Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review

Shiza Mushtaq, M. M. Manjurul Islam and Muhammad Sohaib
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Shiza Mushtaq: Department of Computer Science & Engineering, Lahore Garrison University, Lahore 54000, Pakistan
M. M. Manjurul Islam: Information, Communication and Technology Center, Fondazione Bruno Kessler, 38123 Trento, Italy
Muhammad Sohaib: Department of Computer Science & Engineering, Lahore Garrison University, Lahore 54000, Pakistan

Energies, 2021, vol. 14, issue 16, 1-24

Abstract: This paper presents a comprehensive review of the developments made in rotating bearing fault diagnosis, a crucial component of a rotatory machine, during the past decade. A data-driven fault diagnosis framework consists of data acquisition, feature extraction/feature learning, and decision making based on shallow/deep learning algorithms. In this review paper, various signal processing techniques, classical machine learning approaches, and deep learning algorithms used for bearing fault diagnosis have been discussed. Moreover, highlights of the available public datasets that have been widely used in bearing fault diagnosis experiments, such as Case Western Reserve University (CWRU), Paderborn University Bearing, PRONOSTIA, and Intelligent Maintenance Systems (IMS), are discussed in this paper. A comparison of machine learning techniques, such as support vector machines, k-nearest neighbors, artificial neural networks, etc., deep learning algorithms such as a deep convolutional network (CNN), auto-encoder-based deep neural network (AE-DNN), deep belief network (DBN), deep recurrent neural network (RNN), and other deep learning methods that have been utilized for the diagnosis of rotary machines bearing fault, is presented.

Keywords: auto-encoders; bearing; condition monitoring; convolutional neural network; deep belief network; deep learning; fault diagnosis; machine learning; recurrent neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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