Control System Health Analysis Method Based on Joint Noise Reduction and Empirical Mode Decomposition

Abstract

The invention discloses a control system health state analysis method based on combined noise reduction and empirical mode decomposition (EMD). A combined noise reduction method combining median filtering, interscale dependency, and threshold value and threshold value function wavelet threshold value improvement is introduced, so that the control system state signals including pulse noises and Gaussian random noises can be effectively suppressed; and for the signals after nose reduction, a method combining end extension, ensemble empirical mode decomposition (EEMD), and correlation coefficient threshold value comparison is proposed, so that the problems of end effects and mode mixing during the signal feature extraction only by EMD in the prior art can be effectively solved. The method includes performing staging process on common state signals easy to be acquired by a control system; analyzing the energy entropy obtained through calculation to obtain effective state feature information at the end; comparing the state feature information with the energy entropy at the initial normal state and rapidly determining the health state of the control system in real time; and providing accurate criterion for fault diagnosis, on-condition maintenance, and fault tolerance control of the control system. The method is applicable to the feature extraction and health state real-time detection of high-precision control system noised state signals.

Description

technical field [0001] The invention relates to a control system health state analysis method based on combined noise reduction and empirical mode decomposition (EMD), belonging to the technical field of control system signal processing and fault state detection. Background technique [0002] Due to the complex composition of modern control systems, they often need to work for a long time and with high loads under different environmental conditions, which inevitably leads to various failures in the control system. Especially in the fields of aerospace, medical treatment, and large-scale mechanical production, minor faults sometimes cause extremely serious economic losses and personal injuries. Therefore, the status monitoring and fault diagnosis of equipment operation have become important research topics. The prerequisite for ensuring the accuracy of condition monitoring and fault diagnosis is to obtain signal feature information that best represents the health status of eq...

AI Technical Summary

Problems solved by technology

The selection of wavelet and the number of wavelet decomposition layers are often selected through experience. At the same time, in terms of noise suppression, only the removal of Gaussian random noise is considered but the impact of impulse noise is not well eliminated.

[0004] Traditional health status detection is often a manual fault detection method after the equipment is shut down. It is difficult to accurately detect and judge that the control system is unhealthy in real time when the control system just has a little sign of failure but can still complete the control function. (failure) state

This method, which mostly relies on the operator's work experience to identify, has low diagnostic efficiency and is difficult to detect potential faults in time

A large number of downtime inspections not only waste a lot of manpower and time, but also cause many over-maintenance problems

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