CN113884239B - Train braking system pipeline leakage diagnosis method based on TCMS data - Google Patents
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
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- G01M3/2807—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors for pipes, cables or tubes; for pipe joints or seals; for valves ; for welds for pipes
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- G01M3/28—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors for pipes, cables or tubes; for pipe joints or seals; for valves ; for welds
- G01M3/2807—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors for pipes, cables or tubes; for pipe joints or seals; for valves ; for welds for pipes
- G01M3/2815—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors for pipes, cables or tubes; for pipe joints or seals; for valves ; for welds for pipes using pressure measurements
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Abstract
The invention provides a train brake system pipeline leakage diagnosis method based on TCMS data. The invention saves labor cost, does not need to add equipment cost, and has certain noise fault tolerance.
Description
Technical Field
The application belongs to the technical field of brake system maintenance.
Background
The train braking system is one of important components of the train, and once the train braking system fails, serious railway accidents such as train running in and rear-end collision are most likely to be caused; the brake system has many pipelines and pipe joints, so potential leakage faults are easy to occur, and after the leakage faults occur, the performance of the brake system is reduced, and the train operation is influenced. Therefore, maintenance of the train brake system pipe is required to find the existence or potential of a brake system pipe leak. At present, aiming at the diagnosis of the pipeline leakage of the brake system, manual overhaul or pressure maintaining test and other methods are mostly adopted, wherein the manual overhaul of the pipeline leakage of the brake system is mainly included in daily inspection projects, and the methods of ear hearing, hand touch, soapy water and the like are adopted, so that the method is original in comparison, low in efficiency, high in labor cost, and high in required maintainer experience, and the possible omission problem of the manual overhaul cannot be eliminated; the pressure maintaining test judges whether leakage occurs or not through the pressure drop rate under the static working condition of the train, and positions the section of pipeline where the leakage occurs through opening and closing of the plug door. Although the method for testing can detect leakage accurately, the operation time of the urban rail transit train is long, the time for returning to the warehouse and human resources are required for carrying out the test, and even the operation scheduling of the train can be influenced. The patent application CN112141069a adopts predictive maintenance technology to perform overall evaluation on the performance of the braking system by additionally configuring the train with an edge device of the health management host, and additionally installing an additional sensor. However, this solution requires additional equipment, which increases the cost, and is not suitable for configuration on the already operated line due to the longer modification period of the additional equipment. And the implementation mode of the air supply system performance early warning module in the document is as follows: and recording total wind pressure and time before the power failure of the motor train unit, and judging after the power is on again, if the current power failure time is smaller than the total wind pressure complete leakage time of the train, recording the total wind pressure descending rate, and giving an early warning on the leakage condition of the whole train pipeline. The early warning scheme needs to power off the train and can affect the train operation.
Disclosure of Invention
The invention aims to: in order to solve the problems in the prior art, the invention provides a train brake system pipeline leakage diagnosis method based on TCMS data.
