An Ensemble Convolutional Neural Networks for Bearing Fault Diagnosis Using Multi-Sensor Data

Liu, Yang; Yan, Xunshi; Zhang, Chen-An; Wen, Liu

doi:10.3390/s19235300

Cited by 70 publications

(34 citation statements)

References 51 publications

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“…Each fault monitoring raw data is normalized into the range [0, 1] in advance. The common data normalization is the mean value method [34], and the mathematical expression [20] is described as follows:…”

Section: ) Data Normalizationmentioning

confidence: 99%

A Data-Driven-Based Fault Diagnosis Approach for Electrical Power DC-DC Inverter by Using Modified Convolutional Neural Network With Global Average Pooling and 2-D Feature Image

et al. 2020

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A novel convolutional neural network namely the modified CNN-GAP model is proposed for fast fault diagnosis of the DC-DC inverter. This method improves the model structure of the traditional CNN by using a global average pooling layer to replace the fully connected layer of 2∼3 layers. The improved CNN-GAP method mainly contains an input layer, a feature extraction layer, a global average pooling (GAP) layer, and a Softmax output layer. Firstly, the raw 1-D time-series data directly input into the input layer of the established CNN-GAP diagnosis model. The 2-D feature maps are reconstructed in the input layer. Secondly, the representative features are automatically extracted from the 2-D feature maps by using multiple convolutional layers and pooling layers. Thirdly, the dimension transformation and size compression of the output image of the feature extraction layer is completed by the GAP layer. Finally, the fault diagnosis result of the DC-DC inverter is automatically output in the Softmax output layer. The proposed method is used for diagnosing the open-circuit fault of the IGBT in the isolated DC-DC inverter. The proposed method is more accurate and effective than other mainstream intelligent diagnosis methods including the SVM, KNN, DNN, and traditional CNN. The experiment results show that the diagnostic accuracy is up to 99.95%, and the testing time can reduce by more than 15%. The improved CNN-GAP method could greatly reduce the model parameter quantity of the traditional CNN more than 80%, which is more suitable for rapid fault diagnosis in electronic devices. INDEX TERMS Intelligent fault diagnosis, data-driven, convolutional neural network, global average pooling, 2-D feature image, deep learning, DC-DC inverter, IGBT open-circuit fault.

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Section: ) Data Normalizationmentioning

confidence: 99%

A Data-Driven-Based Fault Diagnosis Approach for Electrical Power DC-DC Inverter by Using Modified Convolutional Neural Network With Global Average Pooling and 2-D Feature Image

et al. 2020

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“…As for the learning-based strategy, the SVM is widely used in the multiclassification problem thanks to the small structural risk. More importantly, the SVM method with kernel function can learn the non-learning and non-linear relationship between inputs [22]. Consequently, the SVM fusion method in ensemble learning is applied in our model to fuse three results in three ACSE branch.…”

Section: B the Fusion Strategymentioning

confidence: 99%

Automatic Modulation Recognition for Radar Signals via Multi-Branch ACSE Networks

Wei

Wang

et al. 2020

IEEE Access

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Automatic modulation recognition (AMR) for radar signals plays a significant role in electronic warfare. Conventional recognition methods may suffer from the recognition accuracy and the computation complexity under low signal-to-noise ratio (SNR) conditions. In this paper, a novel multi-branch Asymmetric Convolution Squeeze-and-Excitation (ACSE) networks using multi-domain features and fusion strategy based on a support vector machine is proposed to recognize eight kinds of radar signals. First, features of radar signals in the frequency domain, the autocorrelation domain, and the time-frequency domain are extracted. Then the obtained multi-domain features are converted as the input of the proposed networks which owns the representational power and learning ability. Finally, the outputs of multi-branch ACSE networks are fused via the fusion strategy to obtain the final results. Via simulations, the robustness and effectiveness of the fusion strategy are verified. The results on the simulation dataset prove that the proposed method can achieve more than 93% accuracy at-10dB for all modulations. Compared with four newly proposed networks, the multi-branch ACSE networks achieves better performance under low SNR conditions. And the results on measured signals show that the proposed method outperforms other comparison methods, especially for binary frequency-shift keying (BFSK) signals. INDEX TERMS Automatic modulation recognition, convolutional neural networks, radar signal, neural network application.

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“…Min Xia et al used a CNN-based approach for fault diagnosis of rotating machinery [32]. An ensemble CNN model is proposed for bearing fault diagnosis by Yang Liu et al [33]. Also, a novel sensor data-driven fault diagnosis method is proposed based on CNN [34].…”

Section: Introductionmentioning

confidence: 99%

A New Convolutional Neural Network With Random Forest Method for Hydrogen Sensor Fault Diagnosis

et al. 2020

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Hydrogen is considered to be a hazardous substance. Hydrogen sensors can be used to detect the concentration of hydrogen and provide an ideal monitoring means for the safe use of hydrogen energy. Hydrogen sensors need to be highly reliable, so fault identification and diagnosis for gas sensors are of vital practical significance. However, traditional machine learning methods for fault diagnosis are based on features extracted by experts, prior knowledge requirements and the sensitivity of system changes. In this study, a new convolutional neural network (CNN) using the random forest (RF) classifier is proposed for hydrogen sensor fault diagnosis. First, the 1-D time-domain data of fault signals are converted into 2-D gray matrix images; this process does not require noise suppression and no signal information is lost. Secondly, the features of the gray matrix images are automatically extracted by using a CNN, which does not rely on expert experience. Dropout and zero-padding are used to optimize the structure of the CNN and reduce overfitting. Random forest, which is robust and has strong generalization ability, is introduced for the classification of gas sensor signal modes, in order to obtain the final diagnostic results. Finally, we design and implement a prototype hydrogen sensor array for experimental verification. The accuracy of fault diagnosis in hydrogen sensors is 100% under noisy environment with the proposed method, which is superior of CNN without RF and other methods. The results show that the proposed CNN with RF method provides a good solution for hydrogen sensor fault diagnosis. INDEX TERMS Fault diagnosis, hydrogen sensor, convolutional neural network, random forest, feature extraction.

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An Ensemble Convolutional Neural Networks for Bearing Fault Diagnosis Using Multi-Sensor Data

Cited by 70 publications

References 51 publications

A Data-Driven-Based Fault Diagnosis Approach for Electrical Power DC-DC Inverter by Using Modified Convolutional Neural Network With Global Average Pooling and 2-D Feature Image

A Data-Driven-Based Fault Diagnosis Approach for Electrical Power DC-DC Inverter by Using Modified Convolutional Neural Network With Global Average Pooling and 2-D Feature Image

Automatic Modulation Recognition for Radar Signals via Multi-Branch ACSE Networks

A New Convolutional Neural Network With Random Forest Method for Hydrogen Sensor Fault Diagnosis

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