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Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble

机译：基于改进小波包变换和支持向量机集成的旋转机械故障诊断

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摘要

This paper presents a novel method for fault diagnosis based on an improved wavelet package transform (IWPT), a distance evaluation technique and the support vector machines (SVMs) ensemble. The method consists of three stages. Firstly, with investigating the feature of impact fault in vibration signals, a biorthogonal wavelet with impact property is constructed via lifting scheme, and the IWPT is carried out to extract salient frequency-band features from raw vibration signals. Then, the faulty features can be detected by envelope spectrum analysis of wavelet package coefficients of the most salient frequency band, Secondly, with the distance evaluation technique, the optimal features are selected from the statistical characteristics of raw signals and wavelet package coefficients, and the energy characteristics of decomposition frequency band. Finally, the optimal features are input into the SVMs ensemble with AdaBoost algorithm to identify the different abnormal cases. The proposed method is applied to the fault diagnosis of rolling element bearings, and testing results show that the SVMs ensemble can reliably separate different fault conditions and identify the severity of incipient faults, which has a better classification performance compared to the single SVMs.
机译：本文提出了一种基于改进的小波包变换（IWPT），距离评估技术和支持向量机（SVM）集成的故障诊断新方法。该方法包括三个阶段。首先，通过研究振动信号中的冲击故障特征，通过提升方案构造了具有冲击特性的双正交小波，并进行了IWPT，从原始振动信号中提取了显着的频带特征。然后，通过对最显着频带的小波包系数的包络频谱分析，可以检测出故障特征。其次，采用距离评估技术，从原始信号和小波包系数的统计特性中选择最佳特征，然后对分解频带的能量特性。最后，使用AdaBoost算法将最佳特征输入到SVM集合中，以识别不同的异常情况。将该方法应用于滚动轴承的故障诊断，测试结果表明，支持向量机集成能够可靠地分离出不同的故障条件并识别出早期故障的严重程度，与单个支持向量机相比具有更好的分类性能。

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