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The literature review on fault detection and diagnosis using deep learning models highlights the central idea of utilizing advanced machine learning techniques for improved system performance. Various algorithms such as convolutional neural networks (CNN), stacked autoencoders (AE), and deep neural networks (DNN) have been employed, achieving high accuracy rates ranging from 96.98% to 99.75%. Despite the promising results, drawbacks such as the need for high-quality data, noise interference affecting model performance, and the complexity of training deep architectures have been identified as challenges in implementing these models for machinery fault detection and diagnosis.