Accidental failures of rotating machinery components such as rolling bearings may trigger the sudden breakdown of the whole manufacturing system, thus, fault diagnosis is vital in industry to avoid these massive economical costs and casualties. Since convolutional neural networks (CNN) are poor in extracting reliable features from original signal data, the time-frequency analysis method is usually called for to transform 1D signal into a 2D time-frequency coefficient matrix in which richer information could be exposed more easily. However, realistic fault diagnosis applications face a dilemma in that signal time-frequency analysis and fault classification cannot be implemented together, which means manual signal conversion work is also needed, which reduces the integrity and robustness of the fault diagnosis method. In this paper, a novel network named WPT-CNN is proposed for end-to-end intelligent fault diagnosis of rolling bearings. WPT-CNN creatively uses the standard deep neural network structure to realize the wavelet packet transform (WPT) time-frequency analysis function, which seamlessly integrates fault diagnosis domain knowledge into deep learning algorithms. The overall network architecture can be trained with gradient descent backpropagation algorithms, indicating that the time-frequency analysis module of WPT-CNN is also able to learn the dataset characteristics, adaptively representing signal information in the most suitable way. Two experimental rolling bearing fault datasets were used to validate the proposed method. Testing results showed that WPT-CNN obtained the testing accuracies of 99.73% and 99.89%, respectively, in two datasets, which exhibited a better and more reliable diagnosis performance than any other existing deep learning and machine learning methods.
Deep residual networks (DRNs) are a state-of-the-art deep learning model used in the data-driven fault diagnosis field. Their especially deep architectures give them sufficient capacity to deal with very complex diagnosis issues. However, a neural network with excellent performance usually requires hundreds of thousands of parameters, which is unaffordable for use in current industrial machines due to their limited computational resources. To enable practical applications for fault diagnosis, developing deep learning methods that can perform powerfully and have an economical computational burden is necessary. This study proposes a novel bearing fault diagnosis method based on the wavelet packet transform (WPT) and a lightweight variant of DRN called a multi-branch deep residual network (MB-DRN) in order to resolve the above issues. WPT is utilized to map raw signals into the time-frequency domain, from which the MB-DRN can extract a set of robust features more easily. Additionally, MB-DRN builds several small-sized convolutional layer branches in each building block to increase the network non-linearity, the construction of layer branches can be achieved freely and this design strategy largely saves the parameter usage while approaching a stronger model’s capacity. Two rolling bearing datasets with variable operating conditions were conducted on the proposed method to validate performance. The results verify the necessity of the WPT-based data processing method and show that MB-DRN can outperform the accuracies of standard DRN with only one quarter of the parameter amount, revealing the significant potential of the proposed method for realistic industrial fault diagnosis applications.
Over the past few years, deep learning–based techniques have been extensively and successfully adopted in the field of fault diagnosis. As the diagnosis tasks become more complicated, the structure of the traditional convolutional neural network (CNN) has to become deeper to deal with them, while the gradient of fault features may vanish within the deep network. In addition, all the features are treated equally in the traditional CNN, which cannot make the most of the representation power of CNN. Here, we proposed a method named dual attention dense convolutional network to handle these issues, which is constructed by the dense network and the dual attention block. On one hand, the dense connections and concatenation layers can reinforce the propagation of fault features among layers and mitigate the vanishing gradient phenomenon in the deep network. On the other hand, as the features flow through the channel attention and spatial attention within the dual attention block, this attention mechanism can learn which feature to emphasize or suppress and then obtain the cross-channel and cross-spatial weights of the features. These weights can make the most of the abundant information, elevating the expressive power of network. After passing through these dense and attention blocks, the generated high-level features are then fed into the final classification layer to obtain diagnosis results. The effectiveness of the dual attention dense convolutional network is validated by eight datasets of spindle bearings under various machinery conditions. Compared with eight other approaches including support vector machines, random forest, and six existing deep learning models, the results indicate that the proposed dual attention dense convolutional network possesses higher accuracy, fewer parameters and computations, and faster convergence under complex operational conditions.
Modern machinery becomes more precious with the advance of science, and fault diagnosis is vital for avoiding economical losses or casualties. Among massive diagnosis methods, deep learning algorithms stand out to open an era of intelligent fault diagnosis. Deep residual networks are the state-of-the-art deep learning models which can continuously improve performance by deepening the network structures. However, in vibration-based fault diagnosis, the transient property instability of vibration signal usually calls for time–frequency analysis methods, and the characters of time–frequency matrices are distinct from standard images, which brings some natural limitations for the diagnosis performance of deep learning algorithms. To handle this issue, an enhanced deep residual network named the multilevel correlation stack-deep residual network is proposed in this article. Wavelet packet transform is used to preprocess the sensor signal, and then the proposed multilevel correlation stack-deep residual network uses kernels with different shapes to fully dig various kinds of useful information from any local regions of the processed input. Experiments on two rolling bearing datasets are carried out. Test results show that the multilevel correlation stack-deep residual network exhibits a more satisfactory classification performance than original deep residual networks and other similar methods, revealing significant potentials for realistic fault diagnosis applications.
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