Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems

<p>The book aims to highlight the potential of deep learning (DL)-enabled methods in intelligent fault diagnosis (IFD) along with their benefits and contributions.</p><p>The authors first introduce basic applications of DL-enabled IFD including auto-encoders deep belief networks and convolutional neural networks. Advanced topics of DL-enabled IFD are also explored such as data augmentation multi-sensor fusion unsupervised deep transfer learning neural architecture search self-supervised learning and reinforcement learning. Aiming to revolutionize the nature of IFD <em>Deep Neural Networks-Enabled</em> <em>Intelligent</em> <em>Fault Diangosis of Mechanical Systems</em> contributes to improved efficiency safety and reliability of mechanical systems in various industrial domains.</p><p>The book will appeal to academic researchers practitioners and students in the fields of intelligent fault diagnosis prognostics and health management and deep learning.</p>