计算机科学
对抗制
人工智能
分类器(UML)
机器学习
人工神经网络
卷积神经网络
断层(地质)
试验数据
数据挖掘
模式识别(心理学)
地震学
程序设计语言
地质学
作者
Jinyang Jiao,Ming Zhao,Jing Lin
出处
期刊:IEEE Transactions on Industrial Electronics
[Institute of Electrical and Electronics Engineers]
日期:2020-11-01
卷期号:67 (11): 9904-9913
被引量:98
标识
DOI:10.1109/tie.2019.2956366
摘要
The data distribution shift is inevitable in practical fault diagnosis due to internal and outside changes of equipment. These obstacles will lead to performance degrade or even failure of diagnostic models. In light of these problems, a novel unsupervised intelligent diagnostic framework named Adversarial Adaptation network based on Classifier Discrepancy (AACD) is introduced for mechanical fault diagnosis where the training and test domains have different data distribution. Specifically, AACD mainly contains two parts: one is the shared feature generator built by the 1-D convolutional neural network and the other is double task-specific classifiers. They play an adversarial training game to learn class-separable and domain-invariant features for fault diagnosis. The proposed AACD is evaluated by 15 transfer diagnosis tasks constructed on the planetary gearbox test-bed and the rolling bearing test-bed. Moreover, six popular algorithms are selected for comparison. The comprehensive results validate the effectiveness and superiority of the proposed approach.
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