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Sensors 2016, 16(8), 1336; doi:10.3390/s16081336

Fault Diagnosis Strategies for SOFC-Based Power Generation Plants

Department of Civil, Chemical and Environmental Engineering (DICCA), University of Genoa, Genova 16145, Italy
Department of Electrical, Electronic, Telecommunications Engineering, and Naval Architecture (DITEN), University of Genoa, Genova 16145, Italy
Department of Mechanics, Energetics, Management, and Transportation (DIME), University of Genoa, Genova 16145, Italy
Pattern Analysis & Computer Vision (PAVIS), Istituto Italiano di Tecnologia (IIT), Genova 16163, Italy
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 20 May 2016 / Revised: 10 August 2016 / Accepted: 12 August 2016 / Published: 22 August 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [2435 KB, uploaded 22 August 2016]   |  


The success of distributed power generation by plants based on solid oxide fuel cells (SOFCs) is hindered by reliability problems that can be mitigated through an effective fault detection and isolation (FDI) system. However, the numerous operating conditions under which such plants can operate and the random size of the possible faults make identifying damaged plant components starting from the physical variables measured in the plant very difficult. In this context, we assess two classical FDI strategies (model-based with fault signature matrix and data-driven with statistical classification) and the combination of them. For this assessment, a quantitative model of the SOFC-based plant, which is able to simulate regular and faulty conditions, is used. Moreover, a hybrid approach based on the random forest (RF) classification method is introduced to address the discrimination of regular and faulty situations due to its practical advantages. Working with a common dataset, the FDI performances obtained using the aforementioned strategies, with different sets of monitored variables, are observed and compared. We conclude that the hybrid FDI strategy, realized by combining a model-based scheme with a statistical classifier, outperforms the other strategies. In addition, the inclusion of two physical variables that should be measured inside the SOFCs can significantly improve the FDI performance, despite the actual difficulty in performing such measurements. View Full-Text
Keywords: solid oxide fuel cell (SOFC); quantitative modelling; fault detection and isolation (FDI); model-based and data-driven strategies; pattern recognition; random forest (RF) solid oxide fuel cell (SOFC); quantitative modelling; fault detection and isolation (FDI); model-based and data-driven strategies; pattern recognition; random forest (RF)

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Costamagna, P.; De Giorgi, A.; Gotelli, A.; Magistri, L.; Moser, G.; Sciaccaluga, E.; Trucco, A. Fault Diagnosis Strategies for SOFC-Based Power Generation Plants. Sensors 2016, 16, 1336.

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