A review of the application of deep learning in intelligent fault diagnosis of rotating machinery

An Intelligent Fault Diagnosis Method of Rolling Bearings Based on Short-Time Fourier Transform and Convolutional Neural Network

Bearings are key components of rotating machinery, and their fault diagnosis is essential for machinery operation. Bearing vibration signals belong to time series data, but traditional convolutional neural networks (CNNs) or recurrent neural networks cannot fully extract the fault features from these signals. To address the insufficient feature extraction and poor noise resistance, this paper proposes a fault diagnosis model based on continuous wavelet transform (CWT), CNN with channel attention, bidirectional long short-term memory network (BiLSTM) and residual module. Firstly, a parallel dual-path feature extraction mechanism is constructed which takes time-domain signals and time–frequency images transformed via CWT as the input respectively. Then BiLSTM extracts the time features of the signal as one path, and the CNN with efficient channel attention extracts the spatial features as the other path. This parallel neural network contributes to better feature extraction. Then, the residual module is applied to extract the global features to further improve the feature extraction ability and noise immunity. The experimental results demonstrate that the proposed model on the Case Western Reserve University dataset has better diagnostic accuracy under different working conditions and different signal-to-noise ratios than other methods. In addition, the model shows good generalization performance on Jiangnan University dataset.

Bearing fault diagnosis based on CNN-BiLSTM and residual module

This article introduces a novel framework for diagnosing faults in rolling bearings. The framework combines digital twin data, transfer learning theory, and an enhanced ConvNext deep learning network model. Its purpose is to address the challenges posed by the limited actual fault data density and inadequate result accuracy in existing research on the detection of rolling bearing faults in rotating mechanical equipment. To begin with, the operational rolling bearing is represented in the digital realm through the utilization of a digital twin model. The simulation data produced by this twin model replace traditional experimental data, effectively creating a substantial volume of well-balanced simulated datasets. Next, improvements are made to the ConvNext network by incorporating an unparameterized attention module called the Similarity Attention Module (SimAM) and an efficient channel attention feature referred to as the Efficient Channel Attention Network (ECA). These enhancements serve to augment the network’s capability for extracting features. Subsequently, the enhanced network model is trained using the source domain dataset. Simultaneously, the trained model is transferred to the target domain bearing using transfer learning techniques. This transfer learning process enables the accurate fault diagnosis of the main bearing to be achieved. Finally, the proposed method’s feasibility is validated, and a comparative analysis is conducted in comparison with similar approaches. The comparative study demonstrates that the proposed method effectively addresses the issue of low mechanical equipment fault data density, leading to improved accuracy in fault detection and classification, along with a certain level of robustness.

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Research on Rolling Bearing Fault Diagnosis Based on Digital Twin Data and Improved ConvNext

Rolling bearings play a crucial role as components in mechanical equipment. Malfunctioning rolling bearings can disrupt the normal operation of the equipment and pose safety hazards. Traditional deep learning-based methods for diagnosing faults in rolling bearings present several issues, such as insufficient feature information of fault samples, high model complexity and low accuracy. To overcome these challenges, this paper introduces an intelligent approach for rolling bearing fault diagnosis using intrinsic feature extraction and convolutional block attention module (CBAM)-enhanced InceptionNet. In our researches, variational mode decomposition (VMD) is adopted to decompose the original signal into multiple band-limited intrinsic mode functions (BLIMFs). In the decomposition process of VMD, the number of decomposition layers k is determined by center frequency method and the optimal BLIMF is chosen based on minimum envelope entropy. Subsequently, the continuous wavelet transform is employed to transform the optimal BLIMFs into time-frequency images. Finally, the obtained time-frequency images are fed into the proposed CBAM-enhanced InceptionNet for fault state diagnosis. Experiments on two different datasets prove that the method has stable and reliable accuracy. Comparative experiments have demonstrated that this method can reduce network model parameters and improve diagnosis efficiency while achieving high accuracy.

A novel fault diagnosis approach of rolling bearing using intrinsic feature extraction and CBAM-enhanced InceptionNet

The vibration signals of rolling bearings are affected by changing operating conditions and environmental noise, so they are characterized by multi-scale complexity. Deep residual shrinkage network can achieve bearing fault diagnosis in strong noise environment, but ignore the multi-scale complexity feature. To address this problem, we propose a multi-scale residual shrinkage convolutional neural network for fault diagnosis of rolling bearing. In this method, a multi-scale residual shrinkage layer based on multi-scale learning and a residual shrinkage block is constructed. By stacking multiple multi-scale residual shrinkage layers, the features of vibration signals are automatically learned from the input data. In addition, to establish the connection of different vibration signals and to reduce the number of parameters in the network, we design a separable convolution block using residual connections and separable convolution. By verifying the effectiveness of the proposed method in Case Western Reserve University and Mechanical Failure Prevention Technology datasets, the results show that the proposed method not only has good noise resistance in strong noise environments, but also has high diagnostic accuracy and good generalization performance in different load condition domains. The proposed method is compared with three other deep learning methods under the same experimental conditions, and the results show that it is superior in rolling bearing fault diagnosis.

