NDLI: A Bayesian Approach for Disturbance Detection and Classification and Its Application to State Estimation in Run-to-Run Control

Content Provider	IEEE Xplore Digital Library
Author	Jin Wang He, Q.P.
Copyright Year	1988
Abstract	With growing demand for effective management of abnormal situations in process industry, disturbance detection and classification has drawn considerable interest from researchers in both industry and academia. In this paper, a disturbance detection and classification method is developed using Bayesian statistics. The theoretical derivation of the proposed method as well as its practical implementation are provided. With the introduction of preand post-change windows, detection and classification are achieved simultaneously in the proposed method through matching the posterior probability pattern to predefined patterns. An overlapping window mechanism is incorporated into the proposed method to minimize detection and classification delay. A simulation example is given to illustrate the robustness and effectiveness of the proposed disturbance detection method. One application of the proposed Bayesian disturbance detection and classification algorithm is a Bayesian enhanced exponentially weighted moving average (B-EWMA) state estimator which improves state estimation in the run-to-run control of semiconductor manufacturing processes. The superior performance of B-EWMA compared to the conventional EWMA is demonstrated using an industrial example
Sponsorship	IEEE Electron Devices Society IEEE Components, Packaging, and Manufacturing Technology Society IEEE Reliability Society IEEE Solid-State Circuits Society
Starting Page	126
Ending Page	136
Page Count	11
File Size	947892
File Format	PDF
ISSN	08946507
Volume Number	20
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-05-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods State estimation Statistics Pattern matching Probability Delay Robustness Classification algorithms Manufacturing processes Manufacturing industries state estimation Bayesian statistics disturbance classification disturbance detection process control
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Condensed Matter Physics Electronic, Optical and Magnetic Materials Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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