Potential, challenges and future directions for deep learning in prognostics and health management applications

By Olga Fink, Qin Wang, Markus Svensén, Pierre Dersin, Wan-Jui Lee, Mélanie Ducoffe. Engineering applications of artificial intelligence, 2020

488 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Control and Systems Engineering, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/cf197fe0ae235ca96164584c02561c0f8eef3811

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Arash Heidari
2. Mehmet Unal
3. Hasan Dağ
4. Nima Jafari Navimipour
5. Olga Fink
6. Kai F. Goebel
7. Manuel Arias Chao

In Control and Systems Engineering

1. Olga Fink
2. Kai F. Goebel
3. Manuel Arias Chao
4. Arash Heidari

Citations in the corpus

• Deepfake detection using deep learning methods: A systematic and comprehensive review (2023, methodology): “Besides, R. Wang et al. (2020) introduced DeepSonar, a method that monitors the learned neuron behaviors from a voice synthesis system to detect AI-generated phony sounds.”
• Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics (2021, background): “In light of their superior learning capabilities in a wide range of application fields, Machine Learning (ML), in general, and Deep Learning (DL), in particular, are promising candidates to tackle the challenges involved in the design of intelligent maintenance approaches [2].”
• Fusing Physics-based and Deep Learning Models for Prognostics (2020, unknown): “scenarios of limited labeled data [13,14].”
• Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities (2020, unknown): “gov/project/prognostic-data-repository) [153] Fink, O.”