Research On Fault Diagnosis Of Aviation Hydraulic Pipeline-clamp Based On Improved Kernel Principal Component Analysis

Aviation hydraulic pipeline is an important channel for power transmission of aviation hydraulic system,but because it is in a complex and unstable vibration environment for a long time,pipeline and clamp and other parts are prone to cumulative fatigue damage failure.Therefore,the research on the fault diagnosis method of aviation hydraulic pipeline-clamp is an important guarantee for the safe and stable operation of aviation hydraulic system.It has important scientific research significance and engineering application value.In this thesis,aviation hydraulic pipeline-clamp as the research object,in order to accurately identify fault types from pipeline-clamp vibration signals,by referring to the discriminant function of Fisher criterion in pattern recognition,the GA-PSO hybrid algorithm was used to optimize the selection of kernel parameters in the combined kernel function,and the improved kernel principal component analysis was obtained.Through the vibration experiment of aviation hydraulic pipeline-clamp fault and frequency domain analysis in time,a fault identification and diagnosis method of aviation hydraulic pipeline-clamp fault was proposed based on the combination of nuclear extreme learning machine model optimized by whale optimization algorithm,and it was verified that the method could effectively realize the accurate identification of aviation hydraulic pipeline-clamp fault type.Firstly,the vibration signal processing and feature extraction methods of aviation hydraulic pipeline-clamp are studied,the main principles of kernel principal component analysis and the selection of kernel function of kernel principal component analysis are analyzed,and the two algorithms of genetic algorithm and particle swarm optimization algorithm are integrated to obtain GA-PSO hybrid algorithm.The kernel parameters in the combined kernel function were optimized,and the improved kernel principal component analysis(GAKPCA)was obtained.Through data feature visualization,the selected model was reflected to have good feature extraction ability,and the feasibility of the proposed feature extraction method was verified.Secondly,the hydraulic pipeline vibration test platform was used to carry out the vibration test of aviation hydraulic pipeline-clamp foundation,and the vibration test scheme of the hydraulic pipeline fixed clamp was designed.The vibration test was carried out on the health state and three kinds of fault states of the fixed clamp,and the test data of different clamp states under different pipelines were collected.The variation characteristics of different types of pipeline vibration signals are explored,and the limitations of time-frequency domain analysis are difficult to accurately judge when multiple faults occur.At the same time,KPCA based on GA-PSO fusion algorithm is proposed to optimize the parameters of the combined kernel function to analyze the vibration signal of the clamp,which verifies that the proposed method can map and extract the vibration data of the clamp efficiently and accurately,and has a good identification effect for different fault states of the clamp under different pipelines.It provides good data support for aviation hydraulic pipeline fault identification and diagnosis.Finally,the aviation hydraulic pipeline extracts the time-frequency domain features of the clamp signal under different states,and extracts 13 time-domain features under the time-domain feature and 4 frequecy-domain features under the frequecy-domain feature to form the original feature data set.The obtained time-domain and frequecy-domain feature data set is imported into the feature extraction model,and the feature data set processed by GPKPCA is obtained.Finally,it was imported into the nuclear ultimate Learning machine network model optimized by whale to realize the fault classification recognition and diagnosis of clamp,and the accuracy of the model and feasibility of the whole GA-PSO-KPCA-WOA-KELM fault diagnosis model were analyzed from the monitoring results.The results show that the average accuracy of GA-PSO-KPCA-WOA-KELM fault diagnosis model can reach 99.99%,which proves that the method can effectively realize the accurate identification of aviation hydraulic pipeline-clamp fault type,and has good stability,effectiveness and feasibility.It provides a feasible diagnostic idea for identifying the aviation hydraulic pipeline-clamp fault accurately.