Diagnosis method for sensor faults of motor train unit braking system

Abstract

The invention discloses a diagnosis method for sensor faults of a motor train unit braking system. The method includes: subjecting collected historical sensor signals of a motor train unit to EEMD (ensemble empirical mode decomposition) processing, and creating energy feature vectors of the historical sensor signals; training an FDA (fisher discriminant analysis) model according to the energy feature vectors so as to obtain FDA model parameters; colleting on-line sensor test signals, subjecting the on-line sensor test signals to EEMD processing, and creating energy feature vectors of the on-line sensor test signals; computing FDA score vectors of the energy feature vectors of the sensor test signals according to a projection matrix in the FDA model parameters; classifying the FDA score vectors based on the parameters of the FDA model, and determining fault categories of the on-line test signals. By the method, the defect of modal aliasing effect in the EMD method is overcome, signal features can be extracted effectively, the single FDA model is used for fault classification, and complexity of the SVM (support vector machine) based fault classification algorithm is lowered.

Description

technical field [0001] The invention relates to the field of industrial monitoring and fault diagnosis, in particular to a fault diagnosis method for a sensor of a brake system of an EMU. Background technique [0002] The brake system of EMU is of great significance to ensure the safe operation of trains. The EMU brake system usually includes two parts: the air brake system and the electric brake system, the latter of which is the traction drive system working in the braking state. Sensors are key components in the hardware of the braking system, mainly including speed sensors, shaft temperature sensors, and pressure sensors for measuring various valves. Sensors are the source of information acquisition, and their measurement results directly affect the operation of the system. In a typical closed-loop system such as the EMU braking control system, the performance of various sensors directly determines whether the EMU can complete the required braking tasks in a timely and...

AI Technical Summary

Problems solved by technology

[0004] However, there is a modal aliasing effect in the extraction of signal features based on the EMD method, and training multiple support vector machines increases the complexity of the algorithm

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