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A Deep Intelligent Hybrid Model for Fault Diagnosis of Rolling Bearing

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Journal of Vibration Engineering & Technologies Aims and scope Submit manuscript

Abstract

Background

In the actual industry, due to the small amount of rolling bearing fault data in rotating machinery and the equipment operation under variable operating conditions, there are often serious data imbalance and weak fault signals easily drowned in strong noise, which make it difficult to extract feature information of the bearing health status, leading to a low accuracy of fault diagnosis.

Purpose

The purpose of this paper is to establish a fault diagnosis model based on deep learning, which can efficiently extract the feature information of vibration signals under the condition of data imbalance, and has good fault diagnosis results.

Methods

First, batch normalized convolution and batch normalized dilated convolution are used to build a High-performance Double Pyramidal Fusion (HDPF) module, which increases the perceptual field to extract more adequate and effective multi-scale complex feature information. Batch normalization is used to adjust the feature distribution and eliminate the feature distribution problem caused by data imbalance, the problem which data imbalance causes feature distribution discrepancies is eliminated. Second, a deep Adaptive Residual Decision Fusion (ARDF) module is used to adaptively calibrate the weak feature information, thus improving the adaptive feature learning performance for variable working conditions. Finally, an exponential moving average technique is used to optimize the overall performance of the proposed model by iteratively updating the hyperparameters.

Results

In the noise environment experiment, the diagnosis result of the proposed model for rolling bearing faults is 99.70%, and the diagnosis result for gearbox faults is 99.10%. By comparing with other mainstream intelligent diagnostic models, the experimental results show that the proposed method has better advancement and superiority.

Conclusion

In this paper, a deep intelligent hybrid model for rolling bearing fault diagnosis is proposed, which can achieve good fault diagnosis results under strong noise environment and solve the problem of data imbalance sample under complex working conditions.

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (Grant No. 61763029), the Science and Technology Project of Gansu Province (Grant No. 21YF5GA072).

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Authors and Affiliations

  1. College of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, China

    Xiaoqiang Zhao & Weilan Luo

  2. Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou, 730050, China

    Xiaoqiang Zhao & Weilan Luo

  3. National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou, 730050, China

    Xiaoqiang Zhao

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  1. Xiaoqiang Zhao
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Correspondence to Xiaoqiang Zhao.

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Zhao, X., Luo, W. A Deep Intelligent Hybrid Model for Fault Diagnosis of Rolling Bearing. J. Vib. Eng. Technol. 11, 721–737 (2023). https://doi.org/10.1007/s42417-022-00605-w

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  • DOI: https://doi.org/10.1007/s42417-022-00605-w

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