Machine Learning Based Bearing Fault Diagnosis Using the Case Western Reserve University Data: A Review

• Published in: IEEE access Vol. 9; pp. 155598 - 155608
• Main Authors: Zhang, Xiao, Zhao, Boyang, Lin, Yun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bearings; classifier; CWRU; Datasets; Entropy; Fault diagnosis; Feature extraction; feature selection; Machine learning; Roller bearings; Rolling bearings; Rotating machinery; Time-domain analysis; Vibrations
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3128669

The most important parts of rotating machinery are the rolling bearings. Finding bearing faults in time can avoid affecting the operation of the entire equipment. The data-driven fault diagnosis technology of bearings has recently become a research hotspot, and the starting point of research is often the acquisition of vibration signals. There are many public data sets for rolling bearings. Among them, the most widely used public dataset is Case Western Reserve University bearing center (CWRU). This paper will start from the CWRU data set, compare and analyze some basic methods of machine learning based rolling bearing fault diagnosis, and summarize the characteristics of CWRU. First, we give a comprehensive introduction to CWRU and summarize the results achieved. After that, the basic methods and principles of machine learning based rolling bearing fault diagnosis were summarized. Finally, we conduct experiments and analyze experimental results. This paper will have certain guiding significance for the future use of CWRU for machine learning based rolling bearing fault diagnosis.