Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method
Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method
复制标题
小波自回归模型和主成分分析法齿轮多故障诊断虚拟样机及实验研究
DOI：
10.1016/j.ymssp.2011.02.017
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发表时间：
2011-10-01
影响因子：
8.4
通讯作者：
Li, Li
中科院分区：
文献类型：
--
作者：
Li, Zhixiong;Yan, Xinping;Li, Li
齿轮系统是广泛应用于各种工业应用的基本元件。由于传动机械中约80%的故障是由齿轮故障引起的，因此早期故障检测和准确故障诊断的效率对于机械的正常运行至关重要。回顾文献表明，只有有限的研究已经考虑到齿轮多故障诊断，特别是单一的，耦合的分布和局部故障。通过虚拟样机仿真分析和实验研究，提出了一种齿轮多故障诊断的新方法。提出了一种基于小波变换（WO）技术、自回归（AR）模型和主元分析（PCA）的故障检测方法。采用小波变换方法对原始振动信号进行去噪处理。与基于时间同步平均（TSA）的噪声去除方法相比，小波变换技术可以直接对原始振动信号进行去噪，而不需要对实测齿轮振动信号进行整体平均。更重要的是，小波变换可以在一次操作中处理齿轮副的耦合故障，而TSA必须进行多次检测多个故障。虚拟样机仿真分析结果表明，该方法是一种比TSA更省时、更有效的耦合故障检测方法，故障分类率优于TSA方法的上级。在实验测试中，所提出的方法进行了比较与马氏距离的方法。然而，后者被证明是效率低下的齿轮多故障诊断。其缺陷检测率低于60%，这是远远低于所提出的方法。此外，AR模型的能力，以科普本地以及分布式齿轮故障的虚拟样机仿真和实验研究进行了验证。(C)2011爱思唯尔有限公司版权所有。
Gear systems are an essential element widely used in a variety of industrial applications. Since approximately 80% of the breakdowns in transmission machinery are caused by gear failure, the efficiency of early fault detection and accurate fault diagnosis are therefore critical to normal machinery operations. Reviewed literature indicates that only limited research has considered the gear multi-fault diagnosis, especially for single, coupled distributed and localized faults. Through virtual prototype simulation analysis and experimental study, a novel method for gear multi-fault diagnosis has been presented in this paper. This new method was developed based on the integration of Wavelet transform (WO technique, Autoregressive (AR) model and Principal Component Analysis (PCA) for fault detection. The WT method was used in the study as the de-noising technique for processing raw vibration signals. Compared with the noise removing method based on the time synchronous average (TSA), the WT technique can be performed directly on the raw vibration signals without the need to calculate any ensemble average of the tested gear vibration signals. More importantly, the WT can deal with coupled faults of a gear pair in one operation while the TSA must be carried out several times for multiple fault detection. The analysis results of the virtual prototype simulation prove that the proposed method is a more time efficient and effective way to detect coupled fault than TSA, and the fault classification rate is superior to the TSA based approaches. In the experimental tests, the proposed method was compared with the Mahalanobis distance approach. However, the latter turns out to be inefficient for the gear multi-fault diagnosis. Its defect detection rate is below 60%, which is much less than that of the proposed method. Furthermore, the ability of the AR model to cope with localized as well as distributed gear faults is verified by both the virtual prototype simulation and experimental studies. (C) 2011 Elsevier Ltd. All rights reserved.