NDLI: Probabilistic failure diagnosis in finite state machines under unreliable observations

Content Provider	IEEE Xplore Digital Library
Author	Athanasopoulou, E. Lingxi Li Hadjicostis, C.N.
Copyright Year	2006
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Illinois Univ., Urbana-Champaign, IL (Athanasopoulou, E.; Lingxi Li; Hadjicostis, C.N.)
Abstract	In this paper we develop a probabilistic methodology for calculating the likelihood that an observed, possibly corrupted event sequence was generated by two (or more) candidate finite state machines (FSMs) (one of which could represent the normal mode of operation and the other(s) could represent the failed model(s)). Our objective is to perform failure diagnosis by deciding which FSM is most likely to have generated the observed event sequence. The underlying problem relates to the evaluation problem in hidden Markov models (HMMs) which calculates the probability that an observed sequence is generated by a given Markov model. However, the additional challenge in our setup is the fact that errors may corrupt the observed sequence, potentially causing loops in the resulting trellis diagram. These errors include, in their most basic form, event insertions and deletions and could arise under a variety of conditions (e.g., due to sensor failures or due to problems encountered in the links connecting the system sensors with the diagnoser). Given the possibly erroneous observed sequence, we propose an algorithm for obtaining the most likely underlying FSM
Starting Page	301
Ending Page	306
File Size	270947
Page Count	6
File Format	PDF
ISBN	1424400538
DOI	10.1109/WODES.2006.1678446
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-10
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Military computing Automata Hidden Markov models Transportation Probability Sensor systems Discrete event systems Pattern recognition Joining processes Medical diagnostic imaging
Content Type	Text
Resource Type	Article

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