Fault Diagnosis Methods Based on Information Theory or Machine Learning: From Theory to Application

Dear Colleagues,

At present, system health monitoring is a main concern in industrial and academic research due to the increasing requirements imposed by safety rules and the demand for the reduction of maintenance costs. Typical applications include the monitoring of transportation systems (automobiles, aircrafts, and trains); energy generation, transportation, storage, and distribution systems (nuclear power plants, wind turbines, smart grids, etc.); and industrial processes.

In smart systems, faults are detected at an early stage and classified, and the system lifetime is predicted to optimize maintenance operations. In order to meet these requirements, new monitoring algorithms are continuously developed. These algorithms integrate state-of-the-art signal and data analysis/processing techniques, entropy-based study, statistical learning, and machine learning or deep learning approaches.

This Issue will focus on the application of all of these signal and analysis/processing techniques for the health monitoring of complex systems. Particular attention is paid either to statistical/entropy-based detection/estimation techniques and machine learning/deep learning-based diagnosis techniques. Many approaches are concerned with topics such as quantitative approaches with wide and efficient physical modeling, qualitative approaches, and data driven approaches. For this Issue, either theoretical or applicative works will be considered. Particular attention will be paid to applications in tune with time such as human health, renewable-energy-based systems, energy conversion systems, smart grids, mechanical systems, vehicular and industrial applications, etc.

Dr. Claude Delpha
Prof. Dr. Demba Diallo
Guest Editors

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