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<p>In order to improve the fault diagnosis efficiency of rotating mechanisms such as rolling bearings, a fault diagnosis model based on multi-domain features and RelifF feature selection-Bayesian optimized K nearest neighbor (abbreviated as MDF-Relief-Bayes-KNN) is proposed in this paper. Firstly, fine-grained multi-scale sample entropy (abbreviated as FGMSE) is extracted to characterize the nonlinear complexity characteristics in multi-scale quantitatively. Then, multi-dimensional time-domain features are extracted to quantitatively describe the time-domain statistical features. Multi-domain features are constructed by combining the two types of features.Secondly, ReliefF method is introduced for feature selection and a simplified feature vector is obtained. Bayesian Optimization (abbreviated as BO) is imported to optimize the distance type and k (the nearest neighbors’ number) of KNN model, and Bayes-KNN fault diagnosis model is established. An instance analysis is carried on and the average accuracy reaches 99.24%. Compared with different fault diagnosis models, the proposed technique has high accuracy and calculation speed, and it is a kind of fault diagnosis method with potential application.</p>

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Fault diagnosis technique based on multi-domain features and RelifF-Bayes-KNN in rolling bearing

  • 期刊论文

  • 发表日期：

    2025年3月21日

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  • Wang Bing,
  • Hu Xiong

摘要

In order to improve the fault diagnosis efficiency of rotating mechanisms such as rolling bearings, a fault diagnosis model based on multi-domain features and RelifF feature selection-Bayesian optimized K nearest neighbor (abbreviated as MDF-Relief-Bayes-KNN) is proposed in this paper. Firstly, fine-grained multi-scale sample entropy (abbreviated as FGMSE) is extracted to characterize the nonlinear complexity characteristics in multi-scale quantitatively. Then, multi-dimensional time-domain features are extracted to quantitatively describe the time-domain statistical features. Multi-domain features are constructed by combining the two types of features.Secondly, ReliefF method is introduced for feature selection and a simplified feature vector is obtained. Bayesian Optimization (abbreviated as BO) is imported to optimize the distance type and k (the nearest neighbors’ number) of KNN model, and Bayes-KNN fault diagnosis model is established. An instance analysis is carried on and the average accuracy reaches 99.24%. Compared with different fault diagnosis models, the proposed technique has high accuracy and calculation speed, and it is a kind of fault diagnosis method with potential application.