Motor Bearing Fault Diagnosis Using Deep Convolutional Neural Networks with 2D Analysis of Vibration Signal

M. M. Manjurul Islam, Jong-Myon Kim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Citations (Scopus)


Bearings are critical components in rotating machinery, and it is crucial to diagnose their faults at an early stage. Existing fault diagnosis methods are mostly limited to manual features and traditional artificial intelligence learning schemes such as neural network, support vector machine, and k-nearest-neighborhood. Unfortunately, interpretation and engineering of such features require substantial human expertise. This paper proposes an adaptive deep convolutional neural network (ADCNN) that utilizes cyclic spectrum maps (CSM) of raw vibration signal as bearing health states to automate feature extraction and classification process. The CSMs are two-dimensional (2D) maps that show the distribution of cycle energy across different bands of the vibration spectrum. The efficiency of the proposed algorithm (CSM+ADCNN) is validated using benchmark dataset collected from bearing tests. Experimental results indicate that the proposed method outperforms the state-of-the-art algorithms, yielding 8.25% to 13.75% classification performance improvement.
Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence
ISBN (Electronic)978-3-319-89656-4
Publication statusPublished (in print/issue) - 6 Apr 2018

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Convolutional neural network
  • Cyclostationary signal analysis
  • Feature extraction
  • Fault diagnosis
  • Vibration analysis
  • Data-driven diagnostic


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