An Improved Simultaneous Fault Diagnosis Method based on Cohesion Evaluation and BP-MLL for Rotating Machinery

Yixuan Zhang, Yu Han, Rui Yang, Dongke Su, Yiqi Wang, Yun Di, Qidong Lu, Mengjie Huang

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

3 Citations (Scopus)


With the requirements for safety and stability of rotating machinery, its fault diagnosis is significantly important. To diagnose simultaneous faults of gearbox and bearing in rotating machinery under different working conditions, an improved algorithm based on cohesion-based feature selection and improved back-propagation multi-label learning (BP-MLL) is proposed in this paper. Cohesion evaluation technique is applied to construct a low-dimensional feature vector by selecting high sensitivity parameters in a high-dimensional vector from time and frequency domain. Improved BP-MLL neural network algorithm considers correlation between labels and adopts ReLU as activation function. To show the effectiveness of the proposed method, hardware experiments are conducted on wind turbine drivetrain diagnostics simulator (WTDDS) for simultaneous fault diagnosis. The experiment reveals that the proposed method can achieve better results than conventional methods under six performance evaluation metrics.

Original languageEnglish
Title of host publication2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728156354
Publication statusPublished - Jun 2020
Event29th IEEE International Symposium on Industrial Electronics, ISIE 2020 - Delft, Netherlands
Duration: 17 Jun 202019 Jun 2020

Publication series

NameIEEE International Symposium on Industrial Electronics


Conference29th IEEE International Symposium on Industrial Electronics, ISIE 2020


  • Fault Diagnosis
  • Multi-label Learning
  • Neural Network
  • Rotating Machinery

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