore, it is possible to accurately detect a temperature abnormality occurring in an underfloor device installed under the railcar.
In addition, according to the temperature abnormality detection system 1 of the present embodiment, it is possible to prevent troubles in the railway vehicle due to poor lubrication of the drive device and joints, and contribute to improving the safety of the railway vehicle.

  (2) Further, according to the temperature abnormality detection system 1 of the present embodiment, when it is determined by the temperature abnormality detection unit 20 that a temperature abnormality has occurred in the determination target device, the temperature abnormality is accumulated thereafter. Since the correlation is acquired by excluding the device temperature value acquired from the device to be determined among the device temperature values, the temperature fluctuation at the normal time is suppressed, and the highly accurate temperature abnormality determination becomes possible.

  (3) Moreover, according to the temperature abnormality detection system 1 of the present embodiment, when the correlation coefficient calculated by the inter-part correlation coefficient calculation unit 16 is equal to or greater than a predetermined value, the subsequent processing is not executed. In addition, temperature fluctuations at normal times are suppressed, and highly accurate temperature abnormality determination becomes possible.

[3. Other Embodiments]
As mentioned above, although one Embodiment of this invention was described, this invention is not limited to the said embodiment, It is possible to implement in the following various aspects.

  (1) The correlation of each part of the passing train may be dynamically monitored, and the detection method may be switched depending on the level of the correlation. For example, when the correlation between parts is high, the detection method described in Patent Document 1 may be used, and when the correlation between parts is low, a procedure such as using the method of this embodiment may be executed.

  DESCRIPTION OF SYMBOLS 1 ... Temperature abnormality detection system, 11 ... Equipment temperature sensor, 12 ... Train (composition) detection part, 13 ... Equipment part detection part, 14 ... Data processing part, 15 ... Memory | storage part, 16 ... Inter-part correlation coefficient calculation part, 17 ... temperature regression part selection part, 18 ... part optimal regression temperature calculation part, 19 ... comparison part, 20 ... temperature abnormality detection part.

Claims (4)

A temperature abnormality detection system for detecting a temperature abnormality that occurs in an underfloor device installed under the floor of a railway vehicle,
An apparatus temperature value acquisition unit for acquiring an apparatus temperature value that is a temperature value of the underfloor apparatus;
A device temperature value storage unit that stores the device temperature value acquired by the device temperature value acquisition unit, a correlation between the underfloor devices is acquired from the device temperature value stored by the device temperature value storage unit, and the acquired correlation A correlation coefficient calculator for calculating a correlation coefficient based on the relationship;
A determination temperature value acquisition unit that acquires the device temperature value of the determination target device that is the underfloor device desired to determine the temperature abnormality from the device temperature value stored in the device temperature storage unit as a determination temperature value;
Using the correlation coefficient calculated by the correlation coefficient calculation unit, an appropriate temperature value that is an appropriate temperature value when no temperature abnormality has occurred in the determination target device is predicted from the device temperature value. An appropriate temperature value prediction unit;
By the comparison between the determination temperature value acquired by the determination temperature value acquisition unit and the appropriate temperature value predicted by the appropriate temperature value prediction unit, the determination temperature value is a value that deviates from the appropriate temperature value. An abnormality that determines that a temperature abnormality has occurred in the determination target device when a certain divergence value is greater than an abnormality determination threshold that is a threshold for determining that a temperature abnormality has occurred in the determination target device A temperature abnormality determination unit for performing the determination;
A temperature abnormality detection system comprising: The temperature abnormality detection system according to claim 1,
When the temperature abnormality determination unit determines that a temperature abnormality has occurred in the determination target device, the correlation coefficient calculation unit includes the device temperature value stored in the device temperature value storage unit. A temperature abnormality detection system, wherein the correlation is obtained by excluding a device temperature value acquired from a determination target device, and a correlation coefficient is calculated based on the acquired correlation. A temperature abnormality detection method for detecting a temperature abnormality that occurs in an underfloor device installed under the floor of a railway vehicle,
Obtain an equipment temperature value that is the temperature value of the underfloor equipment,
Accumulate the acquired device temperature value,
Obtain the correlation between the equipment under the floor from the accumulated equipment temperature value, calculate the correlation coefficient based on the obtained correlation,
The device temperature value of the determination target device that is the underfloor device for which it is desired to determine the temperature abnormality is acquired as the determination temperature value from the accumulated device temperature value,
Using the calculated correlation coefficient, predicting an appropriate temperature value that is an appropriate temperature value when no temperature abnormality has occurred in the determination target device from the device temperature value,
By comparing the acquired temperature value for determination with the predicted appropriate temperature value, a deviation value, which is a value that deviates from the appropriate temperature value, results in a temperature abnormality in the determination target device. A temperature abnormality detection method comprising: performing abnormality determination that determines that a temperature abnormality has occurred in the determination target device when the abnormality determination threshold value is larger than an abnormality determination threshold value that is a determination threshold. In the temperature abnormality detection method according to claim 3,
When it is determined that a temperature abnormality has occurred in the determination target device, the correlation is acquired by excluding the device temperature value acquired from the determination target device from the accumulated device temperature values. A temperature abnormality detection method characterized by calculating a correlation coefficient based on the acquired correlation.

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