NDLI: A benchmark of Canonical Variate Analysis for fault detection and diagnosis

Content Provider	IEEE Xplore Digital Library
Author	Carcel, C.R. Yi Cao Mba, D.
Copyright Year	2014
Description	Author affiliation: Sch. of Eng., Cranfield Univ., Cranfield, UK (Carcel, C.R.; Yi Cao; Mba, D.)
Abstract	The early detection and diagnosis of faults can improve the energy efficiency of industrial processes by avoiding the inefficient operation of faulty equipment as well as minimizing unplanned shutdowns, downtime and extensive damage to other parts of the system. In addition, industrial needs are evolving fast towards more flexible schemes. As a consequence, it is often required to adapt the plant production to the demand, which can be volatile depending on the application. This is why it is important to develop tools that can monitor the condition of the process working under varying operational conditions. Canonical Variate Analysis (CVA) is a multivariate data driven methodology that can be applied to detect and diagnose faults in industrial systems. This method has the ability to capture the process dynamics more efficiently than other similar data driven algorithms. The aim of this work is to provide a benchmark case to demonstrate the ability of CVA to detect and diagnose artificially seeded faults in a large scale test rig and measure the impact of those faults on the system performance, in particular its energy efficiency. The results obtained suggest that CVA can be effectively used for fault detection using real process data. The faults introduced were successfully detected in the early stages of degradation, and the source of the faults was identified using contribution plots.
Starting Page	425
Ending Page	431
File Size	511161
Page Count	7
File Format	PDF
ISBN	9781479950119
DOI	10.1109/CONTROL.2014.6915178
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-09
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Fault detection Particle separators Energy Canonical Experimental Valves Diagnosis Matrix decomposition Covariance matrices Multivariate Monitoring
Content Type	Text
Resource Type	Article

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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
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