Rolling element bearings are crucial for supporting rotating parts in machinery, enabling efficient power transmission. Any faults in these bearings can cause significant machine damage, potentially causing catastrophic failures if left unnoticed. Therefore, early detection of bearing damage and its severity is crucial to avoiding abrupt equipment malfunctions. Defects in bearing components generate vibrations, which intensify with the defect’s size. These vibrations can be measured using various signal analysis techniques for fault identification in the bearing elements. Numerous researchers have employed various signal-analysing approaches for bearing condition monitoring purposes under various conditions. This paper discusses diverse approaches researchers use to identify bearing faults and their corresponding models. It aims to comprehensively understand recent developments in defect identification techniques for bearings.
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