Fault Detection Technology Based on Deep Learning, 2nd Edition

Dear Colleagues,

In recent years, there has been increasing interest and investment in electrical-based systems for various applications such as Industry 4.0, electric vehicles, renewables, micro- and smart- grids, and so on. Such systems should exhibit high performance, reliability and availability. Indeed, they are exposed to several types of failures due to external and internal sources. Failures may affect energy sources, actuators, sensors or controllers. Consequently, predictive maintenance based on accurate fault diagnosis approaches and fault-tolerant control strategies are of the utmost importance.

A state-of-the-art review shows that fault diagnosis methods are mainly classified in model-based approaches and signal-based approaches. However, with the increase in data acquisition and processing algorithms, artificial intelligence (AI) tools have become more attractive for fault diagnosis and fault classification issues. Indeed, AI approaches are based only on recorded data obtained from measured quantities rather than specific complex mathematical models.

The main purpose of this Special Issue is to share high-quality original research articles and reviews in the area of fault diagnosis based on deep learning and its applications.

The topics interest include, but are not limited to, the following:

• Fault detection and fault diagnosis based on deep learning;
• Fault-tolerant control strategies based on deep learning algorithms;
• Predictive maintenance with deep learning;
• Implementation of deep learning based algorithms and architectures for diagnosis.

Dr. Séjir Khojet El Khil
Dr. Chiara Boccaletti
Dr. Monia Ben Khader Bouzid
Guest Editors

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