Entropy Based Fault Classification Using the Case Western Reserve University Data: A Benchmark Study

• Published in: IEEE transactions on reliability Vol. 69; no. 2; pp. 754 - 767
• Main Authors: Li, Yongbo, Wang, Xianzhi, Si, Shubin, Huang, Shiqian
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Benchmark analysis; Benchmark testing; Benchmarks; Case Western Reserve University (CWRU) data; Classification; Diagnostic systems; Entropy; fault classification; Fault diagnosis; fault feature extraction; Feature extraction; Industrial applications; Iron; Support vector machines; Time series analysis
• ISSN: 0018-9529; 1558-1721
• DOI: 10.1109/TR.2019.2896240

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.