Yang, Zhijing and Bingham, Chris and Ling, Wing-Kuen and Gallimore, Michael and Stewart, Paul (2011) Intelligent condition monitoring via sparse representation and principal component analysis for industrial gas turbine systems. In: International Conference on Mechanical Engineering and Technology (ICMET-London 2011), November 2011, London.
Full content URL: htttp://dx.doi.org/10.1115/1.859896.paper76
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|Item Type:||Conference or Workshop contribution (Presentation)|
|Item Status:||Live Archive|
This paper proposes an intelligent condition monitoring methodology based on sparse representation and principal component analysis (PCA), for application to key constituent systems of industrial gas turbine units. The contribution and novelty of the presented methods are i) To detect sensor faults, a method based on the recognition results of PCA, is described; ii) A condition monitoring method based on sparse representation data mining techniques, is proposed; (iii) Even in the presence of measurements from faulted sensors that can still provide some information but may be subject to drift or bias, for instance, it is shown that the condition of an operational unit can be assessed. Experimental results based on data from a 14MW SGT-400 industrial gas turbine are used to demonstrate the efficacy of the developed procedures, although it should be noted that the proposed methodologies are much more widely applicable to many other industrial and commercial systems.
|Keywords:||Remote monitoring and sensing, Condition monitoring, Gas Turbines, Principal component analysis, bmjdoi|
|Subjects:||G Mathematical and Computer Sciences > G700 Artificial Intelligence|
H Engineering > H321 Turbine Technology
|Divisions:||College of Science > School of Engineering|
|Deposited On:||19 Aug 2012 19:41|
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