Entropy Measures in Machine Fault Diagnosis: Insights and Applications
Entropy Measures in Machine Fault Diagnosis: Insights and Applications
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DOI：
10.1109/tim.2020.2981220
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发表时间：
2020-06-01
影响因子：
5.6
通讯作者：
Shu, Lei
中科院分区：
文献类型：
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作者：
Huo, Zhiqiang;Martinez-Garcia, Miguel;Shu, Lei
熵作为一种复杂性度量在时间序列分析中得到了广泛的应用。一个突出的例子是机器状态监测和工业故障诊断系统的设计。机器故障的发生通常会导致测量中的非线性特性，这是由瞬时变化引起的，这会增加系统响应的复杂性。熵测量适合于量化潜在过程中的这种动态变化，区分不同的系统条件。然而，在不同的背景下(例如，信息论和动力系统理论)，熵的概念有不同的定义，这可能会使应用科学中的研究人员感到困惑。在本文中，我们系统地回顾了一些基本熵度量的理论发展，并澄清了它们之间的关系。然后总结了基于熵的机械故障诊断系统的典型应用。此外，还解释了对熵度量的可能应用的见解，以及这些度量在哪里以及如何对未来的数据驱动的故障诊断方法有用。最后，讨论了该领域潜在的研究趋势，目的是改进在线熵估计，并将其应用于更广泛的智能故障诊断系统。
Entropy, as a complexity measure, has been widely applied for time series analysis. One preeminent example is the design of machine condition monitoring and industrial fault-diagnostic systems. The occurrence of failures in a machine will typically lead to nonlinear characteristics in the measurements, caused by instantaneous variations, which can increase the complexity in the system response. Entropy measures are suitable to quantify such dynamic changes in the underlying process, distinguishing between different system conditions. However, notions of entropy are defined differently in various contexts (e.g., information theory and dynamical systems theory), which may confound researchers in the applied sciences. In this article, we have systematically reviewed the theoretical development of some fundamental entropy measures and clarified the relations among them. Then, typical entropy-based applications of machine fault-diagnostic systems are summarized. Furthermore, insights into possible applications of the entropy measures are explained, as to where and how these measures can be useful toward future data-driven fault diagnosis methodologies. Finally, potential research trends in this area are discussed, with the intent of improving online entropy estimation and expanding its applicability to a wider range of intelligent fault-diagnostic systems.