Gearbox bearing fault diagnosis based on SANC and 1-D CNN

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (19) : 204-209.

GAO Jiahao, GUO Yu, WU Xing

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Abstract

Recently, feature intelligent extraction and fault recognition techniques of rolling bearing based on deep learning algorithm are widely studied, but most of studies are limited to bearing faults without strong interference. Under the condition of stronger gear vibration interference existing in gearbox, the bearing fault recognition rate based on this type algorithm significantly drops. Here, in order to improve the accuracy rate of gearbox bearing fault intelligent recognition under stronger gear vibration signals interference, a gearbox bearing fault diagnosis method based on self-reference adaptive noise cancellation (SANC) technique and one-dimensional convolution neural network (1-DCNN) was proposed. Firstly, SANC was used to decompose gear vibration signals into periodic signal components and random signal ones, and suppress gear periodic strong interference components. Then, 1-D CNN was used to do intelligent feature extractionand recognition of random signal components containing bearing fault features, and realize improving gearbox bearing fault recognition rate under strong gear vibration interference.The advantages and effectiveness of the proposed method were verified with comparison to different methods.

Key words

gearbox / self-reference adaptive noise cancellation(SANC) technique / 1-D convolution neural network (CNN) / fault diagnosis

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GAO Jiahao, GUO Yu, WU Xing. Gearbox bearing fault diagnosis based on SANC and 1-D CNN[J]. Journal of Vibration and Shock, 2020, 39(19): 204-209

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