Special Issue Information
Title: Recent advances in Machine learning and deep learning theories: Towards Intelligent fault diagnosis
Summary: Machines and mechanical structures undergo various faults during operation. The timely diagnosis of these faults and the prediction of their future health condition is essential for industrial productivity and reliability. Recently, Intelligent fault diagnosis (IFD) has attracted much attention due to its promising way of automatically recognizing the health state of machines. Intelligent fault diagnosis (IFD) refers to applications of machine learning theories, such as artificial neural networks (ANN), support vector machine (SVM), and deep neural networks (DNN), to machine fault diagnosis. In the past, traditional machine learning (ML) theories began to weaken the contribution of human labor and brought the era of artificial intelligence to machine fault diagnosis. Over the recent years, the advent of deep learning (DL) theories has reformed IFD by further releasing artificial assistance that encouraged the development of an end-to-end diagnosis process. The purpose of this Special Issue is to provide a research-publishing environment for articles with the latest developments in ML and DL approaches for real-world applications in intelligent fault diagnosis. We invite researchers and practicing engineers to contribute original research articles that discuss issues related but not limited to: 1. Diagnostic and prognostic techniques based on AI 2. Data-driven and model-based sensor fault diagnosis 3. Feature construction with intelligent algorithms 4. Data augmentation techniques for fault diagnosis 5. AI-based solutions that are explainable 6. Machine-to-machine interfaces and paradigms for fault diagnosis and prognosis in the context of Industry 4.0. We would also welcome review articles that capture the current state-of-the-art and outline future areas of research in
the fields relevant to this Special Issue.
Keywords • Industrial systems • Smart industry • fault diagnosis • deep neural networks • convolutional neural networks • intelligent machines • feature extraction and analysis • Machine learning and deep learning algorithms • classification and clustering • pattern recognition • probabilistic and statistical methods
Anticipated deadline: 30/06/2023 |
