基于KPCA-LSSVM的单向阀故障诊断研究

doi:10.16180/j.cnki.issn1007-7820.2019.03.003

Abstract

Abstract:

It was difficult to extract and diagnose the characteristic of the fault vibration signal of the check valve of the high pressure diaphragm pump. To solve the problem, KPCA combined with LSSVM were performed for fault diagnosis research. The bi-stable SR method and DEMD algorithm were used to preprocess the signal of each state of the one-way valve, and the principal component of the decomposed K-L divergence was used to extract feature parameters of the time-frequency domain to construct the feature vector set. The second feature extraction of vector set was carried out by KPCA, which were further input into the LSSVM diagnostic system to complete check value fault diagnosis and classification. The experimental data proved that the fault diagnosis rate of this method was 90%, and the fault feature of the one-way valve could be diagnosed well.

Key words: check value, DEMD, feature vector, KPCA, LSSVM, fault diagnosis

CLC Number:

TN911.7

MU Zhuqing. Study on the Fault Diagnosis of Check Valve Based on KPCA-LSSVM[J].Electronic Science and Technology, 2019, 32(3): 10-15.

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References 17

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样本类别描述	样本规模	训练样本数	类别标签
CN	3 072×20	3 072×20	1
CZ	3 072×20	3 072×20	2
CM	3 072×20	3 072×20	3
CK	3 072×20	3 072×20	4

算法种类	训练样本	故障错分个数				故障准确率
算法种类	训练样本	CN	CZ	CM	CK	故障准确率
KPCA-LSSVM	20	0	4	3	1	90.00%
PCA-SVM	20	2	4	4	1	86.25%

Study on the Fault Diagnosis of Check Valve Based on KPCA-LSSVM

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