Deep Learning-Based Domain Adaptation Method for Fault Diagnosis in Semiconductor Manufacturing

2020 ◽  
Vol 33 (3) ◽  
pp. 445-453 ◽  
Author(s):  
Moslem Azamfar ◽  
Xiang Li ◽  
Jay Lee
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Semiconductor Manufacturing ◽  
Domain Adaptation ◽  
Adaptation Method

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.

scholarly journals Domain Adaptation for Intelligent Fault Diagnosis under Different Working Conditions

2020 ◽  
Vol 319 ◽  
pp. 03001
Author(s):  
Weigui Li ◽  
Zhuqing Yuan ◽  
Wenyu Sun ◽  
Yongpan Liu
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Domain Adaptation ◽  
Transfer Problem ◽  
Intelligent Manufacturing ◽  
Maximum Mean Discrepancy ◽  
Bearing Fault Diagnosis ◽  
Cross Domain ◽  
Conditional Maximum ◽  
Adaptation Method

Recently, deep learning algorithms have been widely into fault diagnosis in the intelligent manufacturing field. To tackle the transfer problem due to various working conditions and insufficient labeled samples, a conditional maximum mean discrepancy (CMMD) based domain adaptation method is proposed. Existing transfer approaches mainly focus on aligning the single representation distributions, which only contains partial feature information. Inspired by the Inception module, multi-representation domain adaptation is introduced to improve classification accuracy and generalization ability for cross-domain bearing fault diagnosis. And CMMD-based method is adopted to minimize the discrepancy between the source and the target. Finally, the unsupervised learning method with unlabeled target data can promote the practical application of the proposed algorithm. According to the experimental results on the standard dataset, the proposed method can effectively alleviate the domain shift problem.

scholarly journals Multi-Layer domain adaptation method for rolling bearing fault diagnosis

2019 ◽  
Vol 157 ◽  
pp. 180-197 ◽  
Author(s):  
Xiang Li ◽  
Wei Zhang ◽  
Qian Ding ◽  
Jian-Qiao Sun
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Adaptation Method

Deep learning-based cross-domain adaptation for gearbox fault diagnosis under variable speed conditions

2020 ◽  
Vol 31 (5) ◽  
pp. 055601 ◽  
Author(s):  
Jaskaran Singh ◽  
Moslem Azamfar ◽  
Abhijeet Ainapure ◽  
Jay Lee
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Variable Speed ◽  
Cross Domain

scholarly journals Easy domain adaptation method for filling the species gap in deep learning-based fruit detection

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Wenli Zhang ◽  
Kaizhen Chen ◽  
Jiaqi Wang ◽  
Yun Shi ◽  
Wei Guo
Keyword(s):  
Deep Learning ◽  
Technology Development ◽  
Domain Adaptation ◽  
Training Dataset ◽  
Target Domain ◽  
Detection Techniques ◽  
Image Dataset ◽  
Detection And Counting ◽  
Computer Vision Technology ◽  
Adaptation Method

AbstractFruit detection and counting are essential tasks for horticulture research. With computer vision technology development, fruit detection techniques based on deep learning have been widely used in modern orchards. However, most deep learning-based fruit detection models are generated based on fully supervised approaches, which means a model trained with one domain species may not be transferred to another. There is always a need to recreate and label the relevant training dataset, but such a procedure is time-consuming and labor-intensive. This paper proposed a domain adaptation method that can transfer an existing model trained from one domain to a new domain without extra manual labeling. The method includes three main steps: transform the source fruit image (with labeled information) into the target fruit image (without labeled information) through the CycleGAN network; Automatically label the target fruit image by a pseudo-label process; Improve the labeling accuracy by a pseudo-label self-learning approach. Use a labeled orange image dataset as the source domain, unlabeled apple and tomato image dataset as the target domain, the performance of the proposed method from the perspective of fruit detection has been evaluated. Without manual labeling for target domain image, the mean average precision reached 87.5% for apple detection and 76.9% for tomato detection, which shows that the proposed method can potentially fill the species gap in deep learning-based fruit detection.

scholarly journals Wildfire Smoke Classification Based on Synthetic Images and Pixel- and Feature-Level Domain Adaptation

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7785
Author(s):  
Jun Mao ◽  
Change Zheng ◽  
Jiyan Yin ◽  
Ye Tian ◽  
Wenbin Cui
Keyword(s):  
Deep Learning ◽  
Domain Adaptation ◽  
Synthetic Data ◽  
Real Data ◽  
Training Image ◽  
Classification Model ◽  
Environmental Diversity ◽  
Wildfire Smoke ◽  
Synthetic Images ◽  
Adaptation Method

Training a deep learning-based classification model for early wildfire smoke images requires a large amount of rich data. However, due to the episodic nature of fire events, it is difficult to obtain wildfire smoke image data, and most of the samples in public datasets suffer from a lack of diversity. To address these issues, a method using synthetic images to train a deep learning classification model for real wildfire smoke was proposed in this paper. Firstly, we constructed a synthetic dataset by simulating a large amount of morphologically rich smoke in 3D modeling software and rendering the virtual smoke against many virtual wildland background images with rich environmental diversity. Secondly, to better use the synthetic data to train a wildfire smoke image classifier, we applied both pixel-level domain adaptation and feature-level domain adaptation. The CycleGAN-based pixel-level domain adaptation method for image translation was employed. On top of this, the feature-level domain adaptation method incorporated ADDA with DeepCORAL was adopted to further reduce the domain shift between the synthetic and real data. The proposed method was evaluated and compared on a test set of real wildfire smoke and achieved an accuracy of 97.39%. The method is applicable to wildfire smoke classification tasks based on RGB single-frame images and would also contribute to training image classification models without sufficient data.

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