NDLI: Bayesian fault isolation in multivariate statistical process monitoring dimitry gorinevsky

Content Provider	IEEE Xplore Digital Library
Author	Gorinevsky, D.
Copyright Year	2011
Description	Author affiliation: Mitek Analytics LLC, Palo Alto, CA, USA (Gorinevsky, D.)
Abstract	Abstract-Consider a set of multivariable input/output process data. Given a new observation we ask the following questions: is the new observation normal or abnormal? is one of the inputs or outputs abnormal (faulty) and which? Assuming a linear regression model of the process, the problem is solved through Bayesian hypothesis testing. The proposed formulation differs from existing multivariable statistical process control methods by taking uncertainty (variance) of the empirical regression model into account. The derived solution matches the established methods for anomaly detection and fault isolation in case there is no model uncertainty. Taking the model uncertainty into account, the proposed solution yields significant accuracy improvement compared to existing approaches. This is because ill-conditioned multivariable regression models can have large uncertainty even for large training data sets. The paper also demonstrates that isolating faults to a small ambiguity group works significantly better than the exact isolation.
Sponsorship	Control Syst. Soc.
Starting Page	1963
Ending Page	1968
File Size	250010
Page Count	6
File Format	PDF
ISBN	9781457700804
ISSN	07431619
e-ISBN	9781457700811
DOI	10.1109/ACC.2011.5991158
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-29
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Monitoring Uncertainty Circuit faults Bayesian methods Tin Indexes Training data
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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

Bayesian fault isolation in multivariate statistical process monitoring dimitry gorinevsky

Fault Isolation in Data-Driven Multivariate Process Monitoring

Bayesian Fault Isolation in Multivariate Statistical Process Monitoring

Fault detection and identification method based on multivariate statistical techniques

Monitoring of Active Systems With Stratified Uncertain Observations

Test generation for fault isolation in analog circuits using behavioral models

Bayesian network approach based on fault isolation for power system fault diagnosis

Fault detection and prognosis methods for a monitoring system of rotating electrical machines

Bayesian fault identification of multistage processes

Bayesian fault isolation in multivariate statistical process monitoring dimitry gorinevsky

Similar Documents

Bayesian fault isolation in multivariate statistical process monitoring dimitry gorinevsky

Fault Isolation in Data-Driven Multivariate Process Monitoring

Bayesian Fault Isolation in Multivariate Statistical Process Monitoring

Fault detection and identification method based on multivariate statistical techniques

Monitoring of Active Systems With Stratified Uncertain Observations

Test generation for fault isolation in analog circuits using behavioral models

Bayesian network approach based on fault isolation for power system fault diagnosis

Fault detection and prognosis methods for a monitoring system of rotating electrical machines

Bayesian fault identification of multistage processes

Bayesian fault isolation in multivariate statistical process monitoring dimitry gorinevsky