A High-Stability Diagnosis Model Based on a Multiscale Feature Fusion Convolutional Neural Network

Wang, Pengxin; Song, Liuyang; Guo, Xudong; Wang, Huaqing; Cui, Lingli

doi:10.1109/tim.2021.3102745

Cited by 17 publications

(7 citation statements)

References 39 publications

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“…The emergence of Deep Learning (DL) can solve the problem of relying on the workforce [ 11 ]. The DL model represented by the Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM) neural network is used in fault diagnosis, with promising results because of the powerful feature extraction capabilities [ 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. Among most research, applying DL to a fault diagnosis requires two preconditions: (1) Test samples and samples participating in model training have the same label space.…”

Section: Introductionmentioning

confidence: 99%

Cross-Domain Open Set Fault Diagnosis Based on Weighted Domain Adaptation with Double Classifiers

Wang

Tong

et al. 2023

Sensors

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The application of transfer learning in fault diagnosis has been developed in recent years. It can use existing data to solve the problem of fault recognition under different working conditions. Due to the complexity of the equipment and the openness of the working environment in industrial production, the status of the equipment is changeable, and the collected signals can have new fault classes. Therefore, the open set recognition ability of the transfer learning method is an urgent research direction. The existing transfer learning model can have a severe negative transfer problem when solving the open set problem, resulting in the aliasing of samples in the feature space and the inability to separate the unknown classes. To solve this problem, we propose a Weighted Domain Adaptation with Double Classifiers (WDADC) method. Specifically, WDADC designs the weighting module based on Jensen–Shannon divergence, which can evaluate the similarity between each sample in the target domain and each class in the source domain. Based on this similarity, a weighted loss is constructed to promote the positive transfer between shared classes in the two domains to realize the recognition of shared classes and the separation of unknown classes. In addition, the structure of double classifiers in WDADC can mitigate the overfitting of the model by maximizing the discrepancy, which helps extract the domain-invariant and class-separable features of the samples when the discrepancy between the two domains is large. The model’s performance is verified in several fault datasets of rotating machinery. The results show that the method is effective in open set fault diagnosis and superior to the common domain adaptation methods.

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Section: Introductionmentioning

confidence: 99%

Cross-Domain Open Set Fault Diagnosis Based on Weighted Domain Adaptation with Double Classifiers

Wang

Tong

et al. 2023

Sensors

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“…Bearings are a key component of rotating machinery [1]. Once a bearing fails, it will affect the normal operation of the equipment and may cause incalculable consequences [2,3]. The remaining useful life (RUL) prediction of bearings can ensure the stability of equipment, avoid catastrophic events, the model-based methods, the data-driven methods are used to analyze problems by mapping the relationship between monitoring signals and RUL values without models that are generally applicable to complex systems.…”

Section: Introductionmentioning

confidence: 99%

Operation stage division and RUL prediction of bearings based on 1DCNN-ON-LSTM

Guo,

Li,

Huang

2023

Meas. Sci. Technol.

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Remaining useful life (RUL) prediction of bearings is significantly important to ensure reliable operation of bearings. In practice, it is routinely impossible to obtain the full life cycle degradation data of bearings that needs to be used in prediction. The accuracy of the RUL prediction of bearings is often affected by incomplete degradation data. Regarding this situation, this paper proposes a multi-sensor three-stage RUL prediction framework based on the one-dimensional convolutional ordered neuron long short-term memory (1DCNN-ON-LSTM) neural network. Firstly, 1DCNN is used to extract spatial features adaptively from multi-sensor’s data and fuse them into one-dimensional feature. Next, the unsupervised hierarchy mechanism of time series information based ON-LSTM is developed to determine the ‘initial degradation stage point’ and ‘rapid degradation stage point’ of the bearing from the one-dimensional feature. Once the signal features collected by sensors input to the model reach the degradation stage point, select the corresponding sensitive features as input and construct the 1DCNN-ON-LSTM model that performs RUL prediction after the degradation stage point to improve the prediction accuracy of the model. Based on the proposed hierarchy mechanism, the bearings’ operation process is divided into three operation stages: normal stage, initial degradation stage and rapid degradation stage. Finally, the experiments verify that the proposed method can effectively divide the operation stages of bearings to predict the RUL and improve the generalization ability and prediction accuracy of the model.

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“…Eren et al [18] introduced CNN to the field of fault diagnosis research by using a one-dimensional convolutional neural network to train a deep learning model directly on the original signals. Zhang et al [19] built a deep convolutional network (WDCNN) with a wide first layer kernel, where a wide kernel was used in the first convolutional layer to extract features and suppress high frequency noise as a way to improve diagnostic accuracy. Zhang et al [20] used a neural network with a residual structure, which greatly improved the flow of information throughout the network.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent diagnosis method for rolling bearings based on Ghost module and adaptive weighting module

Qiang

Zhao

2023

Preprint

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The vibration signals of rolling bearings are inevitably influenced by noise as well as by working conditions. The use of one-dimensional original signals converted into images for rolling bearing fault diagnosis has achieved good results, but ignores the large size of the model and the speed of diagnosis, so it is not suitable for practical fault diagnosis. To address this problem, we propose a neural network based on Ghost modules and dynamic attention mechanisms. The method uses the Ghost module and coordinate attention to compress model size and reduce computational effort while improving the network's perception of the input signals. In addition, to enable efficient use of similar feature maps generated by convolution, an adaptive weighting module is proposed to further simplify the learning process and reduce network training time. The validity of the proposed method was verified on a dataset provided by Case Western Reserve University and the Mechanical Failure Prevention Technology Society. The results show that the proposed method not only has good noise immunity in noisy environments, but also has good diagnostic performance and good generalization performance under different loads. Under the same experimental conditions, the method proposed in this paper is compared with some methods and the results show the superiority of the proposed method in the diagnosis of rolling bearing faults.

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A High-Stability Diagnosis Model Based on a Multiscale Feature Fusion Convolutional Neural Network

Cited by 17 publications

References 39 publications

Cross-Domain Open Set Fault Diagnosis Based on Weighted Domain Adaptation with Double Classifiers

Cross-Domain Open Set Fault Diagnosis Based on Weighted Domain Adaptation with Double Classifiers

Operation stage division and RUL prediction of bearings based on 1DCNN-ON-LSTM

An Intelligent diagnosis method for rolling bearings based on Ghost module and adaptive weighting module

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