RAMS data monitoring method for railway vehicle

Abstract

Through real-time transmission and dynamic display monitoring of the RAMS data of the railway vehicle and reprocessing of the RAMS data, efficient, real-time and accurate monitoring of the RAMS data of the railway vehicle is realized, and the change trend of the RAMS of the railway vehicle is predicted in cooperation with real-time display of a chart curve and a fault tree model, so that the reliability of the RAMS data of the railway vehicle is improved. And timely maintenance and improvement decisions are made, so that the safety and efficiency of the rail transportation system can be improved.

Description

Technical Field

[0001] The invention belongs to the technical field of RAMS data monitoring, and in particular relates to a method for monitoring RAMS data of a rail vehicle. Background Art

[0002] Traditional rail vehicle RAMS data monitoring methods mainly rely on manual inspections and sensor data collection, which has problems such as incomplete data collection, poor real-time performance, and low data processing efficiency. The rapid development of big data technology has provided new ideas for solving these problems. Therefore, rail vehicle RAMS data monitoring methods based on big data technology have become a current research hotspot. Through big data technology, real-time monitoring and analysis of various data of rail vehicles can be achieved, and how to provide more reliable support for the safe operation and maintenance of rail vehicles based on these data is the main goal that needs further research. Summary of the invention

[0003] The present invention aims to solve the problems that the traditional rail vehicle RAMS data monitoring method mainly relies on manual inspection and sensor data collection, and has the problems of incomplete data collection, poor real-time performance, low data processing efficiency, etc., thereby providing a rail vehicle RAMS data monitoring method, through real-time transmission and dynamic display monitoring of the rail vehicle RAMS data, and reprocessing of the RAMS data, so as to achieve efficient, real-time and accurate monitoring of the rail vehicle RAMS data.

[0004] To achieve the above-mentioned object of the invention, the present invention provides a rail vehicle RAMS data monitoring method, comprising the following steps:

[0005] (1) The data platform for receiving, recording and providing data is connected to the computer group for rail vehicle RAMS data display and monitoring through a communication cable. The data platform is connected to the terminal device installed in each operating section of the rail vehicle through a transmission cable. The terminal device records the rail vehicle operation data. The operation data includes the traction motor current, traction force, speed, position, temperature, wheel axle vibration, wheel axle load, vehicle body tilt angle, car humidity, and car air quality in the traction system, braking system, network control system, and auxiliary power supply system. The computer group realizes wireless two-way connection with the monitoring terminal through wireless transceiver communication, and the monitoring terminal completes the transmission of fault alarm signals through a buzzer;

[0006] (2) After the data platform, computer group and terminal device are successfully connected and the monitoring frequency is set, the data processing unit installed in the computer group completes the collection of data transmitted by the data platform, and the data processing unit completes the real-time display and analysis of the RAMS initial data;

[0007] (3) After the RAMS data is parsed and processed and cleaned, format converted, and calibrated, it is transmitted to the fault tree model in the computer group. The fault tree model calculates the probability of intermediate events and bottom events of the rail vehicle or the probability of top events that endanger safety due to the components based on the transmitted data after RAMS data parsing;

[0008] (4) The output result of the fault tree model is connected with the automatic fault information prompt of the data analysis software in the computer group. The RAMS data is displayed in real time by selecting a chart or a chart curve display. If the chart or icon curve data changes beyond the set value, the data processing unit automatically prompts the fault information and uses the computer group communication to realize the transmission of the fault alarm signal.

[0009] Furthermore, the specific steps of real-time display and analysis of RAMS data in (2) are as follows:

[0010] a: After the data platform transmits the RAMS initial data to the computer group, the computer group copies the RAMS initial data and performs inductive processing on the traction system, braking system, network control system, and auxiliary power supply system according to the classified constraints;

[0011] b: The computer team uses the MapReduce programming model to write Mapper and Reducer functions for the summarized and classified data to complete the processing, conversion, aggregation and cleaning of RAMS data;

[0012] c: For the converted RAMS data, the computer team uses Tableau data analysis software to complete the real-time display of charts or chart curves, and uses neural networks to conduct in-depth analysis of the converted RAMS data.

[0013] Furthermore, the computer group includes a CPU module, a memory module, a power module, a communication module, a constraint induction module, an analytical processing module and a real-time display module.

[0014] Furthermore, the computer group and data platform both use 64-bit industrial-grade processors for receiving and recording RAMS data.

