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Adaptive online dictionary learning for bearing fault diagnosis

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Abstract

One of the most common parts to maintain system balance and support the various load in rotating machinery is the rolling element bearing. The breakdown of the element in bearings leads to inefficiency and sometimes catastrophic events across various industries. The main challenge over the last few years for fault diagnosis of bearings is the early detection of fault signature. In this paper, an adaptive online dictionary learning algorithm is developed for early fault detection of bearing elements. The dictionary is trained using a set of vibration signal from a heavily damaged bearing. The enveloped signal of the bearing is obtained using the Kurtogram and split into several sections. The K-SVD algorithm is implemented to the dictionaries corresponding to the enveloped signal. OMP is implemented with the calculated dictionaries to obtain the sparse representation of the testing signal. Then the envelope analysis is implemented to obtain the fault signal from the recovered signal by the dictionaries. The adaptive algorithm is added to the dictionary learning to update the dictionary based on newly acquired data with the weighted least square method. Without retraining the dictionaries using the K-SVD algorithm, the computation speed is significantly improved. The proposed algorithm is compared with a traditional dictionary learning algorithm to show the improvement in detection of new fault frequency and improved signal to noise ratio.

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Author information

Authors and Affiliations

  1. George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Yanfei Lu & Steven Y. Liang

  2. Department of Statistics at the University of Georgia, Athens, GA, 30602, USA

    Rui Xie

  3. College of Mechanical Engineering, Donghua University, Shanghai, 201620, China

    Steven Y. Liang

Authors
  1. Yanfei Lu
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  2. Rui Xie
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  3. Steven Y. Liang
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Contributions

Y.L. and R.X. created the model and analyzed the data; S.Y.L. provided feedback of the concept; Y.L. and R.X. wrote the paper.

Corresponding author

Correspondence to Yanfei Lu.

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Lu, Y., Xie, R. & Liang, S.Y. Adaptive online dictionary learning for bearing fault diagnosis. Int J Adv Manuf Technol 101, 195–202 (2019). https://doi.org/10.1007/s00170-018-2902-0

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  • DOI: https://doi.org/10.1007/s00170-018-2902-0

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