Weighted quantile discrepancy-based deep domain adaptation network for intelligent fault diagnosis

2022 ◽  
pp. 108149
Author(s):  
Zhenhua Fan ◽  
Qifa Xu ◽  
Cuixia Jiang ◽  
Steven X. Ding
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Intelligent Fault Diagnosis

Intelligent Fault Diagnosis With Deep Adversarial Domain Adaptation

2021 ◽  
Vol 70 ◽  
pp. 1-9
Author(s):  
Yu Wang ◽  
Xiaojie Sun ◽  
Jie Li ◽  
Ying Yang
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Intelligent Fault Diagnosis

Double-level adversarial domain adaptation network for intelligent fault diagnosis

2020 ◽  
Vol 205 ◽  
pp. 106236 ◽  
Author(s):  
Jinyang Jiao ◽  
Jing Lin ◽  
Ming Zhao ◽  
Kaixuan Liang
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Intelligent Fault Diagnosis

scholarly journals A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signal

2017 ◽  
Author(s):  
Wei Zhang ◽  
Gaoliang Peng ◽  
Chuanhao Li ◽  
Yuanhang Chen ◽  
Zhujun Zhang
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Data Augmentation ◽  
Domain Adaptation ◽  
Learning Model ◽  
Frequency Noise ◽  
Nonlinear Mapping ◽  
Intelligent Fault Diagnosis ◽  
Deep Learning Model ◽  
Adaptation Ability

Intelligent fault diagnosis techniques have replaced the time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning model can improve the accuracy of intelligent fault diagnosis with the help of its multilayer nonlinear mapping ability. This paper has proposed a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in first convolutional layer for extracting feature and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform state of the art DNN model which is based on frequency features under different working load and noisy environment.

A new method for intelligent fault diagnosis of machines based on unsupervised domain adaptation

2021 ◽  
Vol 427 ◽  
pp. 96-109
Author(s):  
Nannan Lu ◽  
Hanhan Xiao ◽  
Yanjing Sun ◽  
Min Han ◽  
Yanfen Wang
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
New Method ◽  
Intelligent Fault Diagnosis ◽  
Unsupervised Domain Adaptation

scholarly journals Domain Adaptation Network with Double Adversarial Mechanism for Intelligent Fault Diagnosis

2021 ◽  
Vol 11 (17) ◽  
pp. 7983
Author(s):  
Kun Xu ◽  
Shunming Li ◽  
Ranran Li ◽  
Jiantao Lu ◽  
Xianglian Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Wasserstein Distance ◽  
Diagnostic Methods ◽  
Mechanical Equipment ◽  
Intelligent Fault Diagnosis ◽  
Sample Distribution ◽  
Feature Extractor ◽  
Long Time ◽  
Diagnosis Method

Due to the mechanical equipment working under variable speed and load for a long time, the distribution of samples is different (domain shift). The general intelligent fault diagnosis method has a good diagnostic effect only on samples with the same sample distribution, but cannot correctly predict the faults of samples with domain shift in a real situation. To settle this problem, a new intelligent fault diagnosis method, domain adaptation network with double adversarial mechanism (DAN-DAM), is proposed. The DAN-DAM model is mainly composed of a feature extractor, two label classifiers and a domain discriminator. The feature extractor and the two label classifiers form the first adversarial mechanism to achieve class-level alignment. Moreover, the discrepancy between the two classifiers is measured by Wasserstein distance. Meanwhile, the feature extractor and the domain discriminator form the second adversarial mechanism to realize domain-level alignment. In addition, maximum mean discrepancy (MMD) is used to reduce the distance between the extracted features of two domains. The DAN-DAM model is verified by multiple transfer experiments on some datasets. According to the transfer experiment results, the DAN-DAM model has a good diagnosis effect for the domain shift samples. Moreover, the diagnostic accuracy is generally higher than other mainstream diagnostic methods.

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