[0015] The present invention realizes efficient, real-time and accurate monitoring of the RAMS data of rail vehicles through real-time transmission and dynamic display monitoring of the RAMS data of rail vehicles and reprocessing of the RAMS data. In addition, the present invention cooperates with the real-time display of chart curves and the fault tree model to predict the changing trend of the RAMS of the rail vehicle and make timely maintenance and improvement decisions, which is helpful to improve the safety and efficiency of the rail transportation system and provide data support for the construction of urban rail transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of the monitoring process in the rail vehicle RAMS data monitoring method;

[0017] Figure 2 This is a flow chart of the monitoring method in the rail vehicle RAMS data monitoring method. DETAILED DESCRIPTION

[0018] In order to better understand the purpose, structure and function of the present invention, a rail vehicle RAMS data monitoring method of the present invention is further described in detail below with reference to the accompanying drawings.

[0019] Reference Figure 1 and Figure 2 The rail vehicle RAMS data monitoring method of the present invention comprises the following specific steps:

[0020] (1) The data platform for receiving, recording and providing data is connected to the computer group for rail vehicle RAMS data display and monitoring through a communication cable. The data platform is connected to the terminal device installed in each operating section of the rail vehicle through a transmission cable. The terminal device records the rail vehicle operation data. The operation data includes the traction motor current, traction force, speed, position, temperature, wheel axle vibration, wheel axle load, vehicle body tilt angle, car humidity, and car air quality in the traction system, braking system, network control system, and auxiliary power supply system;

[0021] (2) After the data platform, computer group and terminal device are successfully connected and the monitoring frequency is set, the data processing unit installed in the computer group completes the collection of data transmitted by the data platform, and the data processing unit completes the real-time display and analysis of the RAMS initial data;

[0022] (3) After the RAMS data is parsed and processed and cleaned, format converted, and calibrated, it is transmitted to the fault tree model in the computer group. The fault tree model calculates the probability of intermediate events and bottom events of the rail vehicle or the probability of top events that endanger safety due to the components based on the transmitted data after RAMS data parsing;

[0023] (4) RAMS data is displayed in real time by selecting a chart or chart curve display. If the chart or icon curve has obvious peaks or troughs, and the data change exceeds the set value, the data processing unit automatically prompts the fault information and uses computer group communication to realize the transmission of fault alarm signals.

[0024] Through real-time transmission and dynamic display monitoring of rail vehicle RAMS data and reprocessing of RAMS data, efficient, real-time and accurate monitoring of rail vehicle RAMS data can be achieved. In combination with real-time display of chart curves and fault tree models, the changing trend of the rail vehicle RAMS can be predicted. Timely maintenance and improvement decisions can be made, which helps to improve the safety and efficiency of the rail transportation system and provide data support for urban rail transit construction.

[0025] The terminal device is connected to the traction AC and brake control device in the rail vehicle through a communication cable. The traction AC and brake control device are equipped with brake sensors, traction sensors, displacement sensors and acceleration sensors to capture and record various status information of the rail vehicle.

[0026] The specific steps for real-time display and analysis of RAMS data in (2) above are as follows:

[0027] a: After the data platform transmits the RAMS initial data to the computer group, the computer group copies the RAMS initial data and performs inductive processing on the traction system, braking system, network control system, and auxiliary power supply system according to the classified constraints;

[0028] b: The computer team uses the MapReduce programming model to write Mapper and Reducer functions for the summarized and classified data to complete the processing, conversion, aggregation and cleaning of RAMS data;

[0029] c: For the converted RAMS data, the computer team uses Tableau data analysis software to complete the real-time display of charts or chart curves, and uses neural networks to conduct in-depth analysis of the converted RAMS data.

[0030] Time series analysis can be used to study the changing trends of track geometric and dynamic parameters; cluster analysis can be used to identify patterns and areas of track wear; regression analysis can be used to study the relationship between track status and driving safety and efficiency, etc.

[0031] The monitoring frequencies include 30ms, 50ms, 100ms and 200ms. To ensure the transmission of initial RAMS data of the terminal device, the computer group uses PTU software to complete data collection according to the set monitoring frequency.

[0032] When the monitoring frequency falls below a certain threshold, data transmission is too fast, which can easily cause the data platform and computer group to crash, resulting in data loss, data confusion, etc.

[0033] The computer group includes a CPU module, a memory module, a power module, a communication module, a constraint induction module, an analytical processing module and a real-time display module.

[0034] Both the computer group and the data platform use 64-bit industrial-grade processors to receive and record RAMS data.

[0035] The fault tree model includes an independent event E1 in the traction system, braking system, network control system, and auxiliary power supply system, and another independent event E2 in the traction system, braking system, network control system, and auxiliary power supply system. The failure rates are the probabilities of single failure and continuous failure under λ1 and λ2, respectively, where λ1 and λ2 are the module failure rates in any system.

