Probabilistic Gear Fault Diagnosis Using Bayesian Convolutional Neural Network

Kai Zhou, Jiong Tang

Research output: Journal article publicationConference articleAcademic researchpeer-review

2 Citations (Scopus)


Vibration measurement-based gear fault diagnoses have shown the promise aspects, where the deep learning methods have been harnessed. However, the traditional deep learning methods are deterministic in nature, and will be prone to false prediction when uncertainties are involved, such as time varying condition and measurement noise. To address these challenges, the fault pattern recognition needs to be performed in a probabilistic manner. Considering the features in vibration time-series usually are massive, in this research we develop a Bayesian convolutional neural network (BCNN) to conduct the gear fault diagnosis under uncertainties. The predictive distribution yielded facilitates the decision making with confidence level, leading to the robustness enhancement of the fault diagnosis. Comprehensive case studies are carried out to validate the proposed methodology.

Original languageEnglish
Pages (from-to)795-799
Number of pages5
Issue number37
Publication statusPublished - Nov 2022
Externally publishedYes
Event2nd Modeling, Estimation and Control Conference, MECC 2022 - Jersey City, United States
Duration: 2 Oct 20225 Oct 2022


  • Bayesian convolutional neural network (BCNN)
  • deep learning
  • gear fault diagnosis
  • measurement noise
  • time varying condition

ASJC Scopus subject areas

  • Control and Systems Engineering


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