基于多尺度核独立元分析与核极限学习机的柴油机故障诊断

肖忠宝

doi:10.3969/j.issn.1001-2222.2017.06.017

车用发动机 >

2017 , Vol. 0 >Issue 6: 84 - 89

DOI: https://doi.org/10.3969/j.issn.1001-2222.2017.06.017

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基于多尺度核独立元分析与核极限学习机的柴油机故障诊断

肖忠宝

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（山西交通职业技术学院，山西太原 030002）

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Diesel Engine Fault Diagnosis Based on Multiscale Kernel Independent Component Analysis and Kernel Extreme Learning Machine

XIAO Zhongbao

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（Shanxi Traffic Vocational and Technical College，Taiyuan 030002，China）

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摘要

为提高柴油机故障诊断速度和精度，提出了基于改进多尺度核独立元分析与量子粒子群优化核极限学习机的故障诊断方法。首先利用固有时间尺度分解对缸盖振动信号进行多尺度时频分解，并根据故障敏感度参数筛选有效分量以实现振动冲击特征增强；然后利用核独立元分析消除有效分量间的频带混叠，分离故障敏感频带，并提取各频带的AR模型参数、多尺度模糊熵和标准化能量矩构造联合故障特征向量；最后建立基于量子粒子群优化的核极限学习分类器实现柴油机故障诊断。试验结果表明，该方法有效增强了缸盖振动信号中的故障敏感特征，提高了柴油机故障诊断速度和精度，故障分类准确率达到98.45%。

关键词： 固有时间尺度分解; 多尺度核独立元分析; 特征增强; 量子粒子群; 核极限学习机; 故障诊断

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肖忠宝 . 基于多尺度核独立元分析与核极限学习机的柴油机故障诊断[J]. 车用发动机, 2017 , 0(6) : 84 -89 . DOI: 10.3969/j.issn.1001-2222.2017.06.017

Abstract

In order to improve the speed and accuracy of diesel engine fault diagnosis, a method based on improved multiscale kernel independent component analysis (MSKICA) and kernel extreme learning machine optimized by quantum particle swarm optimization (QPSOKELM) was proposed. The cylinder head vibration signal was first decomposed into several timefrequency bands by intrinsic timescale decomposition and the effective components were selected according to the fault sensitivity in order to enhance the vibration characteristics. Then the frequency aliasing between different effective components was eliminated by using kernel independent component analysis in order to find the fault sensitive frequency bands. And the AR model parameters, multiscale fuzzy entropy and standardized energy moment of each band were extracted to build the structural feature vector. The kernel extreme learning machine optimized by quantum particle swarm optimization was finally constructed to diagnose diesel engine fault. The tests results indicate that the proposed method effectively enhances the features sensitive to engine fault in cylinder head vibration signal and the fault classification accuracy is higher than 98.45%, which improves the speed and accuracy of diesel engine fault diagnosis.

Key words： intrinsic timescale decomposition;multiscale kernel independent component analysis;feature enhancement; quantum particle swarm;kernel extreme learning machine;fault diagnosis

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