Intelligent Fault Diagnosis for Rotary Machinery Using Transferable Convolutional Neural Network

Chen, Zhuyun; Gryllias, Konstantinos; Li, Weihua

doi:10.1109/tii.2019.2917233

Cited by 250 publications

(69 citation statements)

References 34 publications

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“…The local connection and parameter sharing in the CNN reduce the number of parameters, greatly reduce the training complexity, and reduce overfitting. At the same time, its weight-sharing also makes the CNN tolerant of translations, while the down-sampling in the pooling layer further reduces the output parameters and makes the model tolerant to mild deformations, which improves the generalization ability of the model [ 10 , 11 , 12 ]. As shown in Figure 1 , in each feature extraction layer, the feature map performs convolution calculation on multiple convolution kernels, and the feature extraction layers are connected by deviation calculation, activation function, and pooling operation.…”

Section: Basic Theorymentioning

confidence: 99%

GIS Partial Discharge Pattern Recognition Based on a Novel Convolutional Neural Networks and Long Short-Term Memory

Liu

Yan

Wang

et al. 2021

Entropy

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Distinguishing the types of partial discharge (PD) caused by different insulation defects in gas-insulated switchgear (GIS) is a great challenge in the power industry, and improving the recognition accuracy of the relevant models is one of the key problems. In this paper, a convolutional neural network and long short-term memory (CNN-LSTM) model is proposed, which can effectively extract and utilize the spatiotemporal characteristics of PD input signals. First, the spatial characteristics of higher-level PD signals can be obtained through the CNN network, but because CNN is a deep feedforward neural network, it does not have the ability to process time-series data. The PD voltage signal is related to the time dimension, so LSTM saves and analyzes the previous voltage signal information, realizes the modeling of the time dependence of the data, and improves the accuracy of the PD signal pattern recognition. Finally, the pattern recognition results based on CNN-LSTM are given and compared with those based on other traditional analysis methods. The results show that the pattern recognition rate of this method is the highest, with an average of 97.9%, and its overall accuracy is better than that of other traditional analysis methods. The CNN-LSTM model provides a reliable reference for GIS PD diagnosis.

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Section: Basic Theorymentioning

confidence: 99%

GIS Partial Discharge Pattern Recognition Based on a Novel Convolutional Neural Networks and Long Short-Term Memory

Liu

Yan

Wang

et al. 2021

Entropy

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show abstract

“…Transfer learning is considered to have great potential to complete different but similar tasks from the source domain to the target domain [25,26]. Parameter transfer, the most widely applied transfer learning technique, aims to provide valuable parameter knowledge for the target model from a well pre-trained model (source model) [24,27].…”

Section: Introductionmentioning

confidence: 99%

“…With well-located Intelligent fault diagnosis of rotor-bearing system under varying working conditions with modified transfer CNN and thermal images R 2 initial parameters and a small number of target samples, the target model can be quickly fine-tuned to solve the target task. Since 2017, the transfer diagnosis performance of CNNs integrated with parameter transfer has been demonstrated by a few case studies [24,26]. Thus, CNN and parameter transfer can be investigated for fault diagnosis of rotor-bearing system under different working conditions with limited samples.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images

Shao

Xia

Han

et al. 2021

IEEE Trans. Ind. Inf.

339

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The existing intelligent fault diagnosis methods of rotor-bearing system mainly focus on vibration analysis under steady operation, which has low adaptability to new scenes. In this paper, a new framework for rotor-bearing system fault diagnosis under varying working conditions is proposed by using a modified convolutional neural network (CNN) with transfer learning. First, infrared thermal images are collected and used to characterize the health condition of rotor-bearing system. Second, modified CNN is developed by introducing stochastic pooling and Leaky rectified linear unit to overcome the training problems in classical CNN. Finally, parameter transfer is used to enable the source modified CNN to adapt to the target domain, which solves the problem of limited available training data in the target domain. The proposed method is applied to analyze thermal images of rotor-bearing system collected under different working conditions. The results show that the proposed method outperforms other cutting edge methods in fault diagnosis of rotor-bearing system.

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“…Deep transfer learning has become a new research direction. According to its basic concept, an adaptive layer is added between the feature extraction and classification layers so that the data on the source and target domains can be distributed more similarly [6][7][8][9][10]. Besides, more expressive features can be extracted automatically by deep transfer learning and it utilizes the self-extraction of deep learning together with transfer learning to acquire "new knowledge", which assist to solve small sample data problem well in few-shot learning [11].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearings of Different Working Conditions Based on Multi-Feature Spatial Domain Adaptation

2021

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The running state of rolling bearings is complex in operation, and the data are generally collected under different working conditions. However, when single-source domain adaptation is used to model the heterogeneously distributed data obtained under different working conditions, the domaininvariant representations can hardly be used for representation, which directly affects the fault diagnosis rate. To this end, a method for the fault diagnosis of rolling bearings under different working conditions based on multi-feature spatial domain adaptation is proposed. Firstly, all the data from source and target domains are mapped into a feature space to learn the common representations of all domains. Secondly, the data for each pair of source and target domains are mapped into different feature spaces to get the fault feature representations under various working conditions. And the multi-domain adaptation network is used for the domain-specific distribution alignment to learn multiple domain-invariant representations. Thirdly, these representations are used to train multiple domain-specific classifiers, thus obtaining the recognition result for each domain-invariant representation. Finally, the domain-specific decision boundaries predicted by multiple classifiers are employed to align the classifiers' output of target samples and thus to reduce the influence from different classifiers. The effectiveness and feasibility of this proposed method have been verified by diagnostic experiments conducted according to the rolling bearing data from Case Western Reserve University and Laboratory, respectively.

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Intelligent Fault Diagnosis for Rotary Machinery Using Transferable Convolutional Neural Network

Cited by 250 publications

References 34 publications

GIS Partial Discharge Pattern Recognition Based on a Novel Convolutional Neural Networks and Long Short-Term Memory

GIS Partial Discharge Pattern Recognition Based on a Novel Convolutional Neural Networks and Long Short-Term Memory

Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images

Fault Diagnosis of Rolling Bearings of Different Working Conditions Based on Multi-Feature Spatial Domain Adaptation

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