Abstract
Abstract Deep learning has been extensively applied in the rolling bearing fault diagnosis domain due to its superior data analysis and feature extraction capabilities. However, in practical applications, the normal operating state occupies most of the service life of equipment, and the occurrence probability of each kind of fault is different, leading to imbalanced data that significantly degrades the performance of the neural network. In order to solve this problem, a dual-feature enhanced hybrid convolutional network (DEHCNet) is proposed. Firstly, an impulse segment enhancement module (ISEM) is constructed to enhance impulse segment features in the raw data, helping the network to learn fault features more accurately. Then, a hybrid convolutional module (HCM) is designed to fully mine discriminant fault features of minority classes from imbalanced data. In addition, a feature-enhanced combinational pooling module (FCPM) is devised to guide the network to focus more on the critical features and maximize the retention of key features in dimensionality reduction operations, thereby reducing the influence of data imbalance on the classifier. Finally, two distinct datasets are used to verify DEHCNet. Experimental results show that this network has better diagnostic accuracy and robustness under conditions of data imbalance.
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