Fault Detection in Gears Using Fault Samples Enlarged by a Combination of Numerical Simulation and a Generative Adversarial Network

断层(地质) 自编码 人工神经网络 生成对抗网络 卷积神经网络 计算机科学 计算机模拟 故障检测与隔离 算法 人工智能 深度学习 模式识别(心理学) 模拟 执行机构 地质学 地震学
作者
Yun Gao,Xiaoyang Liu,Jiawei Xiang
出处
期刊:IEEE-ASME Transactions on Mechatronics [Institute of Electrical and Electronics Engineers]
卷期号:27 (5): 3798-3805 被引量:37
标识
DOI:10.1109/tmech.2021.3132459
摘要

It is inevitable for gear to become damaged, which has a profound effect on the performance of gear transmission systems. Solving the problem of gear fault detection using artificial intelligence models depends on sufficient fault samples, though they might not always exist. A new method using numerical simulation and a generative adversarial network (GAN) is proposed to enlarge fault samples for detecting faults in gears. First, to supplement the missing fault samples, numerical simulation is employed to obtain simulation fault samples. Then, simulation and measurement fault samples are input into the GAN to generate synthetic fault samples to enlarge the training samples. Finally, the simulation, measurement and related synthetic fault samples serve as typical classifiers, including convolutional neural network, recurrent neural network, and stacked autoencoder, while the test samples of unknown faults are finally detected. Three experimental groups are designed to classify gear faults. The average classification accuracy is 100, 98.83, and 97.64%, which confirms the feasibility and effectiveness of the method for detecting gear faults using incomplete fault samples. The idea presented herein is expected to apply in any type of mechanical system that has the corresponding well-constructed numerical simulation model.
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