A multivariate encoder information based convolutional neural network for intelligent fault diagnosis of planetary gearboxes

Jiao, Jinyang; Zhao, Ming; Lin, Jing; Zhao, Jian

doi:10.1016/j.knosys.2018.07.017

Cited by 107 publications

(33 citation statements)

References 24 publications

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“…Inspired by the Second Generation Wavelet Transform (SGWT), Pan et al [29] presented the Lifting-Net where the structure is constructed alternately by split layers, predict layers and update layers with different convolutional kernel size. The effectiveness of some other novel CNN-based structures have also been verified [30]- [31].…”

Section: Related Work a Fault Diagnosismentioning

confidence: 89%

Data Simulation by Resampling—A Practical Data Augmentation Algorithm for Periodical Signal Analysis-Based Fault Diagnosis

Tang

Chen

2019

IEEE Access

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In recent years, machine learning and deep learning based fault diagnosis methods have been studied, however, most of them remain at the experimental stage mainly because of two obstacles, briefly, a) inadequate faulty examples and b) various working conditions of industrial data. In this literature, a practical algorithm named Data Simulation by Resampling (DSR) is proposed for data augmentation to alleviate the two problems in fault diagnosis. In essence, as a form of Vicinal Risk Minimization (VRM), DSR utilizes a two-stage resampling operation to simulate vicinal examples in both time domain and frequency domain. By doing so, DSR can both increase the sample diversity and the quantity of training set, which regularizes machine learning and deep learning based methods to achieve a higher generalization performance. Our experiments verify the effectiveness of DSR and show the possibility of combining it with other augmentation algorithms.

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Section: Related Work a Fault Diagnosismentioning

confidence: 89%

Data Simulation by Resampling—A Practical Data Augmentation Algorithm for Periodical Signal Analysis-Based Fault Diagnosis

Tang

Chen

2019

IEEE Access

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“…With an integration of de-trend signal, instantaneous angular acceleration and instantaneous angular speed, Jiao et al proposed a multi-information fusion method to preprocess data. The constructed CNN was employed for fault diagnosis of planetary gearbox, data segmentation as a way of data augmentation [106].…”

Section: Data Augmentation Techniquementioning

confidence: 99%

Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery

2020

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Rotating machinery plays a critical role in many significant fields. However, the unpredictable machinery faults may lead to the severe damage and losses. Hence, it is of great value to explore the precise approaches for fault diagnosis. With the development of the intelligent fault diagnosis methods based on deep learning, convolutional neural network (CNN) has aroused the attention of researchers in machinery fault diagnosis. In the light of the reduction of difficulty in feature learning and the improvement of final diagnosis accuracy, data preprocessing is necessary and crucial in CNN-based fault diagnosis methods. This review focuses on CNN-based fault diagnosis approaches in rotating machinery. Firstly, data preprocessing methods are overviewed. Then, we emphatically analyze and discuss several main techniques applied in CNN-based intelligent diagnosis, principally including the fast Fourier transform, wavelet transform, data augmentation, S-transform, and cyclic spectral analysis. Finally, the potential challenges and research objects are prospected on data preprocessing in intelligent fault diagnosis of rotary machinery. INDEX TERMS Data preprocessing, convolutional neural network, intelligent fault diagnosis, rotating machinery.

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“…The mean sampling method is to calculate the average value of each data block as the output value. 27 The calculation complexity of the network can be effectively reduced by the pooled sampling. The pooling function with no parameters is used to slide from the starting point to the end in the input feature, and the feature elements in the pooling window are sampled and then output to the next layer.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

A novel fusion approach of deep convolution neural network with auto-encoder and its application in planetary gearbox fault diagnosis

Chen

Liu

Tang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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The fault features of gearbox are often influenced and interwoven with each other under the non-stationary condition. The traditional shallow intelligent diagnosis models are difficult to detect and identify gearbox faults with selected features according to prior knowledge. To solve this problem, a novel deep convolutional auto-encoding neural network is designed based on the fusion of the convolutional neural network with the automatic encoder in this research. The vibration signals of gearbox are transformed into Hilbert envelope spectrum by using Hilbert transform and Fourier transform, and the different characteristics of spectral spatial data are automatically learned by convolutional auto-encoding neural network with multiple convolution kernels. The parameters of the convolutional neural network are fine-tuned through a fully connected neural network with a small number of labeled samples. Through the analysis for gearbox fault experiments, the effectiveness and practicability of the proposed method in equipment fault diagnosis are verified. The deep convolutional neural network embedded in the auto-encoder has stronger learning ability, and the diagnosis performance is more stable and reliable in practical engineering application.

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A multivariate encoder information based convolutional neural network for intelligent fault diagnosis of planetary gearboxes

Cited by 107 publications

References 24 publications

Data Simulation by Resampling—A Practical Data Augmentation Algorithm for Periodical Signal Analysis-Based Fault Diagnosis

Data Simulation by Resampling—A Practical Data Augmentation Algorithm for Periodical Signal Analysis-Based Fault Diagnosis

Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery

A novel fusion approach of deep convolution neural network with auto-encoder and its application in planetary gearbox fault diagnosis

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