NDLI: Fault Detection and Diagnosis of Multiple-Model Systems With Mismodeled Transition Probabilities

Content Provider	IEEE Xplore Digital Library
Author	Shunyi Zhao Biao Huang Fei Liu
Copyright Year	1982
Abstract	This paper proposes an improved interacting multiple-model (I²MM) algorithm with inaccurate transition probabilities (TPs) for fault detection and diagnosis (FDD). We first study the influence of inaccurate TPs by inspecting the expectation and covariances of residual error vectors in the traditional IMM method. It shows that Kalman filters can be retained as subfilters in the presence of mismodeled TPs, and the effect of TPs can be removed naturally if all of the probabilities of true modes are equal to one. In view of this, a modification operator is proposed to make the real mode probabilities heuristically approach one. The modification degree is governed by a parameter determined by the online measurements. When the modification parameter calculated is identical to one, the I²MM method reduces to the conventional IMM algorithm. An experiment designed through a ball-and-tube testbed is presented to demonstrate that the I²MM-based FDD method can provide more reliable FDD results and reduce the possibility of false alarms.
Sponsorship	IEEE Industrial Electronics Society
Starting Page	5063
Ending Page	5071
Page Count	9
File Size	801913
File Format	PDF
ISSN	02780046
Volume Number	62
Issue Number	8
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vectors Kalman filters Algorithm design and analysis Materials Noise measurement Fault detection Estimation unknown transition probabilities fault diagnosis multiple-model systems unknown transition probabilities (TPs)
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering Computer Science Applications

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