AI-Assisted Condition Monitoring and Fault Diagnosis

Dear Colleagues,

Condition monitoring is necessary for the health diagnosis of expensive and safety-critical industrial assets, as well as for the correct implementation of preventive maintenance schemes. The assets required to be monitored include power transformers, high-voltage cables, gas-insulated switchgear, coaxial cables, and other power lines, and rotating machines. Recently there has been a rapid development of radio electromagnetic wave sensors for detecting and localizing partial discharges. Based on signals and data derived from sensors and arrays of sensors, condition monitoring technologies provide information regarding the status and future evolution of the health condition of machinery and plants, which is of capital importance for the safety and reliability of industrial equipment. Artificial Intelligence (AI) and Machine Learning (ML) techniques may be used advantageously for the early detection and localization of faults which, if left untreated, could lead to catastrophic failures.

The aim of this Special Issue is to attract high-quality, innovative, and original research works and survey articles related but not restricted to the already mentioned research fields, including the following topics:

• Sensor technology;
• Partial discharge detection and monitoring;
• Condition monitoring systems;
• Sensor integration for condition monitoring;
• Artificial intelligence (AI), machine learning (ML), fingerprinting, and pattern recognition techniques;
• AI-based techniques used in the detection and localization of faults;
• Electronic measurement systems for industrial environments;
• Signal processing techniques, signal conditioning, and front-end analog electronics;
• Artificial intelligence, machine learning, and pattern recognition;
• Sensor networks and arrays of sensors;

Papers should be of high quality and should not have been submitted or published elsewhere. However, extended versions of conference papers that show significant improvement can be considered for review.

Prof. Dr. Pavlos Lazaridis
Dr. Euler Cássio Tavares De Macêdo
Dr. Christos Tachtatzis
Guest Editors

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