Fault diagnosis in fuel cell systems using quantitative models and support vector machines

Abstract

Fault detection and identification are new and challenging tasks for electrical generation plants that are based on solid oxide fuel cells. The use of a quantitative model of the plant together with a support vector machine to design and operate a supervised classification system is proposed. This type of system, which uses a few easy-tomeasure features selected through the maximisation of a classification error bound, proved to be effective in revealing a faulty condition and identifying it among the four considered fault classes.Introduction: Solid oxide fuel cells (SOFCs) are electrochemical reactors that have been extensively studied for their application in power generation plants, for both stationary and transportation use [1]. Although SOFCs exhibit high energy conversion efficiencies and environmental compatibilities, systems based on these fuel cells still suffer from low reliability (of both the fuel cells and the entire system) and a limited lifetime. Thus, automatic fault detection and identification (FDI) are of paramount importance for the development of SOFC power generation plants and their commercial diffusion.Several FDI methods have been proposed and applied to a variety of systems in the chemical and mechanical engineering fields [2, 3]. Since the physical processes governing the functioning of an SOFC-based power plant are known, a quantitative model of the system under analysis can be developed. This type of model allows for reliable and physically motivated fault diagnosis and represents an ideal basis to develop FDI techniques. Although the model can be used offline to generate reliable data for the power plant under both healthy and faulty conditions, the FDI task requires a technique that, using such data, reveals and classifies the faults while considering online data.Pattern recognition is a discipline that has long enabled the development of FDI techniques [2]. However, to the best of the authors' knowledge, none of these techniques have been proposed in the context of SOFC systems. A recent report addressing fault diagnosis in SOFC systems [4] only discusses the fault tree and fault signature matrix (i.e. deductive top-down tools, typically used in safety and reliability engineering, based on binary information) as FDI approaches. Although FDI has received more attention in proton exchange membrane fuel cells [5], pattern recognition techniques have been rarely used for fault classification.In this Letter, we propose a supervised classification technique that is based on a support vector machine (SVM) combined with a quantitative model of the SOFC system and performs FDI during the operation of a power generation plant. SVMs are well known in pattern recognition because of their generalisation properties and accurate results in multiple applications [6,7]. In the proposed FDI technique, SVMs are used to classify faults and a feature selection method based on the maximisation of an analytical error bound [8] is developed to determine the most relevant physical variables to be measured...

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

“…As discussed previously and according to [23], in this study, the real plant is replaced by a copy of the quantitative model, modified in view of simulating the effect of different faults of various size occurring in the plant (see Figure 1). Whereas ideal conditions were assumed in [9], here, the model uncertainty and measurement tolerance are considered by adding random errors to the values of the physicochemical variables simulated for the real plant (see Figure 1). This allows us to attain realistic residuals and to investigate the sensitivity of the FDI procedure by testing different error magnitudes.…”

“…Although 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]. Moreover, 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].…”

“…Moreover, 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.…”

“…However, only preliminary results (obtained under the assumption that the model provides exact predictions) are reported in [9], leaving many issues unresolved. One of them is the choice of the best physicochemical variables to be measured during the system operation and used for the FDI procedure.…”

Study on A Fault Diagnosis Method of Rolling Element Bearing Based on Improved ACO and SVM Model

“…[18] proposed a novel intelligent fault diagnosis model based on multi-kernel support vector machine (MSVM) with chaotic particle swarm optimization (CPSO) for roller bearing fault diagnosis . Pellaco et al [19] designed and operated a supervised classification system based on quantitative model of the plant together with a support vector machine. Su et al [20] proposed a multi-fault diagnosis method for rotating machinery based on orthogonal supervised linear local tangent space alignment (OSLLTSA) and least square support vector machine (LS-SVM).…”