The technical scheme is as follows: the invention provides a train brake system pipeline leakage diagnosis method based on TCMS data, which specifically comprises the following steps:
step 1: the method comprises the steps of periodically collecting TCMS data of a train, initializing and anti-interference processing the TCMS data, and calculating the braking frequency of a train braking cylinder, the working rate of an air compressor and the pressure maintaining rate of the braking cylinder in the current period based on the processed TCMS data; establishing a three-dimensional coordinate system by taking the braking frequency of a braking cylinder as a y axis, the working rate of an air compressor as a z axis and the pressure maintaining rate of the braking cylinder as an x axis; drawing the braking frequency of the train braking cylinder in the current period, the working rate of the air compressor and the pressure maintaining rate of the braking cylinder into a three-dimensional coordinate system to serve as three-dimensional characteristic points of a train braking system pipeline in the current period; the TCMS data includes: total jump number sigma N of braking state of random one braking cylinder in train in sampling period brake Total time of operation T of braking system of train in sampling period all Total working time sigma T of all air compressors in train in sampling period fill Total time of pressure in all brake cylinders in train in sampling period
Step 2: judging whether the three-dimensional characteristic points of the current period are abnormal characteristic points according to the isolated forest model, if not, taking the characteristic points as effective values of the current period, and turning to the step 3; otherwise, deleting the three-dimensional characteristic point of the current period, taking the effective value of the previous period as the effective value of the current period, and converting to the step 3;
step 3: the TCMS data corresponding to the current period and the effective values of the first N periods of the current period are formed into a group of current TCMS data groups; and constructing the following linear regression model:
wherein a, B, C are parameters, const=1;
fitting a linear regression model by using the current TCMS data set and preset historical data, and if the B value obtained after fitting the linear regression model by using the current TCMS data set exceeds a preset B value range and the B value obtained after fitting the linear regression model by using the previous Q TCMS data set of the current TCMS data set also exceeds the preset B value range, considering that a leakage fault exists in a brake pipeline of the train;
if the C value obtained after the current TCMS data set is fitted to the linear regression model exceeds a preset C value range and the C value obtained after the previous Q TCMS data sets of the current TCMS data set are fitted to the linear regression model also exceeds the preset C value range, the leakage fault of the total air pipeline of the train is considered.
Further, the expression of the braking frequency of the train brake cylinder is as follows:
the expression of the work rate of the air compressor is as follows:
the expression of the pressure maintaining rate of the brake cylinder is as follows:
further, the obtaining the preset B value range and the preset C value range in the step 3 specifically includes: acquiring TCMS data in a train braking system pipeline health state, initializing and performing anti-interference treatment on the TCMS data, obtaining a plurality of three-dimensional characteristic points according to the treated TCMS data, drawing the three-dimensional characteristic points under a three-dimensional coordinate system in the step 1, and fitting a linear plane, wherein the linear plane Z=A x+B x+y+C; x represents the x-axis and y represents the y-axis, so that the B value range is preset based on the value of B in the linear plane and the C value range is preset based on the value of C in the linear plane.
Further, the period length of the sampling period is 1 day.
The beneficial effects are that:
1. according to the invention, pipeline leakage detection is periodically carried out based on the designed data model, so that the labor cost is saved; according to the invention, by a data driving method, on the basis of not adding a sensor, the leakage detection is carried out by accessing train TCMS data, so that the equipment cost is not increased; meanwhile, the invention has certain noise fault tolerance, because the running state of the train is inconsistent every day, the running state of the train can be likely to be on a line and in a warehouse every day, so that the actual air consumption per day cannot be compared independently, and a mapping relation among a plurality of physical quantities needs to be established. Therefore, the working rate, the braking frequency and the pressure maintaining rate of the braking cylinder of the air compressor are selected as model features for modeling. TCMS data is transmitted by 4G in a ground, transmission loss can be caused by the quality of 4G signals in actual data, and great data loss occurs, so that the data length which can be acquired every day of each train is inconsistent, and the influence of the absolute length of the data can be eliminated by adopting the concept of 'rate' correlation as a model characteristic.
2. According to the invention, the two types of leakage of the brake total air pipeline leakage and the brake cylinder and the connecting pipeline leakage are mixed and modeled, and the leakage phenomena occurring at different positions are represented by using different parameters of the model, so that the problem that the two types of leakage are incapable of respectively considering and modeling due to stronger coupling phenomena existing in mechanism is avoided.
Drawings
FIG. 1 is a schematic diagram of the flow of air supply and braking system signals;
FIG. 2 is a schematic diagram of the logic structure of the method of the present invention;
FIG. 3 is a simplified schematic diagram of a wind supply and braking system according to the present invention;
FIG. 4 is a schematic diagram of a feature point distribution provided by the present invention;
FIG. 5 is a schematic view of a feature point distribution and fitting plan provided by the present invention;
fig. 6 is a schematic diagram of the actual vehicle data result according to the present invention.
