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Robust filtering method and system for a key part of motor train unit braking system

A braking system and robust filtering technology, applied in the field of signal processing, can solve problems such as robustness cannot meet practical applications

Active Publication Date: 2017-03-22
TSINGHUA UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] One of the technical problems to be solved by the present invention is that the robustness of the filtering of the key components of the EMU braking system in the existing robust filtering method can no longer meet the needs of practical applications

Method used

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  • Robust filtering method and system for a key part of motor train unit braking system
  • Robust filtering method and system for a key part of motor train unit braking system
  • Robust filtering method and system for a key part of motor train unit braking system

Examples

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no. 1 example

[0091] figure 1 It is a flow chart of the robust filtering method for the key components of the EMU brake system according to the first embodiment of the present invention, refer to below figure 1 , detailing each step.

[0092] Step S110, establishing a soft sensor model of the key components of the brake system of the EMU under the condition that the resolution of the sensor is limited, and there is stochastic model uncertainty in the soft sensor model.

[0093] In the actual application process, the online monitoring of the operating status of the key components of the brake system of the EMU will have strong random model uncertainty and limited sensor resolution. However, the existing robust filtering technology only Known systems perform filtering and do not take into account the limited sensor resolution. In order to improve the filtering robustness of the key components of the braking system of the EMU, the soft sensor model of the key components of the braking system...

no. 2 example

[0174] In this embodiment, the soft sensor model of the key components of the brake system of the EMU is first established when the resolution of the sensor is limited. The specific modeling process is as follows:

[0175] After 4 experiments, the ideal measurement output data and the actual measurement output data of the key components of the braking system of 4 sets of EMUs were collected respectively.

[0176] Calculate according to the ideal measurement equation of the ideal sensor in step S220 as Figure 4 The ideal measurement output curve of the key components of the brake system of the EMU is shown, and then measured as Figure 5 The actual measured output curves of the key components of the brake system of the EMU are shown.

[0177] Calculate the ideal measurement output second-order moment of the ideal sensor based on the above data, see expression 16 for details,

[0178] Expression sixteen:

[0179] According to the ideal sensor, the ideal measurement outputs...

no. 3 example

[0196] Based on the same inventive concept, the embodiment of the present invention also provides a robust filtering system for key components of the EMU braking system. Since the problem-solving principle of these devices is similar to a robust filtering method for key components of the EMU braking system, Therefore, the implementation of these devices can refer to the implementation of the method, and repeated descriptions will not be repeated.

[0197] Figure 8 It is a structural schematic diagram of a robust filter system, a key component of the brake system of an EMU according to the second embodiment of the present invention, and each component of the system will be described in detail below according to the figure.

[0198] The soft-sensing module 810 is used to establish a soft-sensing model of the key components of the EMU brake system when the resolution of the sensor is limited, and there is random model uncertainty in the soft-sensing model;

[0199] The first de...

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Abstract

The invention discloses a robust filtering method and system for a key part of a motor train unit braking system, and the method comprises the steps: building a soft measurement model of the key part of the motor train unit braking system under the condition that the resolution of a sensor is limited, wherein the soft measurement model has random model uncertainty; determining a filter model of the key part of the motor train unit braking system according to a state equation and the soft measurement model of the key part of the motor train unit braking system, a current control input signal of the key part of the motor train unit braking system and a current measurement output single of the sensor; carrying out the state estimation through the filter model, and determining a state estimation correlation error. Therefore, the method can be independent of a model uncertainty structure, carries out filtering of the key part of the motor train unit braking system, improves the filtering robustness of the key part of the motor train unit braking system, and effectively guarantees application demands of the online monitoring of the operation state of the key part of the motor train unit braking system.

Description

technical field [0001] The invention relates to the technical field of signal processing, in particular to a method and system for robust filtering of key components of an EMU braking system. Background technique [0002] The stable and reliable operation of the braking system of EMUs is very important to ensure the safety of trains. Any type of failure in the EMU brake system may lead to catastrophic consequences and cause huge loss of life and property. Therefore, in order to improve the safety of the EMU braking system, it is necessary to monitor the operating status of its key components in real time. [0003] The sensor is the main way to obtain the operation information of the key components of the braking system, and is the basis of filtering. It usually consists of four parts: sensitive element, conversion element, conversion circuit and auxiliary power supply. The performance index of the sensor generally includes accuracy, resolution, sensitivity, etc. In the a...

Claims

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Application Information

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IPC IPC(8): G01M17/08G06K9/00
CPCG01M17/08G06F2218/02
Inventor 周东华张峻峰何潇
Owner TSINGHUA UNIV
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