Gear fault diagnosis based on information fusion and stacked de-noising auto-encoder

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Journal of Vibration and Shock ›› 2019, Vol. 38 ›› Issue (5) : 216-221.

LI Songbai, KANG Zijian, TAO Jie

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Abstract

Aiming at problems of traditional classifiers being susceptible to noise interference and a single sensor’s reliability and fault tolerance being not good, a gear fault diagnosis method based on multi-sensor information fusion and stacked de-noising auto-encoder (SDAE) was proposed. Firstly, multi-sensor vibration time domain signals were extracted to do data level fusion. Then SDAE was used to extract features layer by layer. Finally, labeled data was used to do the overall fine-tuning of the deep learning network and establish a gear state monitoring model. The fault diagnoses were conducted for different faulty gears, and diagnosis correctness and robustness of SDAE, SVM and BPNN were compared. The results showed that the gear fault diagnosis accuracy rate of SDAE based on information fusion reaches 95.17%, it is higher than that of the single signal classifier; the proposed method’s robustness is superior to those of other methods compared with the former.

Key words

information fusion / stacked denoising auto-encoder / deep learning / gear / fault diagnosis

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LI Songbai, KANG Zijian, TAO Jie. Gear fault diagnosis based on information fusion and stacked de-noising auto-encoder[J]. Journal of Vibration and Shock, 2019, 38(5): 216-221

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