Rolling element bearings are a vital component of all machines; thus, identifying their issues is critical. Rolling bearing fault diagnosis is essential for ensuring operational efficiency and safety in complex mechanical systems. Data collection, signal conditioning, and fault categorization are the three main components of the fault assessment approach. With the increasing volume of monitoring data and the confrontation with associated with long-established fault diagnosis approaches, deep learning has come into view as a powerful modus operandi to finding out insightful bearing faults. This paper offers a comprehensive review of the literature on deep learning techniques for diagnosing bearing faults, a topic that has attracted scholarly interest recently. It explores widely used deep learning algorithms namely Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Autoencoders (AE), Generative Adversarial Networks (GAN), as well as Deep Belief Networks (DBN).
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