Potential, challenges and future directions for deep learning in prognostics and health management applications. (June 2020)

• Record Type: Journal Article
• Authors: Fink, Olga; Wang, Qin; Svensén, Markus; Dersin, Pierre; Lee, Wan-Jui; Ducoffe, Melanie
• Is Part Of: Engineering applications of artificial intelligence. Volume 92(2020)
• Publication Date: 2020-06
• Subjects: Deep learning -- Prognostics and health management -- GAN -- Domain adaptation -- Fleet PHM -- Deep reinforcement learning -- Physics-induced machine learning; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.103678
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Deep learning applications have been thriving over the last decade in many different domains, including computer vision and natural language understanding. The drivers for the vibrant development of deep learning have been the availability of abundant data, breakthroughs of algorithms and the advancements in hardware. Despite the fact that complex industrial assets have been extensively monitored and large amounts of condition monitoring signals have been collected, the application of deep learning approaches for detecting, diagnosing and predicting faults of complex industrial assets has been limited. The current paper provides a thorough evaluation of the current developments, drivers, challenges, potential solutions and future research needs in the field of deep learning applied to Prognostics and Health Management (PHM) applications.