Machine Learning and Deep Learning Applications for Anomaly and Fault Detection

Dear Colleagues,

Unmanned factories are now being discussed as part of Industry 4.0. There have been numerous studies conducted on this subject. One of these is the monitoring of machines. An explanation for this condition is that failures on rare occasions are crucial to the operation's performance in this manufacturing environment.

An unnoticed machinery problem is certain to worsen over time, causing other mechanical problems. For solving this problem, the newly intelligent manufacturing systems are used to predict failure in advance. Technical advancements in manufacturing have addressed the research interests of integrated distributed intelligent manufacturing systems, which include distributed artificial intelligence theory and applications. It is critical to monitor them and forecast problems at an early stage, as well as to repair machine parts on time. Through machine monitoring, many machine faults can be diagnosed in time to prevent more serious damage later. Early detection of machine faults can improve their reliability, reduce energy consumption, reduce service and maintenance costs, and increase their lifecycle and safety, thereby significantly reducing lifecycle costs.

A lot of work has been done in this area and continues to be done. The aim of this Special Issue is to collect recent developments in machine learning and deep learning algorithms, signal processing techniques, feature extraction, feature selection, and data science studies. It is expected that the studies will be application-oriented. The topics include, but are not limited to, the following:

• Deep Learning
• Machine Learning
• Object Detection
• Computer Vision
• Fault Diagnosis
• Anomaly Detection
• Machine Monitoring
• Fault Detection
• Signal Processing
• Predictive Maintenance
• Time-Series and Image-based detection

Dr. Mustafa Demetgül
Guest Editor

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