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

Shiza Mushtaq, M. M. Manjurul Islam, Muhammad Sohaib

Research output: Contribution to journalArticlepeer-review

53 Citations (Scopus)
75 Downloads (Pure)


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.
Original languageEnglish
Article number5150
Issue number16
Publication statusPublished (in print/issue) - 20 Aug 2021


  • auto-encoders
  • bearing
  • condition monitoring
  • convolutional neural network
  • deep belief network
  • deep learning
  • fault diagnosis
  • machine learning
  • recurrent Neural Network


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