Joint Feature and Model Selection for SVM Fault Diagnosis in Solid Oxide Fuel Cell Systems

Gabriele Moser,Lissy Pellaco,Paola Costamagna,Andrea Greco,Andrea Trucco,Andrea De Giorgi,Loredana Magistri

doi:10.1155/2015/282547

Abstract

This paper describes an original technique for the joint feature and model selection in the context of support vector machine (SVM) classification applied as a diagnosis strategy in model-based fault detection and isolation (FDI). We demonstrate that the proposed technique contributes to the solution of an open research problem: to design a robust FDI procedure, correctly functioning with different operating conditions and fault sizes, specifically settled for an electric generation system based on solid oxide fuel cells (SOFCs). By using a quantitative model of the generation system coupled to an optimized SVM classifier, a satisfactory FDI procedure is achieved, which is robust against modeling and measurement errors and is compliant with practical deployment.

Highlights

The interest in electric generation plants based on the fuel cell (FC) technology [1, 2] is constantly growing owing to their high energy conversion efficiency and environmental compatibility
This paper describes an original technique for the joint feature and model selection in the context of support vector machine (SVM) classification applied as a diagnosis strategy in model-based fault detection and isolation (FDI)
We demonstrate that the proposed technique contributes to the solution of an open research problem: to design a robust FDI procedure, correctly functioning with different operating conditions and fault sizes, settled for an electric generation system based on solid oxide fuel cells (SOFCs)

Summary

Introduction

The interest in electric generation plants based on the fuel cell (FC) technology [1, 2] is constantly growing owing to their high energy conversion efficiency and environmental compatibility. Systems based on SOFC stacks are universally reputed to be one of the best options for distributed electric generation plants, the literature regarding the FDI procedure in these systems is still scarce [7,8,9,10]. In many of these papers [7, 8], the proposed diagnosis strategy [6] is limited to inference approaches that use a binary fault signature matrix arranged according to a fault tree analysis (i.e., a deductive top-down tool, typically used in safety and reliability engineering) or an improved version of such a matrix [10]. To overcome the weaknesses of the binary signature matrix [11], we recently proposed [9] a supervised classification approach, implemented through a SVM, as a possible diagnosis strategy. We demonstrated that the detection and isolation of faults of random size, occurring in an SOFC generation system that works under many different steady-state operating conditions, is possible by using a supervised SVM classifier

Methods

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Conclusion