An Improved Fault Diagnosis Method of Rotating Machinery Using Sensitive Features and RLS-BP Neural Network

Qidong Lu, Rui Yang, Maiying Zhong*, Youqing Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

65 Citations (Scopus)


An improved algorithm with feature selection and neural network classification is proposed in this paper to investigate the fault diagnosis problem of rotating machinery. The feature vectors are constructed by extracting the time- and frequency-domain characteristics of the overall machine under multiple operating conditions. To strengthen the fault diagnostic ability, an improved sensitive feature selection algorithm is proposed by improving the distance evaluation (DE) method and reconstructing a low-dimensional sensitive feature sample with selectively chosen parameters from multidimensional feature vectors. The recursive least square backpropagation (RLS-BP) neural network algorithm is used for fault diagnosis by classifying the feature vectors of normal signal and faulty signals. The effectiveness of the proposed method is verified via hardware experiments using wind turbine drivetrain diagnostics simulator (WTDDS) by comparing with conventional feature vector construction methods and neural network algorithm.

Original languageEnglish
Article number8701663
Pages (from-to)1585-1593
Number of pages9
JournalIEEE Transactions on Instrumentation and Measurement
Issue number4
Publication statusPublished - Apr 2020


  • Fault diagnosis
  • feature selection
  • feature vector
  • neural network
  • rotating machinery

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