An Unsupervised Deep Feature Learning Model Based on Parallel Convolutional Autoencoder for Intelligent Fault Diagnosis of Main Reducer

Traditional diagnostic framework consists of three parts: data acquisition, feature generation, and fault classification. However, manual feature extraction utilized signal processing technologies heavily depending on subjectivity and prior knowledge which affect the effectiveness and efficiency. To...

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Vydáno v:Computational intelligence and neuroscience Ročník 2021; číslo 1; s. 8922656
Hlavní autoři: Ye, Qing, Liu, Changhua
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Hindawi 2021
John Wiley & Sons, Inc
Témata:
Automobiles
Bias
Cable television broadcasting industry
Classification
Data acquisition
Deep learning
Diagnostic systems
Fault diagnosis
Feature extraction
Identification
Learning
Machine learning
Machinery
Neural networks
Regularization
Signal processing
Wavelet transforms
United States
ISSN:1687-5265, 1687-5273, 1687-5273
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Shrnutí:Traditional diagnostic framework consists of three parts: data acquisition, feature generation, and fault classification. However, manual feature extraction utilized signal processing technologies heavily depending on subjectivity and prior knowledge which affect the effectiveness and efficiency. To tackle these problems, an unsupervised deep feature learning model based on parallel convolutional autoencoder (PCAE) is proposed and applied in the stage of feature generation of diagnostic framework. Firstly, raw vibration signals are normalized and segmented into sample set by sliding window. Secondly, deep features are, respectively, extracted from reshaped form of raw sample set and spectrogram in time-frequency domain by two parallel unsupervised feature learning branches based on convolutional autoencoder (CAE). During the training process, dropout regularization and batch normalization are utilized to prevent over fitting. Finally, extracted representative features are feed into the classification model based on deep structure of neural network (DNN) with softmax. The effectiveness of the proposed approach is evaluated in fault diagnosis of automobile main reducer. The results produced in contrastive analysis demonstrate that the diagnostic framework based on parallel unsupervised feature learning and deep structure of classification can effectively enhance the robustness and enhance the identification accuracy of operation conditions by nearly 8%.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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Academic Editor: Amparo Alonso-Betanzos
ISSN:1687-5265
1687-5273
1687-5273
DOI:10.1155/2021/8922656