The study of TE process based on the improved PCA method

Yu, Yang; Zhao, Yongsheng; Yang, Qing; Wang, D.; Chen, Liang

doi:10.1109/iciea.2008.4582712

Cited by 1 publication

(2 citation statements)

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“…(1) Logistic Regression (LR) [2] (2) Support Vector Machine (SVM) [1] (3) Cross-Domain Spectral Classification (CDSC) [35] (4) Transfer Component Analysis (TCA) [36] (5) Deep Domain Confusion (DDC) [34] (6) Domain Adversarial Neural Network (DANN) [37] LR and SVM are the classical conventional supervised classification approaches, which have been successfully applied for fault diagnosis. CDSC and TCA are both effective methods based on transfer subspace learning proposed for fault diagnosis issues, especially, TCA is the representative technique by searching the feature subspace in the domain adaptation field.…”

Section: B Compared Methodsmentioning

confidence: 99%

“…Based on the actual chemical reaction process, TE process was created on an open and challenging chemical model simulation platform, whose purpose is to provide a realistic industrial process for evaluating process control and monitoring methods [34]. As a data source for comparing various methods, TE datasets has been widely used in research such as control optimization, process monitoring and fault diagnosis.…”

Section: ) Te Process Datamentioning

confidence: 99%

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Cross-Task Fault Diagnosis Based on Deep Domain Adaptation With Local Feature Learning

2020

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Data based intelligent fault diagnosis is a critical tool for the healthy development of industry process. In actual industrial production, there are often a few or even no labeled samples for target monitoring problem, while large amounts of training data come from different but related diagnosis task under variable working conditions. To utilize the labeled data on related issue for better monitoring performance, the cross-task fault diagnosis based on deep domain adaptation with local feature leaning is proposed. In our strategy, the two-stream stacked autoencoders based deep architecture is used to extract transferable features of collected data across the target diagnosis task domain and the related data-rich monitoring task domain. Then, the maximum mean discrepancy is introduced to establish a deep transfer diagnosis model. Moreover, to further optimize the model, we propose the local feature learning, which can make test data with better intra-class compactness and inter-class separability. Eventually, the proposed method is verified on the Tennessee Eastman process and the rolling bearing data, the results show that our approach achieves positive performance for cross-task fault diagnosis problems.

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