Journal ArticleDOI
Canonical correlation analysis-based fault detection methods with application to alumina evaporation process
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TLDR
In this paper, canonical correlation analysis (CCA)-based fault detection methods are proposed for both static and dynamic processes, which are applied to an alumina evaporation process, and the achieved results show that both methods are applicable for fault detection.About:
This article is published in Control Engineering Practice.The article was published on 2016-01-01. It has received 151 citations till now. The article focuses on the topics: Fault detection and isolation & Fault (power engineering).read more
Citations
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Journal ArticleDOI
A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network.
TL;DR: By comparing EDBN and DBN under different network structures, the results show that EDBN has better feature extraction and fault classification performance than traditional DBN.
Journal ArticleDOI
Fault Detection for Non-Gaussian Processes Using Generalized Canonical Correlation Analysis and Randomized Algorithms
TL;DR: An FD technique combining the generalized CCA with the threshold-setting based on the randomized algorithm is proposed and applied to the simulated traction drive control system of high-speed trains and shows that the proposed method is able to improve the detection performance significantly in comparison with the standard generalized C CA-based FD method.
Journal ArticleDOI
A Review of Fault Detection and Diagnosis for the Traction System in High-Speed Trains
Hongtian Chen,Bin Jiang +1 more
TL;DR: A comprehensive review on the fault detection and diagnosis techniques for high-speed trains is presented using data-driven methods which are receiving increasing attention in transportation fields over the past ten years.
Journal ArticleDOI
Review and Perspectives of Data-Driven Distributed Monitoring for Industrial Plant-Wide Processes
TL;DR: The key idea of DMSPPM is first decomposing a plant-wide process into multiple subprocesses and then establishing a data-driven model for monitoring the process, in which process variable decomposition is important for guaranteeing the monitoring performance.
Journal ArticleDOI
Parallel PCA–KPCA for nonlinear process monitoring
Qingchao Jiang,Xuefeng Yan +1 more
TL;DR: In this article, the authors proposed a parallel PCA-KPCA (P-PCA-kPCA) model and monitoring scheme that incorporates randomized algorithm (RA) and genetic algorithm (GA) for fault detection for a process with linearly correlated and nonlinearly related variables.
References
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Book ChapterDOI
Relations Between Two Sets of Variates
TL;DR: The concept of correlation and regression may be applied not only to ordinary one-dimensional variates but also to variates of two or more dimensions as discussed by the authors, where the correlation of the horizontal components is ordinarily discussed, whereas the complex consisting of horizontal and vertical deviations may be even more interesting.
Journal ArticleDOI
Detection of abrupt changes: theory and application
TL;DR: A unified framework for the design and the performance analysis of the algorithms for solving change detection problems and links with the analytical redundancy approach to fault detection in linear systems are established.
Book
Fault detection and diagnosis in engineering systems
TL;DR: In this article, a fault detection and diagnosis framework for discrete linear systems with residual generators and residual generator parameters is presented for additive and multiplicative faults by parameter estimation using a parity equation.
Book
Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools
TL;DR: This book is to introduce basic model-based FDI schemes, advanced analysis and design algorithms and the needed mathematical and control theory tools at a level for graduate students and researchers as well as for engineers.
Journal ArticleDOI
A review of process fault detection and diagnosis: Part III: Process history based methods
TL;DR: This final part discusses fault diagnosis methods that are based on historic process knowledge that need to be addressed for the successful design and implementation of practical intelligent supervisory control systems for the process industries.