Issue Highlights

Abstract

The Canadian Journal of Chemical EngineeringVolume 96, Issue 2 p. 423-423 Issue HighlightsFree Access Issue Highlights First published: 02 January 2018 https://doi.org/10.1002/cjce.22973AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat 426 Online Incipient Fault Diagnosis Based on Kullback–Leibler Divergence and Recursive Principle Component Analysis Yi Chai, Songbing Tao, Wanbiao Mao, Ke Zhang and Zhiqin Zhu For principal component analysis (PCA), the Hotelling's T2, that is the most common used statistical distance, can fail in detecting small shifts such as a sensor incipient fault with low fault-to-noise ratio (FNR). In this study, a realistic online diagnosis method for incipient faults with low FNR is presented utilizing the Kullback-Leibler divergence (KLD) and recursive PCA. From simulations, it is shown that the proposed approach can detect, isolate, and estimate the sensor incipient fault of the multivariate AR system successfully. 444 Modified Partial Least Square for Diagnosing Key-Performance-Indicator-Related Faults Siyuan He, Youqing Wang and Changqing Liu Faults related to a key performance indicator (KPI) should be paid much attention in the field of multivariate statistical process monitoring. To detect KPI faults, the process data should be divided into KPI-related and KPI-unrelated parts; however, all existing methods did not decompose the residual space according to KPI. In this study, balancing the influence of principal component space and residual space on KPI, the authors find the suitable projection matrix for partial least square (PLS), which can achieve perfect partition of process data in terms of KPI. The novel modification is named as KPI-PLS. 463 Fault Diagnosis Based on the Integration of Exponential Discriminant Analysis and Local Linear Embedding Ruixuan Wang, Jing Wang, Jinglin Zhou and Haiyan Wu The paper proposes two fault diagnosis methods based on the fusion of the exponential discriminant analysis and manifold learning methods, LLEDA and NPEDA. LLEDA, a parallel strategy, focuses on the global supervised discriminant with the tradeoff of local nonlinearity preserving. NPEDA, a cascaded strategy, implements the discriminant analysis with each data firstly constructed by the linear weighted combination of its neighbours. These methods both combine the global discriminant analysis with local structure preserving, so they are particularly suitable for the feature extraction of data with strong nonlinearity and high dimensions. References 1 Y. Chai, S. Tao, W. Mao, K. Zhang, Z. Zhu, Can. J. Chem. Eng. 2018, 96, 426. 2 S. He, Y. Wang, C. Liu, Can. J. Chem. Eng. 2018, 96, 444. 3 R. Wang, J. Wang, J. Zhou, H. Wu, Can. J. Chem. Eng. 2018, 96, 463 Volume96, Issue2February 2018Pages 423-423 ReferencesRelatedInformation