Support tensor machine with dynamic penalty factors and its application to the fault diagnosis of rotating machinery with unbalanced data

He, Ziming; Cheng, Junsheng; Zhao, Xianzhu; Yu, Yang

doi:10.1016/j.ymssp.2019.106441

Cited by 57 publications

(31 citation statements)

References 38 publications

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“…Frequency-domain features can describe the variations in the frequency band from the view of the signal spectrum and spectral energy distribution. In total, 29 time and frequency features (P1, P2,…, P29) are calculated in this paper according to reference [ 8 ] and reference [ 20 ], as illustrated in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

“…Batista et al [ 16 ] combined different SVMs to detect the bearing failures using 13 statistical parameters in the time domain and frequency domain. In addition, some improved methods, such as hidden markov, adaptive neuro-fuzzy inference system (ANFIS), extreme learning machine (ELM) and support tensor machine (STM), have been proposed to implement the bearing fault diagnosis and classification [ 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

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Fault Diagnosis for High-Speed Train Axle-Box Bearing Using Simplified Shallow Information Fusion Convolutional Neural Network

Luo

Lin

Peng³

et al. 2020

Sensors

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Axle-box bearings are one of the most critical mechanical components of the high-speed train. Vibration signals collected from axle-box bearings are usually nonlinear and nonstationary, caused by the complicated operating conditions. Due to the high reliability and real-time requirement of axle-box bearing fault diagnosis for high-speed trains, the accuracy and efficiency of the bearing fault diagnosis method based on deep learning needs to be enhanced. To identify the axle-box bearing fault accurately and quickly, a novel approach is proposed in this paper using a simplified shallow information fusion-convolutional neural network (SSIF-CNN). Firstly, the time domain and frequency domain features were extracted from the training samples and testing samples before been inputted into the SSIF-CNN model. Secondly, the feature maps obtained from each hidden layer were transformed into a corresponding feature sequence by the global convolution operation. Finally, those feature sequences obtained from different layers were concatenated into one-dimensional as the fully connected layer to achieve the fault identification task. The experimental results showed that the SSIF-CNN effectively compressed the training time and improved the fault diagnosis accuracy compared with a general CNN.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for High-Speed Train Axle-Box Bearing Using Simplified Shallow Information Fusion Convolutional Neural Network

Luo

Lin

Peng³

et al. 2020

Sensors

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“…In [ 5 , 6 ], a novel detection method based on a deep autoencoder was proposed, which performed the unsupervised diagnosis of motor faults and evaluated three different autoencoder architectures: the multilayer perceptron (MLP) auto-encoder, CNN auto-encoder, and cyclic auto-encoder composed of an LSTM unit. In view of the above considerations, both CNN and LSTM provide satisfactory fault diagnosis pattern recognition results in a short period of time [ 6 ]. Therefore, it is foreseeable that a reasonable integration of CNN and LSTM will further reduce the classification errors.…”

Section: Introductionmentioning

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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“…16,[23][24][25][26][27] For example, Li et al 23 chose the wavelet time-frequency diagram as tensorial features and then designed non-parallel least squares support matrix machine to identify the fault types of rolling bearing. He et al 24 presented a new tensor classifier called support tensor machine with dynamic penalty factors to solve the classification problem with unbalanced data, and also applied it in fault diagnosis of rolling bearing successfully. Bo et al 25 first selected the sub-band timefrequency image as tensorial features and then introduced linear support higher-order tensor machines to achieve the fault identification.…”

Section: Introductionmentioning

confidence: 99%

“…17–22 Similarly, those tensor classifiers have been introduced in fault diagnosis of rolling bearing. 16,23–27 For example, Li et al 23 chose the wavelet time-frequency diagram as tensorial features and then designed non-parallel least squares support matrix machine to identify the fault types of rolling bearing. He et al 24 presented a new tensor classifier called support tensor machine with dynamic penalty factors to solve the classification problem with unbalanced data, and also applied it in fault diagnosis of rolling bearing successfully.…”

Section: Introductionmentioning

confidence: 99%

Health condition identification for rolling bearing based on hierarchical multiscale symbolic dynamic entropy and least squares support tensor machine–based binary tree

Yang

Jia

2020

Structural Health Monitoring

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Bearing health condition identification plays a crucial role in guaranteeing maximum productivity and reducing maintenance costs. In this article, a novel tensorial feature extraction approach called hierarchical multiscale symbolic dynamic entropy is developed, which can be used to assess the dynamic characteristic of the measured vibration data at different hierarchical layers and different scales. Besides, the influence of parameters in hierarchical multiscale symbolic dynamic entropy is investigated so as to select the optimal parameters. Then, a new multi-fault classifier called least squares support tensor machine–based binary tree is presented to achieve the fault identification automatically. In the least squares support tensor machine–based binary tree method, the divisibility measure strategy is constructed by two new separability measures (i.e. the average center distance of samples in one class, the center distance of samples between sub-class and global class). Finally, a novel intelligent fault diagnosis scheme based on hierarchical multiscale symbolic dynamic entropy and least squares support tensor machine–based binary tree is developed, which is applied to analyze the experimental data of rolling bearing. The results indicate that the proposed scheme has a superior performance in health condition identification. Compared with the existing symbolic dynamic entropy–based fault diagnosis methods, the proposed method has higher diagnostic accuracy and better stability.

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Support tensor machine with dynamic penalty factors and its application to the fault diagnosis of rotating machinery with unbalanced data

Cited by 57 publications

References 38 publications

Fault Diagnosis for High-Speed Train Axle-Box Bearing Using Simplified Shallow Information Fusion Convolutional Neural Network

Fault Diagnosis for High-Speed Train Axle-Box Bearing Using Simplified Shallow Information Fusion Convolutional Neural Network

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

Health condition identification for rolling bearing based on hierarchical multiscale symbolic dynamic entropy and least squares support tensor machine–based binary tree

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