Fault Detection Technology Based on Deep Learning

Dear Colleagues,

In recent years, there has been increasing interest in and investment on electrical-based systems in various applications, such as Industry 4.0, electric vehicles, renewables, micro- and smart grids, and so on. Such systems should have 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 is of upmost importance.

State-of-the-art reviews have shown 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 only based on recorded data obtained from measured quantities instead of 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 of interest of this Special Issue include but are not limited to:

• 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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