New Trends in Fault Diagnosis and Prognosis for Engineering Applications: From Signal Processing to Machine Learning and Deep Learning

Dear Colleagues,

Complex industrial systems require increasing performances to guaranty security and safety. Fault diagnosis and prognosis are two of the major concerns that lead to these requirements and provide the reduction in maintenance costs. Typical applications needing these requirements include the monitoring of transportation systems (automobiles, aircraft, and trains); green energy generation, transportation, storage, and distribution systems (e.g., nuclear power plants, wind turbines, photovoltaic panels, smart grids, hydro generators, etc.), and industrial processes.

In smart systems, faults are detected at an early stage and classified, and the system lifetime is predicted to optimize the maintenance operations. 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 new trends in signal and analysis/learning/processing techniques for the health monitoring of complex systems. Particular attention is paid either to statistical-/entropy-based detection/estimation techniques or machine-learning-/deep-learning-based diagnosis techniques. Their particular use for engineering applications are also of interest. Many approaches are concerned with topics such as quantitative approaches with wide and efficient physical modeling, qualitative approaches, and data-driven ones. 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.

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

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