Transfer learning with inception ResNet-based model for rolling bearing fault diagnosis

Liu, Yongbao; Li, Jun; LI, Qijie; Wang, Qiang

doi:10.1299/jamdsm.2022jamdsm0023

Cited by 13 publications

(6 citation statements)

References 46 publications

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“…It can be seen that the features extracted by STFT + CNN, STFT + ResNet, enhanced Inception-ResNet-v2, and RGB + Inception-ResNet-v2 are all severely confounded; SKNet + Inception-v4, SKNet + Inception-ResNet-v2, and NewSKNet + Inception-ResNet-v2 extracted features are also slightly aliased. In contrast, in the features extracted by SIR-CNN, the samples of the same category are completely Model Accuracy (%) STFT + CNN [41] 87.9 STFT + ResNet [46] 89.7 Enhanced Inception-ResNet-v2 [34] 91.0 RGB + Inception-ResNet-v2 [30] 92.5 SKNet + Inception-v4 [47] 96.3 SKNet + Inception-ResNet-v2 96.9 NewSKNet + Inception-ResNet-v2 98.1 SIR-CNN (ours) 99.9 aggregated together, and the samples of diferent categories are completely separated, which again indicates that the SIR-CNN-based fault identifcation method has a more powerful feature extraction capability and superior classifcation performance.…”

Section: Shock and Vibrationmentioning

confidence: 99%

“…To solve the above problems, Google proposed Inception-ResNet [29], which not only solved the problems of gradient vanishing and loss value increasing but also deepened the network and achieved higher recognition accuracy, receiving widespread attention from experts. For example, Liu et al [30] achieved transfer learning based on the Inception-ResNet-v2 model by converting raw data into RGB images. Li et al [31] proposed a bearing fault diagnosis method that combined fault signal spectrum images with the Inception-ResNet-v2 model, achieving good classifcation accuracy.…”

Section: Introductionmentioning

confidence: 99%

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An Integrated Bearing Fault Diagnosis Method Based on Multibranch SKNet and Enhanced Inception-ResNet-v2

Hu,

Liu,

et al. 2024

Shock and Vibration

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Deep learning has recently received extensive attention in the field of rolling-bearing fault diagnosis owing to its powerful feature expression capability. With the help of deep learning, we can fully extract the deep features hidden in the data, significantly improving the accuracy and efficiency of fault diagnosis. Despite this progress, deep learning still faces two outstanding problems. (1) Each layer uses the same convolution kernel to extract features, making it difficult to adaptively select convolution kernels based on the features of the input image, which limits the network’s adaptability to different input features and leads to weak feature extraction. (2) Large number of parameters and long training time. To solve the above problems, this paper proposes an integrated deep neural network that combines an improved selective kernel network (SKNet) with an enhanced Inception-ResNet-v2, named SIR-CNN. First, based on the SKNet, a new three-branch SKNet was designed. Second, the new SKNet is embedded into a depthwise separable convolution network such that the model can adaptively select convolution kernels of different sizes during training. Furthermore, the convolution structure in the Inception-ResNet-v2 network was replaced by the improved depthwise separable convolution network to achieve effective feature extraction. Finally, the time-frequency maps of the raw vibration signals are obtained through short-time Fourier transform (STFT) and then sent to the proposed SIR-CNN network for experiments. The experimental results show that the proposed SIR-CNN achieves superior performance compared to other methods.

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Section: Shock and Vibrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Integrated Bearing Fault Diagnosis Method Based on Multibranch SKNet and Enhanced Inception-ResNet-v2

Hu,

Liu,

et al. 2024

Shock and Vibration

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“…In response to this issue, He et al [16] introduced and analyzed the performance of deep residual networks for image pattern recognition. Liu et al [17] proposed a novel fault diagnosis model based on the Inception-ResNet-v2 model and achieved high fault classification accuracy. Jaber et al [18] proposed a ResNet-based deep learning multilayer fault detection model to enrich high performance.…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis based on transfer learning with dual-flow manifold ResNet and improved CapsNet

Yao,

Wang,

Tao

et al. 2024

Meas. Sci. Technol.

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In intelligent fault diagnosis for rolling bearings, the variable operating conditions of rotating machinery lead to inconsistent distributions between training and testing data, inevitably decreasing fault diagnosis accuracy. To overcome this issue, we propose a bearing fault diagnosis method based on transfer learning with a dual-flow manifold residual network and improved capsule network (DMRCN). Firstly, we introduce a dual-flow manifold residual network (DMResNet) structure consisting of continuous wide-kernel convolution, continuous narrow-kernel convolution, and manifold learning spatial information description blocks. This architecture is utilized to extract low-level features from the data. Then, we present a novel architecture of the capsule network with self-attention output (SAO-CN). We incorporate a self-attention mechanism into the dynamic routing algorithm (SA-DR) to transfer richer high-level features from the low-level capsule layer to the higher-level capsule layer. During training, we propose a loss function known as global-local distribution discrepancy loss (G-L-DDL) to enhance the model's generalization ability and accelerate model convergence. Finally, we validate the performance of DMRCN in cross-condition transfer fault diagnosis on both the CWRU public dataset and a laboratory-built bearing experimental rig dataset. Experimental results demonstrate that DMRCN outperforms other commonly used deep transfer learning methods regarding fault diagnosis across different operating conditions.

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“…Although this structure enhances the extraction of features in multiple dimensions, it also causes a large amount of data redundancy and creates an overfitting problem. The literature [15] uses the stacking idea of Inception to propose a stacked convolutional layer, which is inserted into the ResNet network and effectively extracts feature information from faulty data. With the continuous development of deep learning, a simpler and lighter attention mechanism has been proposed.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Study of MBR Membrane Fouling based on CA-ResNet18

Zeng

2023

FCIS

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In this paper, we propose a diagnostic model for addressing difficult-to-diagnose factors that influence membrane fouling, using a residual neural network (ResNet) optimized with a coordinate attention mechanism. After pre-processing fouling data from the membrane bioreactor using Principal Component Analysis (PCA) to derive nine categories of fouling factors, we determined the residual neural network structure and optimized it using the Coordinate Attention Mechanism (CA) to enhance feature extraction, improve diagnosis accuracy, and establish a stable and reliable diagnostic model for membrane scaling. Through experimental verification, the ResNet with the addition of the CA attention mechanism outperformed the ResNet with other attention mechanisms and the traditional ResNet in terms of prediction accuracy and convergence speed, achieving the research goal of accurately diagnosing the causes of membrane scaling.

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Transfer learning with inception ResNet-based model for rolling bearing fault diagnosis

Cited by 13 publications

References 46 publications

An Integrated Bearing Fault Diagnosis Method Based on Multibranch SKNet and Enhanced Inception-ResNet-v2

An Integrated Bearing Fault Diagnosis Method Based on Multibranch SKNet and Enhanced Inception-ResNet-v2

Bearing fault diagnosis based on transfer learning with dual-flow manifold ResNet and improved CapsNet

Diagnostic Study of MBR Membrane Fouling based on CA-ResNet18

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