Robust Deep Learning-Based Diagnosis of Mixed Faults in Rotating Machinery

Siyuan Chen, Yuquan Meng, Haichuan Tang, Yin Tian, Niao He, Chenhui Shao

Research output: Contribution to journalArticlepeer-review


Fault diagnosis for rolling elements in rotating machinery persistently receives high research interest due to the said machinery's prevalence in a broad range of applications. State-of-the-art methods in such setups focus on effective identification of faults that usually involve a single component while rejecting noise from limited sources. This article studies the data-based diagnosis of mixed faults coming from multiple components with an emphasis on model robustness against a wide spectrum of external perturbation. A dataset is collected on a rotor and bearing system by varying the levels and types of faults in both the rotor and bearing, which results in 48 machine health conditions. A duplet classifier is developed by combining two 1-D convolutional neural networks (CNNs) that are responsible for the diagnosis of the rotor and bearing faults, respectively. Experimental results show that the proposed classifier can reliably identify the onset and nature of mixed faults. In addition, one-vs-all classifiers are built using the features generated by the developed 1-D CNNs as predictors to recognize previously unlearned fault types. The effectiveness of such classifiers is demonstrated using data collected from four new fault types. Finally, the robustness and ability to reject external perturbation of the duplet classification model are analyzed using kernel density estimation. The code for the proposed classifiers is available at

Original languageEnglish (US)
Article number9136730
Pages (from-to)2167-2176
Number of pages10
JournalIEEE/ASME Transactions on Mechatronics
Issue number5
StatePublished - Oct 2020


  • Condition monitoring
  • Kernel density estimation (KDE)
  • convolutional neural network (CNN)
  • deep learning
  • fault diagnosis
  • model robustness
  • rotating machinery
  • rotor and bearing systems

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


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