Detailed Description
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
For urban rail trains, the signal flow directions of the air supply and braking systems are shown in fig. 1, in the braking system, a main air pipe is a pipeline connecting a main air cylinder with the braking system and other air consuming equipment, the main air cylinder is inflated by an air source system to obtain and store pressure air, and the pressure air is supplied to all air consuming modules on the train, and the urban rail train comprises: wind for a braking system, wind for an air spring, wind for a pneumatic sliding plug door, waste exhaust of an air conditioner, pantograph lifting, air cooling of a high-temperature element and the like.
The leakage failure of the total air pipe is probably due to the limited sealing performance of the pipe, the rubber element and the pipe joint, the leakage of the pressure air can be generated in the long-time service process, and the final result of the leakage failure can lead to the acceleration of the pressure drop rate of the total air cylinder, thereby leading to the frequent start and stop of the air compressor.
The leakage fault of the brake cylinder and the connecting pipeline is probably caused by the fact that the pressure maintaining performance of the valve and the sealing performance of the pipeline joint are reduced in the pressure maintaining process of the brake cylinder, but because the pressure of the brake cylinder is used as a controlled quantity, if leakage occurs in the actual operation process to generate pressure drop, the BCU can automatically perform pressure control to supplement air, and the air supplementing process is not represented by a corresponding instruction signal in the TCMS signal, so that effective leakage characteristics cannot be extracted in the brake cylinder pressure signal. Therefore, the mechanism analysis is carried out on the leakage phenomenon of the brake pipeline, the source is traced to the air supply system, and the leakage of the brake pipeline can also directly lead to the increase of the air consumption of the whole train, so that the air compressor is started and stopped more frequently.
Based on the above-mentioned thought, as shown in fig. 2, the present embodiment provides a method for diagnosing leakage of a train brake system pipeline based on TCMS data, wherein TCMS data firstly enters the interior of a model through an initialization and anti-interference module, and then through data processing, state monitoring, health evaluation and fault early warning four-layer structure, the characteristic parameters of the working state of the air supply/brake system are evaluated through three items of statistics of the working rate of an air compressor, the braking frequency and the pressure maintaining rate of a brake cylinder, and the leakage items of a total air pipeline, the brake cylinder and the connected pipeline are decoupled, so that the diagnosis of the leakage of the two types of pipelines is completed and distinguished. The embodiment specifically comprises the following steps:
1. mechanism model simplification
According to the mechanism analysis of the air supply and braking systems, whether the total air pipeline is leaked or the braking cylinder and the connecting pipeline are leaked, the air compressor working time is increased, and the characteristics of the total air pipeline leakage and the braking pipeline leakage are required to be respectively represented through different characteristics so as to position the leakage. Therefore, the model is built by analyzing the mechanism relation between the start and stop of the air compressor, the pressure air consumed in the braking process and the pressure air leaked by the start and stop of the air compressor.
In summary, the whole brake system and air supply system are abstracted into a topological structure as shown in fig. 3
2. Selecting relevant features
Firstly, constructing model features, and selecting the following three index parameters as three-dimensional reconstruction features through understanding business and data:
air compressor machine operating rate, braking frequency, brake cylinder pressurize rate.
The mapping relation between the two can reflect the air consumption and leakage of the whole vehicle. And the root cause of the increase of the air consumption of the whole vehicle can be distinguished as the leakage of the total air pipeline or the leakage of the brake cylinder and the connecting pipeline.
The three-dimensional reconstruction feature calculation method comprises the following steps of:
1) The air compressor work rate: total working time sigma T of all air compressors (two in this embodiment) in train fill Total time T of operation of braking system all Is a ratio of (2);
2) Braking frequency of the brake cylinder: optionally a brake cylinder, the number of jumps of the brake state of the brake cylinder is calculated brake Total time T of operation of braking system all Is a ratio of (2);
3) Brake cylinder dwell rate: total time of pressure in any brake cylinder and total time T of brake system operation all Is characterized by comprising the following components in percentage by weight:
b stat =1 indicates that there is pressure in the brake cylinder.
