The recent development of information and communication technologies has engendered the concept of the smart factory that adds intelligence into the manufacturing process to drive continuous improvement, knowledge transfer, and data-based decision making. Fault diagnosis which consists of fault detection and fault classification has long been recognized as one of the important aspects of improving the reliability of industrial process systems. With the development of Artificial Intelligence algorithms and Internet of Things solutions and sensors, the reliability of automatic fault diagnosis is ever-increasing. However, to have a flexible and scalable IoT platform, the central computing of all data collected in manufacturing is the way for growth and innovation. Edge computing is the technology that makes it possible to quickly perform the necessary computational tasks in the network edge, i.e., between data producers and the cloud center.
The aim of this Special Issue is to highlight innovative developments with respect to the current challenges and opportunities for the applications of IoT, edge computing, and Artificial Intelligence for fault diagnosis. Topics include but are not limited to the following: real-time fault diagnosis with machine learning and deep learning; IoT-enabled predictive maintenance, IoT and edge computing-based condition monitoring, anomaly detection for fault diagnosis, fault diagnosis in multivariate control charts, and data mining approaches for fault diagnosis.