Semantic Scholar Open Access 2020 464 sitasi

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

Olga Fink Qin Wang M. Svensén P. Dersin Wan-Jui Lee +1 lainnya

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Computer Science Engineering

Penulis (6)

Olga Fink

Qin Wang

M. Svensén

P. Dersin

Wan-Jui Lee

Mélanie Ducoffe

Format Sitasi

APA MLA BibTeX

Fink, O., Wang, Q., Svensén, M., Dersin, P., Lee, W., Ducoffe, M. (2020). Potential, challenges and future directions for deep learning in prognostics and health management applications. https://doi.org/10.1016/j.engappai.2020.103678

Akses Cepat

Lihat di Sumber doi.org/10.1016/j.engappai.2020.103678

Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Total Sitasi: 464×
Sumber Database: Semantic Scholar
DOI: 10.1016/j.engappai.2020.103678
Akses: Open Access ✓