Improved DBN method with attention mechanism for the fault diagnosis of gearboxes under varying working condition

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (14) : 47-52.

ZHANG Zhiyu1，YIN Aijun1，TAN Jian2

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Abstract

Aiming at the fact that the fault mode of gearboxes running under varying working condition is difficult to identify and the classification accuracy is reduced, an improved deep belief network (DBN) method with attention mechanism was proposed.Firstly, in order to solve the problem that single time domain or frequency domain characters are not comprehensive and insensitive for responding to gearbox fault information,the time domain, frequency domain and wavelet packet time-frequency domain features were extracted and synthesized to form a high-dimensional feature set.Then, making use of the greedy learning advantage of the DBN, the features were separately mined further.At the same time, the attention mechanism was used to adaptively give more attention to the features that effectively describe the gearbox state to improve the accuracy of gearbox fault diagnosis.Finally, the cosine loss function was introduced to reduce the sensitivity of the deep confidence network to the vibration intensity under different working conditions, thereby reduce the network fitting burden and improve the generalization ability.The fault diagnosis tests of a gearbox under varying working condition show that the proposed method effectively improves the fault diagnosis accuracy of the gearbox, and has good generalization ability.

Key words

attention mechanism;cosine loss function / deep belief network(DBN) / gearbox / fault diagnosis under varying working condition

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ZHANG Zhiyu，YIN Aijun，TAN Jian. Improved DBN method with attention mechanism for the fault diagnosis of gearboxes under varying working condition[J]. Journal of Vibration and Shock, 2021, 40(14): 47-52

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