Current students

In the rapidly changing landscape of Industry 4.0, integrating machine learning (ML) and deep learning (DL) techniques has transformed fault diagnosis and prognosis. This integration enables predictive maintenance and enhances operational efficiency. This work provides a thorough overview of the latest ML and DL techniques used for diagnosing and predicting faults in industrial systems. We cover various approaches, including supervised and unsupervised learning, and discuss their advantages and limitations. Furthermore, we study the application of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models that combine different architectures to achieve robust fault detection and prediction, suggesting novel methods to be applied in real-world situations. Case studies from different industrial sectors demonstrate the practical implications and effectiveness of these techniques. We also address challenges related to data quality, model interpretability, and real-time implementation, and propose potential solutions and future research directions.