Fault Diagnosis for Rolling Bearings Based on Multiscale Feature Fusion Deep Residual Networks

Abstract

Deep learning, due to its excellent feature-adaptive capture ability, has been widely utilized in the fault diagnosis field. However, there are two common problems in deep-learning-based fault diagnosis methods: (1) many researchers attempt to deepen the layers of deep learning models for higher diagnostic accuracy, but degradation problems of deep learning models often occur; and (2) the use of multiscale features can easily be ignored, which makes the extracted data features lack diversity. To deal with these problems, a novel multiscale feature fusion deep residual network is proposed in this paper for the fault diagnosis of rolling bearings, one which contains multiple multiscale feature fusion blocks and a multiscale pooling layer. The multiple multiscale feature fusion block is designed to automatically extract the multiscale features from raw signals, and further compress them for higher dimensional feature mapping. The multiscale pooling layer is constructed to fuse the extracted multiscale feature mapping. Two famous rolling bearing datasets are adopted to evaluate the diagnostic performance of the proposed model. The comparison results show that the diagnostic performance of the proposed model is superior to not only several popular models, but also other advanced methods in the literature.

Dual-path multi-scale attention residual network for fault diagnosis of rolling bearings under complex operating conditions

“…In CNNs, non-linear mappings in each layer cause some loss of information, worsening as network depth increases and complicating training [31]. He et al [32] introduced ResNet to address these issues, using residual blocks with shortcut connections for achieving direct feature transmission between non-adjacent layers.…”

Lightweight Tunnel Defect Detection Algorithm Based on Knowledge Distillation

“…The YOLO algorithm introduces the concept of residual blocks in the training process to avoid problems such as gradient disappearance and low training efficiency due to the depth of the network [26] as shown in Figure 3. The introduction of the residual structure allows YOLO to achieve deeper and easier training while improving classification accuracy [27,28]. The improved residual structure is applied to the C3 module of the YOLO algorithm, as shown in Figure 4.…”

“…The new feature information is obtained by normalizing the output of the two pooling layers by giving weights to the weights, which are fed into the convolution layer as an The YOLO algorithm introduces the concept of residual blocks in the training process to avoid problems such as gradient disappearance and low training efficiency due to the depth of the network [26] as shown in Figure 3. The introduction of the residual structure allows YOLO to achieve deeper and easier training while improving classification accuracy [27,28]. The improved residual structure is applied to the C3 module of the YOLO algorithm, as shown in Figure 4.…”

Fault diagnosis of rolling bearings based on dynamic convolution and dual-channel feature fusion under variable working conditions

“…The local maximum mean discrepancy method was employed to attain precise alignment of the distribution among interconnected subfields within the same class. Wu et al [18] introduced a diagnostic model that incorporates the domain adversarial neural network and an attention mechanism to address issues related to rolling bearing failures. This model aims to reduce the impact of noisy data on feature extraction and enhance the quality of learnt features.…”