A Hybrid Generalized Hidden Markov Model-Based Condition Monitoring Approach for Rolling Bearings

Sensors
Jie LiuFengyun Xie

Abstract

The operating condition of rolling bearings affects productivity and quality in the rotating machine process. Developing an effective rolling bearing condition monitoring approach is critical to accurately identify the operating condition. In this paper, a hybrid generalized hidden Markov model-based condition monitoring approach for rolling bearings is proposed, where interval valued features are used to efficiently recognize and classify machine states in the machine process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition (VMD). Parameters of the VMD, in the form of generalized intervals, provide a concise representation for aleatory and epistemic uncertainty and improve the robustness of identification. The multi-scale permutation entropy method is applied to extract state features from the decomposed signals in different operating conditions. Traditional principal component analysis is adopted to reduce feature size and computational cost. With the extracted features' information, the generalized hidden Markov model, based on generalized interval probability, is used to recognize and classify the fault types and fault severity levels. Finally, the experiment ...Continue Reading

References

May 15, 2002·Physical Review Letters·Christoph Bandt, Bernd Pompe
Aug 23, 2002·Physical Review Letters·Madalena CostaC-K Peng

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Citations

Nov 4, 2017·Sensors·Paolo BellavistaMauro Tortonesi

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Methods Mentioned

BETA
feature extraction
PCA
features extraction
feature

Software Mentioned

VMD
MPE
MATLAB

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