LEFE-Net: A Lightweight Efficient Feature Extraction Network With Strong Robustness for Bearing Fault Diagnosis

Fang, Hairui; Deng, Jin; Zhao, Bo; Shi, Yan; Zhou, Jianye; Shao, Siyu

doi:10.1109/tim.2021.3067187

Cited by 48 publications

(21 citation statements)

References 18 publications

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“…The results of the experiment mainly contain the total number of model parameters and model sizes for SVM, 1D-CNN, BS-net and MLS-net under the three dataset. The recently proposed bearing fault diagnosis models ANS-net [22] and LEFE-net [23] are also compared. We jointly determine the merit of a model based on the parameters and the accuracy rate.…”

Section: Model Lightweight Comparisonmentioning

confidence: 99%

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A Multi-Scale and Lightweight Bearing Fault Diagnosis Model with Small Samples

Gao

Gong

2022

Symmetry

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Currently, deep-learning-based methods have been widely used in fault diagnosis to improve the diagnosis efficiency and intelligence. However, most schemes require a great deal of labeled data and many iterations for training parameters. They suffer from low accuracy and over fitting under the few-shot scenario. In addition, a large number of parameters in the model consumes high computing resources, which is far from practical. In this paper, a multi-scale and lightweight Siamese network architecture is proposed for the fault diagnosis with few samples. The architecture proposed contains two main modules. The first part implements the feature vector extraction of sample pairs. It is composed of two lightweight convolutional networks with shared weights symmetrically. Multi-scale convolutional kernels and dimensionality reduction are used in these two symmetric networks to improve feature extraction and reduce the total number of model parameters. The second part takes charge of calculating the similarity of two feature vectors to achieve fault classification. The proposed network is validated by multiple datasets with different loads and speeds. The results show that the model has better accuracy, fewer model parameters and a scale compared to the baseline approach through our experiments. Furthermore, the model is also proven to have good generalization capability.

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Section: Model Lightweight Comparisonmentioning

confidence: 99%

“…Hence, controlling the number of model parameters is extremely important in practical applications. Fang, H. et al proposed a lightweight fault diagnosis model that can solve the problem of too many model parameters [23]. However, it cannot perform fault diagnosis when there are insufficient samples.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Scale and Lightweight Bearing Fault Diagnosis Model with Small Samples

Gao

Gong

2022

Symmetry

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“…Although these methods achieved higher accuracy, they also bring higher model complexity and more computation. Fang et al [32] extended an efficient feature extraction method based on CNN and used a lightweight network to complete high-precision fault diagnosis tasks. The spatial attention mechanism (SAM) is used to adjust the weight of the output feature map.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis Method through ACCC-Based Improved Convolutional Neural Network

Zhang

Huang

Yang³

et al. 2023

Actuators

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Fault diagnosis plays an important role in improving the safety and reliability of complex equipment. Convolutional neural networks (CNN) have been widely used to diagnose faults due to their powerful feature extraction and learning capabilities. In practical industrial applications, the obtained signals always are disturbed by strong and highly non-stationary noise, so the timing relationships of the signals should be highlighted more. However, most CNN-based fault diagnosis methods directly use a pooling layer, which may corrupt the timing relationship of the signals easily. More importantly, due to a lack of an attention mechanism, it is difficult to extract deep informative features from noisy signals. To solve the shortcomings, an intelligent fault diagnosis method is proposed in this paper by using an improved convolutional neural network (ICNN) model. Three innovations are developed. Firstly, the receptive field is used as a guideline to design diagnosis network structures, and the receptive field of the last layer is close to the length of the original signal, which can enable the network to fully learn each sample. Secondly, the dilated convolution is adopted instead of standard convolution to obtain larger-scale information and preserves the internal structure and temporal relation of the signal when performing down-sampling. Thirdly, an attention mechanism block named advanced convolution and channel calibration (ACCC) is presented to calibrate the feature channels, thus the deep informative features are distributed in larger weights while noise-related features are effectively suppressed. Finally, two experiments show the ICNN-based fault diagnosis method can not only process strong noise signals but also diagnose early and minor faults. Compared with other methods, it achieves the highest average accuracy at 94.78% and 90.26%, which are 6.53% and 7.70% higher than the CNN methods, respectively. In complex machine bearing failure conditions, this method can be used to better diagnose the type of failure; in voice calls, this method can be used to better distinguish between voice and noisy background sounds to improve call quality.

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“…However, AAnNet is done at a single feature scale. Fang et al [25] proposed a CNN with better antinoise capability and domain adaptability and studied the splitting of feature maps to greatly reduce the number of parameters. But the proposed model only considers spatial attention.…”

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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LEFE-Net: A Lightweight Efficient Feature Extraction Network With Strong Robustness for Bearing Fault Diagnosis

Cited by 48 publications

References 18 publications

A Multi-Scale and Lightweight Bearing Fault Diagnosis Model with Small Samples

A Multi-Scale and Lightweight Bearing Fault Diagnosis Model with Small Samples

Intelligent Fault Diagnosis Method through ACCC-Based Improved Convolutional Neural Network

Identifying Bearing Faults Using Multiscale Residual Attention and Multichannel Neural Network

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