Empirical Study of Machine Learning for Intelligent Bearing Fault Diagnosis

Abstract: Bearing failure highly impacts performance and production of manufacturing systems, causes safety incidents, and results in casualties and property loss. According to the current literature, bearing faults cause 30-40% of all failures in induction motors. Therefore, identification of bearing faults, at early stages, is crucial to ensure seamless and reliable operation of induction motors in industrial and manufacturing operations. Faults occur in four components of bearing: inner race, outer race, ball, and cage. Regardless of the component in which fault occurs, it causes changes in vibration signals. Therefore, comparing normal signals with faulty ones is helpful in detecting localized faults in bearings. We use a benchmark publicly-available data set to conduct this analysis. The main challenge in using publicly-available benchmark datasets for fault detection is lack of manual for instruction on analysis experiments on the original data, which leaves researchers with the challenge and opportunity of applying various analytical methods for achieving higher accuracy rates and useful models for fault detection. This study presents a machine learning-based fault detection and classification scheme in induction motors to evaluate the significance and effects of various data preparation and feature extraction methods on accuracy and reliability of fault detection outcomes. The data preparation stage includes discussion of efficient data dimension reduction, and noise eradication, as well as feature extraction methods for induction motor signals. The main methodology is developing a variety of machine learning classifiers for detection and classification of normal bearings versus faulty bearings. Finally, the implications of the methodology and results for early fault diagnosis and enhanced reliability, as well as maintenance planning efforts in manufacturing systems are discussed. This study introduces proper implementation of machine learning models to improve system performance with higher speed and reliability. Furthermore, the methodology and results contribute to planning and undertaking maintenance operation more efficiently. Therefore, the approach, methodology, and results will be beneficial to both researchers and practitioners involved in manufacturing systems reliability analysis and optimization.

Keywords: machine learning, monitoring and fault diagnostics, bearing faults

DOI: 10.54941/ahfe1003049

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