Fault Diagnosis of Rolling Bearing Using Convolutional Denoising Autoencoder and Siamese Neural Network With Small Sample

Bearing fault diagnosis is critical for ensuring mechanical reliability and operational safety. Industrial Internet of Things (IIoT) sensors provide real-time monitoring data, advancing research in data-driven approaches to bearing fault diagnosis. However, current studies overlook two key challenge...

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Vydáno v:IEEE internet of things journal Ročník 12; číslo 5; s. 5233 - 5244
Hlavní autoři: Zhao, Xufeng, Chen, Ying, Yang, Mengshu, Xiang, Jiawei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.03.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Artificial neural networks
Automation
Classification
Convolution
Convolutional codes
Convolutional denoising autoencoder (DAE)
Data models
Decoding
Fault diagnosis
Feature extraction
Industrial applications
Industrial Internet of Things
Internet of Things
Neural networks
Noise
Noise reduction
Real time
Roller bearings
rolling bearing
Rolling bearings
siamese neural network
signal processing
small sample
Training
White noise
ISSN:2327-4662, 2327-4662
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Shrnutí:Bearing fault diagnosis is critical for ensuring mechanical reliability and operational safety. Industrial Internet of Things (IIoT) sensors provide real-time monitoring data, advancing research in data-driven approaches to bearing fault diagnosis. However, current studies overlook two key challenges: 1) susceptibility to noise interference during fault signal acquisition and 2) the scarcity of fault data for effective diagnostic tasks in practical scenarios. To address these issues, this article proposes a novel method termed convolutional denoising autoencoder and siamese neural network (CDAE-SNN) for fault diagnosis in rolling bearings. This method is designed to be robust against noise and applicable in scenarios with limited data. Initially, Gaussian white noise is added to raw signals to simulate noisy signals encountered in real operating conditions. Subsequently, a convolutional denoising autoencoder (DAE) is constructed and optimized. The encoder in CDAE compresses feature information from samples into a lower dimensional space, while the decoder reconstructs signals to mitigate noise effects. Denoised signal sample pairs are then fed into a 2-D convolutional neural network-based siamese network to generate embedding vectors. Fault classification of rolling bearings is performed based on similarity metrics between sample pairs. Experimental results confirm the enhanced diagnostic accuracy of our proposed model across various signal-to-noise ratios and sample sizes. Furthermore, the model exhibits superior performance in classifying faults across diverse proportion of new categories.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2024.3487989