Time-Varying and Multiresolution Envelope Analysis and Discriminative Feature Analysis for Bearing Fault Diagnosis
Time-Varying and Multiresolution Envelope Analysis and Discriminative Feature Analysis for Bearing Fault Diagnosis
复制标题
DOI：
10.1109/tie.2015.2460242
复制
发表时间：
2015-12-01
影响因子：
7.7
通讯作者：
Kim, Jong-Myon
中科院分区：
文献类型：
--
作者：
Kang, Myeongsu;Kim, Jaeyoung;Kim, Jong-Myon
本文针对低速滚动轴承的各种单一故障和多种组合故障，提出了一种可靠的故障诊断方法。该方法对声发射信号进行时间分割，选择包含轴承故障内在信息的部分信号。然后，本文进行频率分析所选择的时域AE信号，通过使用多级有限脉冲响应滤波器组，以获得最丰富的子带信号，涉及轴承缺陷的异常症状。它是通过使用一个2-D可视化工具，代表的百分比的高斯混合模型为基础的残留成分缺陷成分比率，通过随时间变化和多分辨率包络分析（TVMREA）。然后，在时域和频域的故障特征提取的信息子带信号。由于所有提取的故障特征可能不是同样有用的诊断，建议基于遗传算法（GA）的判别特征分析（GADFA）选择最具鉴别力的故障特征子集。在实验中，单一和多个组合的轴承故障在不同的条件下，使用TVMREA和GADFA的故障诊断方案的有效性进行了验证。实验结果表明，这种可靠的故障诊断方法准确地识别轴承故障类型在各种条件下。此外，GADFA优于其他最先进的特征分析技术，在平均分类准确率方面提高了7.3%-46.6%。
This paper presents a reliable fault diagnosis methodology for various single and multiple combined defects of low-speed rolling element bearings. This method temporally partitions an acoustic emission (AE) signal and selects a portion of the signal, which contains intrinsic information about the bearing failures. This paper then performs frequency analysis for the selected time-domain AE signal by using multilevel finite-impulse response filter banks to obtain the most informative subband signals involving abnormal symptoms of the bearing defects. It does this by using a 2-D visualization tool that represents the percentage of the Gaussian-mixture-model-based residual component-to-defect component ratios via time-varying and multiresolution envelope analysis (TVMREA). Then, fault signatures in the time and frequency domains are extracted in the informative subband signals. Since all the extracted fault features may not be equally useful for diagnosis, the proposed genetic algorithm (GA)-based discriminative feature analysis (GADFA) selects the most discriminative subset of fault signatures. In experiments, single and multiple combined bearing defects under various conditions are used to validate the effectiveness of this fault diagnosis scheme using TVMREA and GADFA. Experimental results indicate that this reliable fault diagnosis methodology accurately identifies bearing failure type across a variety of conditions. In addition, GADFA outperforms other state-of-the-art feature analysis techniques, yielding 7.3%-46.6% performance improvements in average classification accuracy.