Control system performer fault diagnosis method based on particle swarms and support vector machine

Abstract

The invention discloses a control system performer fault diagnosis method based on multi-swarm cooperative chaos simulated annealing particle swarm optimization-support vector machine (MCCSAPSO-SVM). The method comprises the steps of: by means of combined noise reduction and an improved empirical mode decomposition (EMD) method, carrying out feature extraction on collected output signals of a performer; utilizing MCCSAPSO to optimize structural parameters of the support vector machine; adopting a combined Kernel function to ensure good generalization capability and learning capability of the SVM; and utilizing training data to construct a binary tree SVM, and converting a complex polytomous problem into a plurality of dichotomous problems. According to the invention, common state signals, which can be easily obtained, of the control system are processed, whether the control system performer breaks down can be effectively judged in real time according to the output of the binary tree SVM, and the fault type can be accurately determined when the control system performer breaks down. The method is used for real-time fault diagnosis of a high-precision system.

Description

technical field [0001] The invention relates to a control system actuator fault diagnosis method based on multi-group cooperative chaotic simulated annealing particle swarm optimization algorithm-support vector machine (MCCSAPSO-SVM), belonging to the technical field of control system signal processing and actuator fault diagnosis. 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 aerospace, medical, large-scale mechanical production and other fields, subtle faults sometimes cause extremely serious economic losses and personal injuries, so the status monitoring and fault diagnosis of equipment operation has become an important research topic. Fault diagnosis of actuators in control systems can be divided into fault detection and fault isolation, which are two...

AI Technical Summary

Problems solved by technology

While combining the advantages of each single kernel function in the composition of the combined kernel function to obtain a support vector machine with better performance, there are still two problems as follows: 1) Due to the large number of kernel parameters of the combined kernel function, improper selection is very easy. The performance of the combined kernel function is not as good as that of a single kernel function; 2) When the type of single kernel function and the kernel parameters contained in the combined kernel function have been determined, the selection of the weight of each single kernel function will also affect the performance of the combined kernel function. performance impact

Among them, the particle swarm optimization algorithm (Particle Swarm Optimization, PSO) needs to adjust fewer parameters, and has the advantages of simple structure, easy implementation, and fast convergence speed. find the global optimal solution

Introducing the idea of ​​chaos into PSO not only improves the ability of the particle swarm optimization algorithm to jump out of local extreme points, but also improves the convergence speed and accuracy of the algorithm to a certain extent. Causes misjudgment of particle information exchange

The simulated annealing algorithm (Simulated Annealing, SA) uses the probabilistic jump characteristics to randomly search for the global optimal solution of the optimization function, and can also avoid falling into the local optimal solution during the search process, and has good global optimization performance, but because of the initial The value determines the performance of the algorithm to find the global optimal solution of the problem, so the algorithm also has certain limitations

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