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Intelligent identification of bolt looseness with one-dimensional deep convolutional neural networks

SIGNAL IMAGE AND VIDEO PROCESSING 2025年第1期19卷 1-11页

作者： Zhang, Xiaoli Xia, Yong Zhao, Junfeng Changan Univ Minist Educ Key Lab Rd Construct Technol & Equipment Xian 710064 Peoples R China

The bolt connection has characteristics of strong bearing capacity, easy maintenance and replacement, but the bolt connection structure often has loose failures during operation due to the detachability of the bolts. Bolts looseness and connection failure will not only affect the normal use of the mechanism, shorten the service life, and even cause casualties. Online monitoring and evaluation of bolt assembly tightness have attracted numerous interest. Automatic feature extraction plays a crucial role in intelligent state monitoring of mechanical systems, which can adaptively learn features from raw data and discover new state-sensitive features. A one-dimensional deep convolutional neural network (1D-DCNN) with eight convolutional layers and five pooling layers is proposed to achieve high precision in identification of bolt looseness. Firstly, the data overlap sampling is used to obtain the sufficient data so as to satisfy the requirements of 1D-DCNN. Then the 1D-DCNN carries out the process of feature extraction, feature selection and classification, which can take the free vibration signal of the bolt connection structure as input, and then fuse the feature extraction and assembly tightness classification process together to realize the intelligent detection of bolts looseness. The validity of the proposed method is verified by the data acquired from the free vibration excitation experiment of the bolt connection rotor of aero-engine. The results show that the adaptively learned features of the 1D-DCNN can represent the complex mapping relationship between the signal and the assembly state, and achieve higher accuracy than other methods.

关键词： Bolt connection Feature learning Deep convolutional neural networks data overlap sampling

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