Potential, challenges and future directions for deep learning in prognostics and health management applications
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Author / Producer
Date
2020-06
Publication Type
Journal Article
ETH Bibliography
yes
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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.
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Publication status
published
Editor
Book title
Journal / series
Volume
92
Pages / Article No.
103678
Publisher
Elsevier
Event
Edition / version
Methods
Software
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Date collected
Date created
Subject
Deep learning; Prognostics and health management; GAN; Domain adaptation; Fleet PHM; Deep reinforcement learning; Physics-induced machine learning
Organisational unit
09642 - Fink, Olga (ehemalig) / Fink, Olga (former)
Notes
Funding
176878 - Data-Driven Intelligent Predictive Maintenance of Industrial Assets (SNF)