NDLI: Maximum Likelihood 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	1963
Abstract	In this paper, we develop a probabilistic methodology for failure diagnosis in finite state machines based on a sequence of unreliable observations. Given prior knowledge of the input probability distribution but without actual knowledge of the applied input sequence, the core problem we consider is to choose from a pool of known, deterministic finite state machines (FSMs) the one that most likely matches the given sequence of observations. The problem becomes challenging because of sensor failures which may corrupt the observed sequence by inserting, deleting, and transposing symbols with certain probabilities (that are assumed known). We propose an efficient recursive algorithm for obtaining the most likely underlying FSM, given the possibly erroneous observed sequence. The proposed algorithm essentially allows us to perform online maximum likelihood failure diagnosis and is applicable to more general settings where one is required to choose the most likely underlying hidden Markov model (HMM) based on a sequence of observations that may get corrupted with known probabilities. The algorithm generalizes existing recursive algorithms for likelihood calculation in HMMs by allowing loops in the associated trellis diagram. We illustrate the proposed methodology using an example of diagnosis in the context of communication protocols.
Sponsorship	IEEE Control Systems Society
Starting Page	579
Ending Page	593
Page Count	15
File Size	597621
File Format	PDF
ISSN	00189286
Volume Number	55
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Automata Hidden Markov models Discrete event systems Protocols Probability distribution Context Telecommunication network reliability Medical diagnostic imaging Transportation State-space methods transpositions Deletions discrete event systems (DESs) failure diagnosis finite state machines (FSMs) insertions maximum likelihood model classification probabilistic automata
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering Computer Science Applications

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Probabilistic detection of FSM single state-transition faults based on state occupancy measurements

Stochastic Failure Prognosability of Discrete Event Systems

A Petri Net Diagnoser for Discrete Event Systems Modeled by Finite State Automata

Safe Diagnosability of Stochastic Discrete Event Systems

Current-State Opacity Formulations in Probabilistic Finite Automata

Diagnosability of stochastic discrete-event systems

Integrating Temporal Logic as a State-Based Specification Language for Discrete-Event Control Design in Finite Automata

Fault identification using a finite state machine model with unreliable partially observed data sequences

Maximum Likelihood Sequence Estimation for Hidden Reciprocal Processes

Maximum Likelihood Failure Diagnosis in Finite State Machines Under Unreliable Observations

Similar Documents

Probabilistic detection of FSM single state-transition faults based on state occupancy measurements

Stochastic Failure Prognosability of Discrete Event Systems

A Petri Net Diagnoser for Discrete Event Systems Modeled by Finite State Automata

Safe Diagnosability of Stochastic Discrete Event Systems

Current-State Opacity Formulations in Probabilistic Finite Automata

Diagnosability of stochastic discrete-event systems

Integrating Temporal Logic as a State-Based Specification Language for Discrete-Event Control Design in Finite Automata

Fault identification using a finite state machine model with unreliable partially observed data sequences

Maximum Likelihood Sequence Estimation for Hidden Reciprocal Processes

Maximum Likelihood Failure Diagnosis in Finite State Machines Under Unreliable Observations