And establishing a three-dimensional coordinate system by taking the braking frequency of the braking cylinder as a y axis, the working rate of the air compressor as a z axis and the pressure maintaining rate (the duration proportion of the braking state) of the braking cylinder as an x axis.
Since the leakage belongs to a slightly slowly varying fault form, the characteristic is possibly submerged in the data noise or the problem caused by the data quality in the short period, the characteristic cannot be effectively represented on the data, and since the daily working state of the train is uncertain, the data statistics period is put to one day, namely, the data output of each train is a point (characteristic point) in a three-dimensional space, and the range of the health domain is determined through the calibration of the historical data. The distribution of the feature points in three-dimensional space is shown in fig. 4.
3. Leak detection based on calculated characteristics
1) Abnormality detection stage:
firstly, an isolated forest model is established to detect the abnormality, and whether the three-dimensional characteristic points of a certain day to be diagnosed exceed the normal data boundary due to abnormal transmission, abnormal working conditions and the train in a debugging state is judged. If the point does not exceed the boundary, the point is an effective value, and the effective value is reserved; if it exceeds the boundary, the data is discarded and the most recent valid value for the three-dimensional feature point is used as the three-dimensional feature point for the day of the required diagnosis.
2) Parameter regression calibration:
the TCMS data corresponding to the current period and the effective values of the first N periods of the current period are formed into a set of current TCMS data sets, in this embodiment n=10
A statistical model is built for the air supply system and the braking system, and the model formula is as follows
In the Const representing time period, the ratio of the total air charge quantity and the air consumption quantity of the braking system is a fixed value, and 1 is taken here; a, B, C are three-dimensional space plane parameters which need to be calibrated by actual data, namely, a plane with the shape of z=A x+B x y+C is calibrated in space, and the three-dimensional space plane parameters are used for representing the mapping relation among the variables. The fitted plane is shown in fig. 5.
Wherein, A can represent the average air consumption for braking once in a unit time of each air charge A;
b can represent the average air quantity which can be supplied to leakage of a brake cylinder and a connected pipeline in unit time in B units of air charge, and the larger the value is, the more serious the leakage phenomenon of the brake pipeline is;
and C can represent the average air quantity which can be supplied to the leakage of the total air pipeline in one unit time in C units per unit time of air filling, and the larger the value is, the more serious the leakage phenomenon of the total air pipeline is.
In particular implementations, the fitting of the decision plane may be performed using historical data and data from a recent period of time (e.g., the last 10 days). In principle, only the last 10 days of data can be used for fitting, but the fitting needs to be performed in combination with the historical data because the points of the fitting plane are fewer, resulting in larger parameter fluctuations.
3) Fault reasoning stage:
if the leakage of the total air pipeline needs to be judged, namely the time sequence change of the C value is judged, and if the C value is always a larger value in a period of time, the leakage of the total air pipeline is judged.
If the leakage of the brake cylinder and the connected pipeline needs to be judged, namely, the time sequence change of the B value is judged, and if the B value is always a larger value in a period of time, the leakage of the brake cylinder and the connected pipeline is judged.
4. Model evaluation verification
The model evaluates all operation data of 11 trains for one month by using a certain operation subway line, firstly, carrying out statistics on all the data in a three-dimensional space and fitting a linear plane:
the fitting plane parameters are:
Z＝13.2314*X+0.01858*Y+0.05499
13.2314 is A in a model formula, and represents the average air consumption rate of 13.2314 units of air charge for one brake;
0.01858 is B in the model formula, and represents the average air quantity which can be supplied to leakage of a brake pipeline in unit time for 0.01858 units of time per air charge;
0.05499 is C in the model formula, and represents the average air volume which can be supplied to the leakage of the total air pipeline in unit time for 0.05499 units of air filling time;
all parameters of the fitting are reasonable in terms of physical meaning and size relationship.
