FAULT DIAGNOSIS OF ROLLING BEARING BASED ON FEATURE FUSION OF MULTI-SCALE DEEP CONVOLUTIONAL NETWORK

Wang Nini; Ma Ping; Zhang Hongli; Wang Cong

doi:10.19912/j.0254-0096.tynxb.2020-0752

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (4) : 351-358. DOI: 10.19912/j.0254-0096.tynxb.2020-0752

Topics on Key Technologies for Safety of Electrochemical Energy Storage Systems and Echelon Utilization of Decommissioned Power Batteries

FAULT DIAGNOSIS OF ROLLING BEARING BASED ON FEATURE FUSION OF MULTI-SCALE DEEP CONVOLUTIONAL NETWORK

Wang Nini, Ma Ping, Zhang Hongli, Wang Cong

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Abstract

Aiming for the limitations of traditional fault diagnosis model, such as, strong dependence on engineering priori knowledge, incomplete feature extraction, difficulties in selection of classifiers, a fault diagnosis model of rolling bearing based on feature fusion of multi-scale deep convolutional neutral network is proposed. First, a convolutional neural network model that integrates feature extraction and pattern recognition is constructed, the vibration signals of rolling bearing are converted into two-dimensional images by wavelet transform and used as the input sample set. Second, a multi-scale feature fusion module is built up in the network structure for the purpose of adaptive extraction of features at different levels of fault samples, aiming to extract different-scale features completely. Finally, fault samples are input into the network to realize adaptive features extract of bearing signals and end-to-end diagnosis. According to the experimental analysis results, the proposed fault diagnosis model based on feature fusion of multi-scale deep convolutional network can extract features of the signal at all levels and achieves higher diagnosis accuracy and robustness under interferences of different noises. It provides a theoretical basis to realize fault diagnosis of rolling bearings.

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wind turbines / rolling bearings / fault diagnosis / wavelet transforms / multi-scale convolutional neural network / feature fusion

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Wang Nini, Ma Ping, Zhang Hongli, Wang Cong. FAULT DIAGNOSIS OF ROLLING BEARING BASED ON FEATURE FUSION OF MULTI-SCALE DEEP CONVOLUTIONAL NETWORK[J]. Acta Energiae Solaris Sinica. 2022, 43(4): 351-358 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0752

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