Multiscale Convolutional Neural Network With Feature Alignment for Bearing Fault Diagnosis

Chen, Junbin; Huang, Ruyi; Zhao, Kun; Wang, Wei; Liu, Longcan; Li, Weihua

doi:10.1109/tim.2021.3077673

Cited by 54 publications

(29 citation statements)

References 35 publications

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“…As introduced in 2.1.2, GAP has the ability to allow different input size signals to go through the network and get the output, known as feature alignment [51], which is not available in the traditional FC layer. With the increase of input signal length, the accuracies and testing time of the model both increase.…”

Section: Effectiveness Of the Adopted Gap Structurementioning

confidence: 99%

Intelligent fault diagnosis of rotating machinery using lightweight network with modified tree‐structured parzen estimators

Liang

Liao

Chen

et al. 2022

IET Collab Intel Manufact

Self Cite

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Deep learning-based methods have been widely used in the field of rotating machinery fault diagnosis. It is of practical significance to improve the calculation speed of the model on the premise of ensuring accuracy, so as to realise real-time fault diagnosis. However, designing an efficient and lightweight fault diagnosis network requires expert knowledge to determine the network structure and adjust the hyperparameters of the network, which is time-consuming and laborious. In order to design fault diagnosis networks considering both time and accuracy effortlessly, a novel lightweight network with modified treestructured parzen estimators (LN-MT) is proposed for intelligent fault diagnosis of rotating machinery. Firstly, a lightweight framework based on global average pooling and group convolution is proposed, and a hyperparameter optimisation (HPO) method based on Bayesian optimisation called tree-structured parzen estimator is utilised to automatically search the optimal hyperparameters for the fault diagnosis task. The objective of the HPO algorithm is the weighting of accuracy and calculating time, so as to find models that balance both time and accuracy. The results of comparison experiments indicate that LN-MT can achieve superior fault diagnosis accuracies with few trainable parameters and less calculating time. K E Y W O R D S convolutional neural network, fault diagnosis, hyperparameter optimisation, neural architecture search | INTRODUCTIONModern electromechanical equipment is playing an important role in manufacturing and industry. Gears and bearings are key components of rotating machinery [1,2], the failure of which will greatly affect the performance of the machine, resulting in serious security risk and economic loss [3][4][5]. In order to ensure the continuous and stable operation of the equipment, an algorithm that can timely diagnose the failure of rotating machinery is required [6][7][8], so as to reduce economic losses. Hence, it is of great significance to develop a fault diagnosis algorithm for rotating machinery [9].

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Section: Effectiveness Of the Adopted Gap Structurementioning

confidence: 99%

Intelligent fault diagnosis of rotating machinery using lightweight network with modified tree‐structured parzen estimators

Liang

Liao

Chen

et al. 2022

IET Collab Intel Manufact

Self Cite

View full text Add to dashboard Cite

show abstract

“…To overcome the above-mentioned technical problems, researchers have conducted intensive studies. For example, Chen et al [20] introduced a multi-scale CNN with parallel branches of four kernel sizes for bearing fault diagnosis. But the proposed model lacks an interaction strategy between different branches.…”

Section: Introductionmentioning

confidence: 99%

“…Shi et al [22] proposed a parallel CNN with kernel sizes of geometric series to extract multi-scale features and introduces the introduction of residual connections to prevent the loss of useful information. But the proposed model has the same problem as [20] which lacks the interaction strategies between different branches. Liu et al [23] proposed a multi-scale parallel CNN consisting of three branches with different kernel sizes.…”

Section: Introductionmentioning

confidence: 99%

Identifying Bearing Faults Using Multiscale Residual Attention and Multichannel Neural Network

Lee

Zhuo

2023

IEEE Access

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To solve the problem of the low signal-to-noise ratio and fault features can only be extracted from a single scale of traditional convolutional neural network (CNN) in vibration-based bearing fault diagnosis, this paper proposes a new multi-scale residual attention and multi-channel network (MSCNet), which can effectively reduce noise and fully extract meaningful features from different scales of the signal. The proposed method combines filtering methods to remove redundant parts and noise in the signal, and multiple filtered signals are input into the proposed CNN. The proposed CNN can perform multi-scale feature extraction on the signal and make the network focus on valuable information in the feature through the residual attention mechanism. Therefore, MSCNet achieves better performance. Experimental results on two published bearing datasets show that MSCNet achieves higher accuracy than five state-of-the-art (SOTA) networks in strong noise environments. INDEX TERMSConvolutional neural network (CNN), bearing fault diagnosis, multi-scale feature extraction, multi-channel network.

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“…DL-based fault diagnosis methods have achieved remarkable achievements, including convolutional neural networks (CNNs) [10][11][12] and recurrent neural networks (RNNs) [13][14][15]. CNNs have been widely applied in fault diagnosis because of their locality and translation equivariance, which enable CNNs with extraordinary capability to learn the local features and easy to be trained with small datasets [10][11][12]. Luo et al [16] trained the deep convolutional neural network (DCNN) with an explicable training guide for fault diagnosis of planetary gearbox and obtained ideal diagnosis results.…”

Section: Introductionmentioning

confidence: 99%

Effective Convolutional Transformer for Highly Accurate Planetary Gearbox Fault Diagnosis

Sun

Wang

et al. 2022

IEEE Open J. Instrum. Meas.

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To extract the global temporal correlations and local features together to enhance the accuracy for fault diagnosis, this paper proposes an effective convolutional Transformer (ECT), which can learn the global temporal correlations using Transformer and local features with convolution at the same time. The proposed method designs a multi-stage hierarchical structure of Transformer, which utilizes convolutional tokenization to distill dominating sequence features from raw vibration signals while increasing the dimension of token embedding across stages at the same time as that in CNNs. The spatial-reduction attention (SRA) and the linear dimension reduction projections are introduced respectively to Transformer at different stages to reduce the resource consumption of the model. Finally, the proposed method utilizes a sequence pooling strategy on the output of Transformer to eliminate the requirement of the class token and make the model accurate for classification. The specially designed structure makes the model flexible and effective for planetary gearbox fault diagnosis. Experiments performed on planetary gearbox fault simulators indicate that the ECT method has significant effectiveness and high accuracy compared with the state-of-the-art methods for planetary gearbox fault diagnosis.

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Multiscale Convolutional Neural Network With Feature Alignment for Bearing Fault Diagnosis

Cited by 54 publications

References 35 publications

Intelligent fault diagnosis of rotating machinery using lightweight network with modified tree‐structured parzen estimators

Intelligent fault diagnosis of rotating machinery using lightweight network with modified tree‐structured parzen estimators

Identifying Bearing Faults Using Multiscale Residual Attention and Multichannel Neural Network

Effective Convolutional Transformer for Highly Accurate Planetary Gearbox Fault Diagnosis

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