Journal
IEEE ACCESS
Volume 7, Issue -, Pages 122644-122662Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2938227
Keywords
Deep learning; fault detection and diagnosis; current challenges; future developments
Categories
Funding
- Institute of Noise and Vibration Universiti Teknologi Malaysia (UTM) under the Higher Institution Centre of Excellence (HICoE) Grant Scheme [R.K130000.7809.4J225, R.K130000.7809.4J226, R.K130000.7843.4J227, R.K130000.7843.4J228]
- UTM Research University [Q.K130000.2543.11H36]
- Fundamental Research Grant Scheme by Ministry of Higher Education Malaysia [R.K130000.7840.4F653]
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In the age of industry 4.0, deep learning has attracted increasing interest for various research applications. In recent years, deep learning models have been extensively implemented in machinery fault detection and diagnosis (FDD) systems. The deep architecture's automated feature learning process offers great potential to solve problems with traditional fault detection and diagnosis (TFDD) systems. TFDD relies on manual feature selection, which requires prior knowledge of the data and is time intensive. However, the high performance of deep learning comes with challenges and costs. This paper presents a review of deep learning challenges related to machinery fault detection and diagnosis systems. The potential for future work on deep learning implementation in FDD systems is briefly discussed.
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