[0036] For the fault tree model, it is assumed that the rail vehicle is within its effective life span and that correct maintenance work is carried out according to the regular maintenance procedures. According to statistical theory, it can be considered that the reliability of each component is distributed according to an exponential law, so the failure rate of a component at a certain moment is a constant value.

[0037] E1 is checked during the entire operation mission of the rail vehicle, and E2 is checked once in all N missions, which means that E1 must be checked when E2 is checked;

[0038] Then the interval time in a task cycle is T0=T1;

[0039] The time interval between two checks on E2 is E2 = nT0 = T2;

[0040] Therefore, in summary, if E1 and E2 fail at the same time during task i, the rail vehicle system will not be able to operate. Therefore, during the inspection period, the probability that E1 and E2 fail one after another is:

[0041] P1=λ1T0×λ2T0=λ1λ2T0 2

[0042] If E1 fails, before task i, E2 also fails, with probability:

[0043] P2=λ1T0×λ2(i-1)T0=λ1λ2T0 2 (i-1)

[0044] According to probability theory, the probability of task i is:

[0045] P=P1+P2==λ1λ2T0 2 [1+(i-1)]=λ1λ2T0 2 i

[0046] Between two consecutive repairs of E2, the total failure probability is:

[0047]

[0048] Therefore, the final time probability is:

[0049]

[0050] In the above formula, T0n=T2 is the time to check E2 twice;

[0051] T0=T1 is the time for two checks on E1;

[0052] Finally get

[0053] The computer group realizes wireless two-way connection with the monitoring terminal through wireless transceiver communication, and the monitoring terminal completes the transmission of fault alarm signals through a buzzer.

[0054] The result output of the fault tree model is connected with the automatic prompt fault information of the data analysis software in the computer group, and the fault alarm signal transmission is realized by using the computer group communication.

Claims

1. A method for monitoring RAMS data of a rail vehicle, characterized in that: The steps include: (1) The data platform for receiving, recording and providing data is connected to the computer group for rail vehicle RAMS data display and monitoring through a communication cable. The data platform is connected to the terminal device installed in each operating section of the rail vehicle through a transmission cable. The terminal device records the rail vehicle operation data. The operation data includes the traction motor current, traction force, speed, position, temperature, wheel axle vibration, wheel axle load, vehicle body tilt angle, car humidity, and car air quality in the traction system, braking system, network control system, and auxiliary power supply system. The computer group realizes wireless two-way connection with the monitoring terminal through wireless transceiver communication, and the monitoring terminal completes the transmission of fault alarm signals through a buzzer; (2) After the data platform, computer group and terminal device are successfully connected and the monitoring frequency is set, the data processing unit installed in the computer group completes the collection of data transmitted by the data platform, and the data processing unit completes the real-time display and analysis of RAMS initial data; (3) After the RAMS data is parsed and processed and cleaned, format converted, and calibrated, it is transmitted to the fault tree model in the computer group. The fault tree model calculates the probability of intermediate events and bottom events of the rail vehicle or the probability of top events that endanger safety due to the components based on the transmitted data after RAMS data parsing; (4) The output result of the fault tree model is connected with the automatic fault information prompt of the data analysis software in the computer group. The RAMS data is displayed in real time by selecting a chart or a chart curve display. If the chart or icon curve data changes beyond the set value, the data processing unit automatically prompts the fault information and uses the computer group communication to realize the transmission of the fault alarm signal.

2. A rail vehicle RAMS data monitoring method according to claim 1, characterized in that: The specific steps of real-time display and analysis of RAMS data in (2) are as follows: a: After the data platform transmits the RAMS initial data to the computer group, the computer group copies the RAMS initial data and performs inductive processing on the traction system, braking system, network control system, and auxiliary power supply system according to the classified constraints; b: The computer team uses the MapReduce programming model to write Mapper and Reducer functions for the summarized and classified data to complete the processing, conversion, aggregation and cleaning of RAMS data; c: For the converted RAMS data, the computer team uses Tableau data analysis software to complete the real-time display of charts or chart curves, and uses neural networks to conduct in-depth analysis of the converted RAMS data.

3. A rail vehicle RAMS data monitoring method according to claim 1, characterized in that: The computer group includes a CPU module, a memory module, a power module, a communication module, a constraint induction module, an analysis processing module and a real-time display module.

4. A rail vehicle RAMS data monitoring method according to claim 1, characterized in that: The computer group and data platform both use 64-bit industrial-grade processors for receiving and recording RAMS data.

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