MCVAE-GAN: A Novel Diagnosis Method for Solving Harmonic Reducer Fault Signal Scarcity and Sample Diversity Problem

Harmonic reducers are vital for robotics and aerospace due to their ability to deliver high torque and reduction, but they face challenges in fault diagnosis because of their complex design and specific operating conditions. The scarcity of real-world fault data and signal sample diversity for these...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement Vol. 73; pp. 1 - 10
Main Authors: Zhao, Zida, Sun, Shilong, Wang, Dong, Xu, Wenfu
Format: Journal Article
Language:English
Published: New York IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Coders
Data models
Data processing
Discriminators
Diversity reception
Fault diagnosis
Feature extraction
Generative adversarial networks
Generators
Harmonic analysis
harmonic reducer
Machine learning
multiconditional variational autoencoder generative adversarial network (MCVAE-GAN)
Real variables
Robotics
sample diversity
signal scarcity
Vibration measurement
ISSN:0018-9456, 1557-9662
Online Access:Get full text
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Summary:Harmonic reducers are vital for robotics and aerospace due to their ability to deliver high torque and reduction, but they face challenges in fault diagnosis because of their complex design and specific operating conditions. The scarcity of real-world fault data and signal sample diversity for these reducers exacerbates the problem, leading to a significant data imbalance that hinders the effectiveness of deep learning-based fault diagnosis methods. To address this, we propose a novel approach, the multiconditional variational autoencoder generative adversarial network (MCVAE-GAN), featuring a multihead attention mechanism (MHAM) fusion to enhance sample diversity and tackle fault data scarcity. The MCVAE-GAN employs MHAM in its encoder, decoder, and discriminator to improve data processing and analysis. The encoder uses MHAM to focus on crucial data attributes, the decoder enhances sample characteristics by managing conditional and latent variables interaction, and the discriminator distinguishes between real and generated samples by concentrating on key features. The expanded dataset is used to train a classification network to complete fault diagnosis. Our method significantly outperforms contrasting data generation techniques in fault diagnosis, demonstrating superior sample imbalance management and showcasing its applicability in real-world engineering scenarios.
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2024.3472781