An intelligent diagnosis method of rolling bearing based on multi-scale residual shrinkage convolutional neural network

Abstract Data-driven quality prediction model has been widely used in product estimation of batch processes. However, the initial conditions of different batches in batch process are different, and the multiphase characteristics and nonlinearity in batch are not conducive to the quality prediction. To solve these problems, a model for batch process quality prediction based on a convolutional neural network (CNN) is proposed. Firstly, in order to enhance data characteristics and reduce model computing time, a maximum information coefficient (MIC) method based on mutual information is used to select variables according to the correlation between process variables and quality variables. Secondly, the quality prediction model of convolutional block attention module (CBAM)-CNN based on the attention mechanism is established. On the one hand, an improved CBAM is fused into the CNN. The input feature mapping is re-calibrated to focus on useful feature information and weaken irrelevant redundant information in each sliding window. On the other hand, by introducing an improved convolutional module with double-band skip connection lines, the backpropagation speed of the CBAM-CNN model is accelerated, which can effectively avoid the occurrence of the overfitting problem. Finally, the data of batch process is used as the input of the prediction model. The superiority and effectiveness of the proposed model are verified by predicting the quality variable of the penicillin fermentation process simulation benchmark and the industrial-scale penicillin fermentation process. It is proved that the proposed model has better generalization performance in the quality prediction of the penicillin fermentation process with different control strategies. 

Deep learning with CBAM-based CNN for batch process quality prediction


 Machine learning techniques have had great success in fault diagnosis. However, the traditional machine learning methods rely heavily on manual priori knowledge leading to poor fault diagnosis results in rolling bearing fault diagnosis. Deep learning techniques can improve the accuracy of fault intelligent diagnosis with the help of automatic extraction of fault features. In this article, a method of smart fault diagnosis for rolling bearings based on DSCB-NFAM is proposed. Firstly, a depth-separable convolutional block (DSCB) is constructed, which can reduce the number of parameters and effectively alleviate the gradient disappearance phenomenon. Then a non-local feature awareness module (NFAM) is constructed to enhance the global sensing ability while adaptively adjusting the weight of each channel. Further, a feature extraction layer is constructed to extend the fault feature of the input information. Finally, a feature pyramid approach is used to fuse the extracted information for output. The validity of the suggested method is tested on both of the bearing datasets. By comparing with several mainstream intelligent diagnosis algorithms, the results of the experiments show that the proposed method has better advancement under complex conditions such as variable load and variable noise.

Rolling bearing fault diagnosis model based on DSCB-NFAM

In industrial systems, the vibration signals of rolling bearings are highly complex due to varying operating conditions and ambient noise. Presented with signals with a lack of features after being disturbed by a complex environment, traditional convolutional neural networks cannot diagnose bearing faults accurately and effectively. This paper proposes a dynamic capsule network (DCCN) based on adaptive shared weights to solve the above problems. To learn the features of the vibration signals, the convolution weights are adaptively adjusted and shared to different convolutional layers through an attention mechanism, which can effectively reduce the computational cost of the network. In addition, a dynamic routing algorithm is used to generate sub-capsules that share weights. These sub-capsules extract and transform the fault features into vector features information storage to reduce feature loss. Finally, the feature-extraction capability of the DCCN is enhanced by concatenating the dynamic convolution using skip connect lines. The effectiveness of the proposed method is verified by noise and variable-load experiments, and the DCCN exhibits a better generalization and diagnosis performance than current advanced deep-learning methods.

Adaptive weight-based capsule neural network for bearing fault diagnosis

Aiming at the problems that the traditional Unsupervised Domain Adaptation fault diagnosis method is unable to sufficiently capture the distributional differences among different source domains, and ignoring the subdomain-level distributional relationships during the alignment process leading to poor diagnosis results, a domain feature decoupling-guided collaborative bearing fault diagnosis method with multi-source domain subnetworks is proposed. First, a shared feature extractor is constructed to map the raw data of each source and target domain to a common feature space, learn the fundamental feature representations common to all domains, and facilitate the transfer of knowledge between different domains. Then, parallel sub-networks are designed to shrink the distributional differences between each pair of source and target domains to extract domain-invariant features. On this foundation, the Class-Wise Weighted Maximum Mean Discrepancy is proposed for fine-grained local alignment of subdomain distributions within the same class, which effectively solves the negative transfer problem in the multi-source domain transfer task. Second, the domain feature decoupling module is designed to enhance the knowledge coverage capability by calculating the multi-source domain adversarial loss and joint sub-network guidance to extract the common features between multi-source and target domains. Finally, a dynamic confidence-based weighted classifier is constructed to adaptively adjust the weight contribution of each source domain. Experimental results on two bearing datasets show that the proposed method can effectively address distributional differences and noise interference in industrial scenarios, and significantly improve the accuracy of bearing fault diagnosis.

Domain feature decoupling-guided cooperative bearing fault diagnosis method with multi-source domain subnetworks

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