Development of Intelligent Fault-Tolerant Control Systems with Machine Learning, Deep Learning, and Transfer Learning Algorithms: A Review

• Published in: Expert systems with applications Vol. 238; p. 121956
• Main Authors: Amin, Arslan Ahmed, Sajid Iqbal, Muhammad, Hamza Shahbaz, Muhammad
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.03.2024
• Subjects: Algorithms; Deep learning; Fault diagnosis; Intelligent fault-tolerant control; Machine learning; Transfer learning; ANN; AE; Intelligent fault-tolerant control; DNN; Transfer learning; JDA; KNN; IFTC; DBM; TDA; Fault diagnosis; Deep learning; TCA; Algorithms; FTC; GAN; Machine learning; RBM; IC Engine
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2023.121956

Intelligent Fault-Tolerant Control (IFTC) refers to the applications of machine learning algorithms for fault diagnosis and design of Fault-Tolerant Control (FTC). The overall goal of the FTC is to accommodate defects in the system components while they are in use and maintain stability with little to no performance reduction. These systems are crucial for mission-critical and safety-related applications where the safety of people is at stake and service continuity is crucial. In this review paper, a systematic study has been done for the development of FTC with machine learning, deep learning, and transfer learning algorithms. The challenges and limitations faced with their possible solutions through machine learning theories for the IFTC model are lined up. This paper guides researchers on the different possible types of machine learning algorithms and their advanced forms like deep learning and transfer learning. The differences among these are highlighted by the challenges and limitations of each. The paper is significant such that most of the important literature references from the Scopus database particularly related to important electrical and mechanical industrial problems have been discussed to guide the researchers who want to apply IFTC for specific industrial problems, being the research gap. Finally, future research directions for the development of IFTC are highlighted.