A new type of change-detection scheme based on the window-limited weighted likelihood ratios
Fangyi He1; Tiantian Mao2; Taizhong Hu2; Lianjie Shu3
2018-03-15
Source PublicationEXPERT SYSTEMS WITH APPLICATIONS
ABS Journal Level3
ISSN0957-4174
Volume94Pages:149-163
Abstract

Process monitoring has been widely recognized as an important and critical tool in system monitoring for detection of abnormal behavior and quality improvement. In manufacturing processes or industrial systems, several sources of out-of-control variation, such as tool wear, gradual equipment deterioration, vibration, inconsistent material etc., often result in dynamic changes in the process parameter, or result in special residual signals of the systems. Detecting such weak or decaying signals is a challenging task. Although the window-limited generalized likelihood ratio (wl-GLR) scheme is widely used in changepoint detection and has some advantages, it may perform poorly when it is used to detect weak or decaying signals. This paper proposes a new change-detection scheme, the window-limited weighted likelihood ratio (wl-WLR) scheme, to improve the wl-GLR scheme. To do this, a new statistical distance measure called the GLR divergence is first defined and then its properties are analyzed. The wl-WLR scheme is designed to monitor the weighted average of the GLR divergences in a moving window, and the wl-GLR scheme can be viewed as a special case of the wl-WLR scheme. Two types of weight functionals are introduced and investigated for the wl-WLR scheme. Numerical algorithms to select the optimal weight parameters are provided based on a calibration sample. Extensive simulation study favors the proposed wl-WLR scheme for detecting weak or decaying signals, and its performance is robust even if the weight parameters are not accurately estimated. This paper has online supplementary materials. (C) 2017 Elsevier Ltd. All rights reserved.

KeywordStatistical Distance Statistical Process Control Signal Detection Generalized Likelihood Ratio Weighted Likelihood Ratio Stochastic Order
DOI10.1016/j.eswa.2017.10.051
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering ; Operations Research & Management Science
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS IDWOS:000418218800013
PublisherPERGAMON-ELSEVIER SCIENCE LTD
The Source to ArticleWOS
Fulltext Access
Citation statistics
Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF ACCOUNTING AND INFORMATION MANAGEMENT
Corresponding AuthorTiantian Mao
Affiliation1.School of Finance, Southwestern University of Finance and Economics, Chengdu, Sichuan 611130, PR China
2.Department of Statistics and Finance, University of Science and Technology of China, Hefei, Anhui 230026, PR China
3.Faculty of Business Administration, University of Macau, Macau, PR China
Recommended Citation
GB/T 7714
Fangyi He,Tiantian Mao,Taizhong Hu,et al. A new type of change-detection scheme based on the window-limited weighted likelihood ratios[J]. EXPERT SYSTEMS WITH APPLICATIONS,2018,94:149-163.
APA Fangyi He,Tiantian Mao,Taizhong Hu,&Lianjie Shu.(2018).A new type of change-detection scheme based on the window-limited weighted likelihood ratios.EXPERT SYSTEMS WITH APPLICATIONS,94,149-163.
MLA Fangyi He,et al."A new type of change-detection scheme based on the window-limited weighted likelihood ratios".EXPERT SYSTEMS WITH APPLICATIONS 94(2018):149-163.
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