The real-time data of a certain train is then adopted for fitting, and the curve of the change of the B value of the model with time is shown in fig. 6. It can be found that the model B value starts to exceed the alarm threshold at 9/16 days and remains high at 17 and 18 days, and it can be presumed that the brake cylinder and the connecting pipeline of the train have a high probability of leakage failure at 9/16 days.
In addition, the specific features described in the above embodiments may be combined in any suitable manner without contradiction. The various possible combinations of the invention are not described in detail in order to avoid unnecessary repetition.
Claims (3)
1. A train brake system pipeline leakage diagnosis method based on TCMS data is characterized by comprising the following steps:
step 1: the method comprises the steps of periodically collecting TCMS data of a train, initializing and anti-interference processing the TCMS data, and calculating the braking frequency of a train braking cylinder, the working rate of an air compressor and the pressure maintaining rate of the braking cylinder in the current period based on the processed TCMS data; establishing a three-dimensional coordinate system by taking the braking frequency of a braking cylinder as a y axis, the working rate of an air compressor as a z axis and the pressure maintaining rate of the braking cylinder as an x axis; drawing the braking frequency of the train braking cylinder in the current period, the working rate of the air compressor and the pressure maintaining rate of the braking cylinder into a three-dimensional coordinate system to serve as three-dimensional characteristic points of a train braking system pipeline in the current period; the TCMS data includes: total jump number sigma N of braking state of random one braking cylinder in train in sampling period brake Total time of operation T of braking system of train in sampling period all Total working time sigma T of all air compressors in train in sampling period fill Total time of pressure in all brake cylinders in train in sampling period
Step 2: judging whether the three-dimensional characteristic points of the current period are abnormal characteristic points according to the isolated forest model, if not, taking the characteristic points as effective values of the current period, and turning to the step 3; otherwise, deleting the three-dimensional characteristic point of the current period, taking the effective value of the previous period as the effective value of the current period, and converting to the step 3;
step 3: the effective values of the current period and TCMS data corresponding to the effective values of the first N periods of the current period are formed into a group of current TCMS data groups; and constructing the following linear regression model:
wherein a, B, C are parameters, const=1;
fitting a linear regression model by using the current TCMS data set and preset historical data, and if the B value obtained after fitting the linear regression model by using the current TCMS data set exceeds a preset B value range and the B value obtained after fitting the linear regression model by using the previous Q TCMS data set of the current TCMS data set also exceeds the preset B value range, considering that a leakage fault exists in a brake pipeline of the train;
if the C value obtained after the current TCMS data set is fitted to the linear regression model exceeds a preset C value range and the C value obtained after the previous Q TCMS data sets of the current TCMS data set are fitted to the linear regression model also exceeds the preset C value range, the leakage fault of the total air pipeline of the train is considered;
the preset value range of B and the preset value range of C obtained in the step 3 are specifically: acquiring TCMS data in a train braking system pipeline health state, initializing and performing anti-interference treatment on the TCMS data, obtaining a plurality of three-dimensional characteristic points according to the treated TCMS data, drawing the three-dimensional characteristic points under a three-dimensional coordinate system in the step 1, and fitting a linear plane, wherein the linear plane Z=A x+B x+y+C; x represents the x-axis and y represents the y-axis, so that the B value range is preset based on the value of B in the linear plane and the C value range is preset based on the value of C in the linear plane.
2. The TCMS data-based train brake system line leakage diagnosis method according to claim 1, wherein the expression of the brake frequency of the train brake cylinder is as follows:
the expression of the work rate of the air compressor is as follows:
the expression of the pressure maintaining rate of the brake cylinder is as follows:
3. the TCMS data based train brake system line leakage diagnostic method according to claim 1, wherein the period length of the sampling period is 1 day.
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