A Hierarchical Training-Convolutional Neural Network for Imbalanced Fault Diagnosis in Complex Equipment

Gao, Yiping; Gao, Liang; Li, Xinyu; Cao, Siyu

doi:10.1109/tii.2022.3177662

Cited by 27 publications

(6 citation statements)

References 28 publications

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“…In this case, faults are not common, which leads to imbalanced data, and therefore, one cannot propose CNN methods directly. In order to address this problem, a hierarchical training CNN is implemented in [47]. At first, the method uses a number-resampling technique to balance data.…”

Section: Related Workmentioning

confidence: 99%

Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks

et al. 2023

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Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models from achieving satisfactory results. ID is the occurrence of a situation where the quantity of the samples belonging to one class outnumbers that of the other by a wide margin, making such models’ learning process biased towards the majority class. In recent years, to address this issue, several solutions have been put forward, which opt for either synthetically generating new data for the minority class or reducing the number of majority classes to balance the data. Hence, in this paper, we investigate the effectiveness of methods based on Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) mixed with a variety of well-known imbalanced data solutions meaning oversampling and undersampling. Then, we propose a CNN-based model in combination with SMOTE to effectively handle imbalanced data. To evaluate our methods, we have used KEEL, breast cancer, and Z-Alizadeh Sani datasets. In order to achieve reliable results, we conducted our experiments 100 times with randomly shuffled data distributions. The classification results demonstrate that the mixed Synthetic Minority Oversampling Technique (SMOTE)-Normalization-CNN outperforms different methodologies achieving 99.08% accuracy on the 24 imbalanced datasets. Therefore, the proposed mixed model can be applied to imbalanced binary classification problems on other real datasets.

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Section: Related Workmentioning

confidence: 99%

Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks

et al. 2023

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“…By increasing the value of the attenuation factor, the dominance of easy positive samples on the loss can be reduced, and thus the weight of the classes (minority) can be increased. Then, for the positive samples, a dynamic positive attenuation factor is proposed, as shown in Equation (18).…”

Section: Variable-asymmetric Focal Lossmentioning

confidence: 99%

“…Nevertheless, the rotating machinery systems often operate in a healthy state, and the collected fault samples only account for a small part. DL models will be dominated by classes with sufficient samples and ignore the minority classes with insufficient feature understanding [18][19][20], which leads to overfitting. If the model is severely biased, resulting in a sharp decrease in the classification accuracy of the minority class, it will influence the maintenance efficiency of the mechanical system.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Network with Variable Asymmetric Rebalancing Strategy for Small and Imbalanced Fault Diagnosis

et al. 2022

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Deep learning-related technologies have achieved remarkable success in the field of intelligent fault diagnosis. Nevertheless, the traditional intelligent diagnosis methods are often based on the premise of sufficient annotation signals and balanced distribution of classes, and the model structure is so complex that it requires huge computational resources. To this end, a lightweight class imbalanced diagnosis framework based on a depthwise separable Laplace-wavelet convolution network with variable-asymmetric focal loss (DSLWCN-VAFL) is established. Firstly, a branch with few parameters for time-frequency feature extraction is designed by integrating wavelet and depthwise separable convolution. It is combined with the branch of regular convolution that fully learns time-domain features to jointly capture abundant discriminative features from limited samples. Subsequently, a new asymmetric soft-threshold loss, VAFL, is designed, which reasonably rebalances the contributions of distinct samples during the model training. Finally, experiments are conducted on the data of bearing and gearbox, which demonstrate the superiority of the DSLWCN-VAFL algorithm and its lightweight diagnostic framework in handling class imbalanced data.

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“…Ji et al proposed an order-tracking method using 1DCNN in two steps for the fault diagnosis of variable condition signals [21]. Gao et al proposed the use of hierarchically trained CNNs to overcome the problem of unbalanced data distribution during fault diagnosis [22]. Therefore, it is evident that PC-based CNNs have been widely applied in fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

An Edge Intelligent Method for Bearing Fault Diagnosis Based on a Parameter Transplantation Convolutional Neural Network

et al. 2023

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A bearing is a key component in rotating machinery. The prompt monitoring of a bearings’ condition is critical for the reduction of mechanical accidents. With the rapid development of artificial intelligence technology in recent years, machine learning-based intelligent fault diagnosis (IFD) methods have achieved remarkable success in the field of bearing condition monitoring. However, most algorithms are developed based on computer platforms that focus on analyzing offline, rather than real-time, signals. In this paper, an edge intelligence diagnosis method called S-AlexNet, which is based on a parameter transplantation convolutional neural network (CNN), is proposed. The method deploys the lightweight IFD method in a low-cost embedded system to monitor the bearing status in real time. Firstly, a lightweight IFD algorithm model is designed for embedded systems. The model is trained on a PC to obtain optimal parameters, such as the model’s weights and bias. Finally, the optimal parameters are transplanted into the embedded system model to identify the bearing status on the edge side. Two datasets were used to validate the performance of the proposed method. The validation using the CWRU dataset shows that the proposed method achieves an average prediction accuracy of 94.4% on the test set. The validation using self-built data shows that the proposed method can identify bearing operating status in embedded systems with an average prediction accuracy of 99.81%. The results indicate that the proposed method has the advantages of high recognition accuracy, low model complexity, low cost, and high portability, which allow for the simple and effective implementation of the edge IFD of bearings in embedded systems.

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A Hierarchical Training-Convolutional Neural Network for Imbalanced Fault Diagnosis in Complex Equipment

Cited by 27 publications

References 28 publications

Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks

Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks

Lightweight Network with Variable Asymmetric Rebalancing Strategy for Small and Imbalanced Fault Diagnosis

An Edge Intelligent Method for Bearing Fault Diagnosis Based on a Parameter Transplantation Convolutional Neural Network

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