Towards automatic detection of local bearing defects in rotating machines
Towards automatic detection of local bearing defects in rotating machines
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DOI：
10.1016/j.ymssp.2003.12.004
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发表时间：
2005-05-01
影响因子：
8.4
通讯作者：
Strömberg, JO
中科院分区：
文献类型：
--
作者：
Ericsson, S;Grip, N;Strömberg, JO
在本文中，我们推导和比较了几种不同的振动分析技术，自动检测局部缺陷的轴承。基于信号模型和讨论在何种程度上一个好的轴承监测方法应该相信它，我们提出了几种分析工具，轴承状态监测，并得出结论，小波特别适合这项任务。然后，我们描述了几种不同的自动轴承监测方法，使用103实验室和工业环境测试信号，轴承的真实状况是已知的目视检查的大规模评估。我们详细描述了四种性能最好的方法（两种基于小波，两种基于包络和周期化技术）。在我们的基本实现中，没有使用历史数据或使方法适应（大致）已知的机器或信号参数，四种最佳方法的错误率为9-13%，并且都是进一步微调和优化的良好候选者。特别是对于基于小波的方法，有几个潜在的性能提高的补充，我们最后总结成一个指导性的建议清单。(C)2003爱思唯尔有限公司。保留所有权利。
In this paper we derive and compare several different vibration analysis techniques for automatic detection of local defects in bearings.Based on a signal model and a discussion on to what extent a good bearing monitoring method should trust it, we present several analysis tools for bearing condition monitoring and conclude that wavelets are especially well suited for this task. Then we describe a large-scale evaluation of several different automatic bearing monitoring methods using 103 laboratory and industrial environment test signals for which the true condition of the bearing is known from visual inspection. We describe the four best performing methods in detail (two wavelet-based, and two based on envelope and periodisation techniques). In our basic implementation, without using historical data or adapting the methods to (roughly) known machine or signal parameters, the four best methods had 9-13% error rate and are all good candidates for further fine-tuning and optimisation. Especially for the wavelet-based methods, there are several potentially performance improving additions, which we finally summarise into a guiding list of suggestion. (C) 2003 Elsevier Ltd. All rights reserved.