Applied fault detection and diagnosis for industrial gas turbine systems

Zhang, Yu and Bingham, Chris and Garlick, Mike and Gallimore, Michael (2017) Applied fault detection and diagnosis for industrial gas turbine systems. International Journal of Automation and Computing . ISSN 1476-8186

Documents
IJAC.pdf
[img]
[Download]
[img]
Preview
PDF
IJAC.pdf - Whole Document
Available under License Creative Commons Attribution.

1MB
Item Type:Article
Item Status:Live Archive

Abstract

The paper presents readily implementable approaches for fault detection and diagnosis (FDD) based on measurements from multiple sensor groups, for industrial systems. Specifically, the use of hierarchical clustering (HC) and self-organizing map neural networks (SOMNNs) are shown to provide robust and user-friendly tools for application to industrial gas turbine (IGT) systems. HC fingerprints are found for normal operation, and FDD is achieved by monitoring cluster changes occurring in the resulting dendrograms. Similarly, fingerprints of operational behaviour are also obtained using SOMNN based classification maps (CMs) that are initially determined during normal operation, and FDD is performed by detecting changes in their CMs. The proposed methods are shown to be capable of FDD from a large group of sensors that measure a variety of physical quantities. A key feature of the paper is the development of techniques to accommodate transient system operation, which can often lead to false-alarms being triggered when using traditional techniques if the monitoring algorithms are not first desensitized. Case studies showing the efficacy of the techniques for detecting sensor faults, bearing tilt pad wear and early stage pre-chamber burnout, are included. The presented techniques are now being applied operationally and monitoring IGTs in various regions of the world.

Keywords:Fault detection and diagnosis, hierarchical clustering, self-organizing map neural network, bmjgoldcheck
Subjects:H Engineering > H661 Instrumentation Control
G Mathematical and Computer Sciences > G700 Artificial Intelligence
Divisions:College of Science > School of Engineering
ID Code:17324
Deposited On:06 May 2015 09:30

Repository Staff Only: item control page