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...

Examples

Embodiment Construction

[0047] The present invention will be further explained below in conjunction with the accompanying drawings.

[0048] like figure 1 As shown, a control system health status analysis method based on joint noise reduction and Empirical Mode Decomposition (EMD), by introducing a joint noise reduction method combining median filtering and improved wavelet threshold using inter-scale correlation, and using endpoint The method of combining continuation, ensemble empirical mode decomposition (EEMD) and correlation coefficient threshold comparison method extracts the state characteristics of the control system through four steps of median filtering, wavelet threshold noise reduction, empirical mode decomposition, and energy entropy calculation. Compared with the initial detected normal state entropy value, real-time judgment of the operating state of the control system includes the following specific steps:

[0049] Step 1) Perform median filtering on the discrete noisy signal f(k) to...

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...

Examples

Embodiment Construction

[0047] The present invention will be further explained below in conjunction with the accompanying drawings.

[0048] like figure 1 As shown, a control system health status analysis method based on joint noise reduction and Empirical Mode Decomposition (EMD), by introducing a joint noise reduction method combining median filtering and improved wavelet threshold using inter-scale correlation, and using endpoint The method of combining continuation, ensemble empirical mode decomposition (EEMD) and correlation coefficient threshold comparison method extracts the state characteristics of the control system through four steps of median filtering, wavelet threshold noise reduction, empirical mode decomposition, and energy entropy calculation. Compared with the initial detected normal state entropy value, real-time judgment of the operating state of the control system includes the following specific steps:

[0049] Step 1) Perform median filtering on the discrete noisy signal f(k) to...

Images