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Fault prediction/diagnosis and sensor validation technique for a steam power plant

Lookup NU author(s): Professor Ehsan Mesbahi

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Abstract

An intelligent sensor validation and fault prediction/diagnosis technique for a typical steam power plant is proposed and studied. An auto-associative Artificial Neural Network (ANN) is trained to examine the consistency of the overall simulated data and allocate a confidence level to each signal. The same set is used to replace the missing or faulty data with a close approximation, For fault prediction and diagnostic system a feed-forward ANN with extra linear connections is trained to recognise faulty and healthy behaviour of the steam cycle for a wide range of operating conditions, Both ANNs are tested with unseen data sets, including combined scenarios of the partially failed system to assess fault prediction capability of the proposed ANN, It is concluded that a significantly more reliable sensor reading and a highly accurate fault prediction/diagnosis system is achieved.


Publication metadata

Author(s): Mesbahi E, Genrup M, Assadi M

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Proceedings of the Institute of Marine Engineering, Science and Technology Part A: Journal of Marine Engineering and Technology

Year of Conference: 2005

Pages: 33-40

ISSN: 1476-1548

Publisher: Institute of Marine Engineering, Science and Technology

URL: http://www.scopus.com/record/display.url?eid=2-s2.0-28744432049&origin=resultslist&sort=plf-f&src=s&st1=Fault+prediction%2fdiagnosis+and+sensor+validation+technique+for+a+steam+power+plant&sid=3Z6183s76a1gC6c6hOUXoTU%3a30&sot=b&sdt=b&sl=97&s=TITLE-ABS-KEY%28Fault+prediction%2fdiagnosis+and+sensor+validation+technique+for+a+steam+power+plant%29&relpos=0&relpos=0&searchTerm=TITLE-ABS-KEY%28Fault%20prediction/diagnosis%20and%20sensor%20validation%20technique%20for%20a%20steam%20power%20plant%29

Notes: Formerly part of: International Maritime Technology


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