Rotating machinery is widely used in aerospace, CNC machine tools, industrial robots and other fields. As the support and rotating component of the shaft, rolling bearings play a crucial role in the stable operation and expected function of rotating machinery[1–2]. However, due to harsh working conditions such as high temperature, high pressure and corrosion, rolling bearings are prone to failure, accounting for approximately 45–55% of rotating machinery failures[3]. Therefore, studying the fault diagnosis of rolling bearings is of great significance for the safe operation of rotating machinery[4–5]. In the face of a large number of equipment operation status monitoring data in modern industry, it is unrealistic to rely on manual extraction of fault features. In recent years, identifying fault types through vibration data has become the mainstream in the field of fault diagnosis, among them, which the intelligent fault diagnosis method based on deep learning has been favored by a large number of scholars[6]. The model based on deep learning has strong big data learning ability and high generalization performance, which can automatically extract fault features from bearing vibration data without manual intervention, greatly reducing the dependence on expert experience and domain knowledge. As one of the important branches of deep learning, convolutional neural network (CNN) is widely used in fault diagnosis and has achieved remarkable results[7]. Yao et al.[8] proposed a stacked inverse residual convolutional neural network intelligent bearing fault diagnosis method, which improved the diagnosis speed of the model and the diagnosis effect in noisy environment. Cui et al.[9] presented a multi-layer adaptive convolutional neural network bearing fault method, which enhanced the feature learning ability of the model and achieved high fault diagnosis accuracy under variable working conditions. Li et al.[10] put forward an improved convolutional neural network fault diagnosis method, which effectively improved the feature extraction and generalization ability of bearing fault diagnosis. Chang et al.[11] proposed an efficient and lightweight residual network fault diagnosis method based on attention mechanism, which maintained high accuracy while reducing time complexity and model size. Zhang et al.[12] presented a fault diagnosis method using attention based dual-scale feature fusion capsule network, designed an attention based dual branch network to calculate the weights of different scale features, and based on this, performed dual scale feature fusion to achieve effective fault recognition. Liu et al.[13] proposed a bearing fault diagnosis method based on multi-scale fusion attention CNN, which learned the importance of fault features through improved attention and achieved high fault recognition accuracy. Xu et al.[14] put forward a multi-scale convolutional neural network fault diagnosis method based on channel space attention mechanism, which achieved good recognition results.
Although the above methods have achieved encouraging results, there are still the following problems: (1) Most models use the ReLU activation function. Since the negative part of the ReLU activation function is always 0, the phenomenon of “neuron death” will occur during model training, resulting in some neurons failing to be activated. (2) Most fault diagnosis methods use convolution to extract features first, and then use the spatial or channel attention mechanism to adjust the feature weights, which inevitably leads to the loss of channel or feature information. Aiming at the above problems, an end-to-end rolling bearing fault diagnosis method based on BICNN is proposed. A M-ReLU activation function is proposed for the first question, which can enhance the model’s feature learning ability while avoiding the phenomenon of “neuron death”. For the second issue, a bidirectional interactive feature extraction module and a feature enhancement module are designed. The bidirectional interactive feature extraction module can simultaneously extract channel and spatial feature information, making the model have stronger feature extraction and generalization capabilities. The feature enhancement module controls the degree of information retention through the gating unit, enabling the model to focus on more important feature information.
The subsequent arrangement is as follows: the section 2 introduces some relevant basic theories. The section 3 presents the fault diagnosis approach, and gives the framework of the model as well as the structural analysis. The section 4 describes the data set and conducts comparative experiments and performance analysis. Finally, some conclusions are summarized in the section 5.