Fault diagnosis of an industrial gas turbine prototype using a system identification approach

Simani, S.; Patton, Ron J.

Abstract

In this work, a model-based procedure exploiting analytical redundancy for the detection and isolation of faults on a gas turbine simulated process is presented. The main point of the paper consists of exploiting an identification scheme in connection with dynamic observer or filter design procedures for diagnostic purposes. Thus, black-box modelling and output estimation approaches to fault diagnosis are in particular advantageous in terms of solution complexity and performance achieved. Moreover, the suggested scheme is especially useful when robust solutions are considered for minimising the effects of modelling errors and noise, while maximising fault sensitivity. In order to experimentally verify the robustness of the solution obtained, the proposed FDI strategy has been applied to the simulation data of a single-shaft industrial gas turbine plant in the presence of measurement and modelling errors. Hence, extensive simulations of the test-bed process and Monte Carlo analysis are the tools for assessing experimentally the capabilities of the developed FDI scheme, when compared also with different data-driven diagnosis methods. © 2007 Elsevier Ltd. All rights reserved.

Citation

Simani, S., & Patton, R. J. (2008). Fault diagnosis of an industrial gas turbine prototype using a system identification approach. Control engineering practice, 16(7), 769-786. https://doi.org/10.1016/j.conengprac.2007.08.009

• Journal Article Type: Article
• Acceptance Date: Aug 10, 2007
• Online Publication Date: Oct 17, 2007
• Publication Date: Jul 1, 2008
• Deposit Date: Nov 13, 2014
• Journal: Control Engineering Practice
• Print ISSN: 0967-0661
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Issue: 7
• Pages: 769-786
• DOI: https://doi.org/10.1016/j.conengprac.2007.08.009
• Keywords: Control and Systems Engineering; Electrical and Electronic Engineering; Applied Mathematics; Computer Science Applications
• Public URL: https://hull-repository.worktribe.com/output/460234
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0967066107001542?via%3Dihub
• Contract Date: Nov 13, 2014