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Novelty detection methods for online health monitoring and post data analysis of turbopumps

Hu, Lei, Hu, Niaoqing, Zhang, Xinpeng, Gu, Fengshou and Gao, Ming (2013) Novelty detection methods for online health monitoring and post data analysis of turbopumps. Journal of Mechanical Science and Technology, 27 (7). pp. 1933-1942. ISSN 1738-494X

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Abstract

As novelty detection works when only normal data are available, it is of considerable promise for health monitoring in cases lacking fault samples and prior knowledge. In this paper, two novelty detection methods are presented for health monitoring of turbopumps in large-scale liquid-propellant rocket engines. The first method is the adaptive Gaussian threshold model. This method is designed to monitor the vibration of the turbopumps online because it has minimal computational complexity and is easy for implementation in real time. The second method is the One-Class Support Vector Machine (OCSVM) which is developed for post analysis of historical vibration signals. Via post analysis the method not only confirms the online monitoring results but also provides diagnostic results so that faults from sensors are separated from those actually from the turbopumps. Both of these two methods are validated to be efficient for health monitoring of the turbopumps.

Item Type: Article
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Schools: School of Computing and Engineering
School of Computing and Engineering > Automotive Engineering Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre
School of Computing and Engineering > Diagnostic Engineering Research Centre > Energy, Emissions and the Environment Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre > Machinery Condition and Performance Monitoring Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre > Measurement System and Signal Processing Research Group
School of Computing and Engineering > High-Performance Intelligent Computing
School of Computing and Engineering > High-Performance Intelligent Computing > Information and Systems Engineering Group
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Depositing User: Cherry Edmunds
Date Deposited: 19 Jun 2014 08:46
Last Modified: 04 Nov 2015 21:12
URI: http://eprints.hud.ac.uk/id/eprint/20955

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