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The rolling element bearing is a core component of many systems such as aircraft, train, steamboat, and machine tool, and their failure can lead to reduced capability, downtime, and even catastrophic breakdowns. Due to misoperation, manufacturing deficiencies, or the lack of monitoring and maintenance, it is often found to be the most unreliable component within these systems. Therefore, effective and efficient fault diagnosis of rolling element bearings has an important role in ensuring the continued safe and reliable operation of their host systems. This study presents a trace ratio criterion-based kernel discriminant analysis (TR-KDA) for fault diagnosis of rolling element bearings. The binary immune genetic algorithm (BIGA) is employed to solve the trace ratio problem in TR-KDA. The numerical results obtained using extensive simulation indicate that the proposed TR-KDA using BIGA (called TR-KDA-BIGA) can effectively and efficiently classify different classes of rolling element bearing data, while also providing the capability of real-time visualization that is very useful for the practitioners to monitor the health status of rolling element bearings. Empirical comparisons show that the proposed TR-KDA-BIGA performs better than existing methods in classifying different classes of rolling element bearing data. The proposed TR-KDA-BIGA may be a promising tool for fault diagnosis of rolling element bearings

Trace Ratio Criterion-Based Kernel Discriminant Analysis for Fault Diagnosis of Rolling Element Bearings Using Binary Immune Genetic Algorithm