A Semi-supervised Gaussian Mixture Variational Autoencoder method for few-shot fine-grained fault diagnosis

In practical engineering, obtaining labeled high-quality fault samples poses challenges. Conventional fault diagnosis methods based on deep learning struggle to discern the underlying causes of mechanical faults from a fine-grained perspective, due to the scarcity of annotated data. To tackle those...

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Bibliographic Details
Published in:Neural networks Vol. 178; p. 106482
Main Authors: Zhao, Zhiqian, Xu, Yeyin, Zhang, Jiabin, Zhao, Runchao, Chen, Zhaobo, Jiao, Yinghou
Format: Journal Article
Language:English
Published: United States Elsevier Ltd 01.10.2024
Subjects:
Algorithms
Deep Learning
Fault diagnosis
Few-shot
Fine-grained
Gaussian Mixture
Neural Networks, Computer
Normal Distribution
Semi-supervised
Supervised Machine Learning
Variational Autoencoder
Fault diagnosis
Gaussian Mixture
Fine-grained
Variational Autoencoder
Semi-supervised
Few-shot
ISSN:0893-6080, 1879-2782, 1879-2782
Online Access:Get full text
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Summary:In practical engineering, obtaining labeled high-quality fault samples poses challenges. Conventional fault diagnosis methods based on deep learning struggle to discern the underlying causes of mechanical faults from a fine-grained perspective, due to the scarcity of annotated data. To tackle those issue, we propose a novel semi-supervised Gaussian Mixed Variational Autoencoder method, SeGMVAE, aimed at acquiring unsupervised representations that can be transferred across fine-grained fault diagnostic tasks, enabling the identification of previously unseen faults using only the small number of labeled samples. Initially, Gaussian mixtures are introduced as a multimodal prior distribution for the Variational Autoencoder. This distribution is dynamically optimized for each task through an expectation–maximization (EM) algorithm, constructing a latent representation of the bridging task and unlabeled samples. Subsequently, a set variational posterior approach is presented to encode each task sample into the latent space, facilitating meta-learning. Finally, semi-supervised EM integrates the posterior of labeled data by acquiring task-specific parameters for diagnosing unseen faults. Results from two experiments demonstrate that SeGMVAE excels in identifying new fine-grained faults and exhibits outstanding performance in cross-domain fault diagnosis across different machines. Our code is available at https://github.com/zhiqan/SeGMVAE.
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ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2024.106482