Fault diagnosis of bearings using classification techniques plays an important role in industrial applications, and, hence, has received increasing attention. Recently, significant efforts have been made to develop various methods for bearing fault classification and the application of Case Western Reserve University (CWRU) data for validation has become a standard reference to test the fault classification algorithms. However, a systematic research for evaluating bearing fault classification performance using the CWRU data is still lacking. This paper aims to provide a comprehensive benchmark analysis of the CWRU data using various entropy and classification methods. The main contribution of this paper is applying entropy-based fault classification methods to establish a benchmark analysis of entire CWRU datasets, aiming to provide a proper assessment of any new classification methods. Recommendations are provided for the selection of the CWRU data to aid in testing new fault classification algorithms, which will enable the researches to develop and evaluate various diagnostic algorithms. In the end, the comparison results and discussion are reported as a useful baseline for future research.

中文翻译：

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使用凯斯西储大学数据的基于熵的故障分类：基准研究

使用分类技术的轴承故障诊断在工业应用中发挥着重要作用，因此受到越来越多的关注。最近，在开发各种轴承故障分类方法方面做出了重大努力，凯斯西储大学 (CWRU) 数据进行验证已成为测试故障分类算法的标准参考。然而，目前还缺乏使用 CWRU 数据评估轴承故障分类性能的系统研究。本文旨在使用各种熵和分类方法对 CWRU 数据进行全面的基准分析。本文的主要贡献是应用基于熵的故障分类方法来建立整个 CWRU 数据集的基准分析，旨在为任何新的分类方法提供适当的评估。为选择 CWRU 数据提供了建议，以帮助测试新的故障分类算法，这将使研究能够开发和评估各种诊断算法。最后，比较结果和讨论被报告为未来研究的有用基线。