Visualization of Damage Progress in Solid Oxide Fuel Cells

The fuel cell is regarded as a highly efficient, low-pollution power generation system. In particular, Solid Oxide Fuel Cell (SOFC) has a high generation efficiency. However, a crucial issue in putting SOFC to practical use is the establishment of a technique for evaluating the deterioration. We previously developed a technique by which to measure the mechanical damage of SOFC using the Acoustic Emission (AE) method. In the present paper, we applied the kernel Self-Organizing Map (SOM), which is an extended neural network model, to produce a cluster map reflecting the similarity of AE events. The obtained map visualized the change in occurrence patterns of similar AE events, revealing four phases of damage progress. The methodology of the present study provides a common foundation for a comprehensive damage evaluation system and a damage monitoring system.

[1]  Andrew J. Viterbi,et al.  Error bounds for convolutional codes and an asymptotically optimum decoding algorithm , 1967, IEEE Trans. Inf. Theory.

[2]  Paul McIntire,et al.  Acoustic emission testing , 1987 .

[3]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[4]  Samuel Kaski,et al.  Self organization of a massive document collection , 2000, IEEE Trans. Neural Networks Learn. Syst..

[5]  Isamu Yasuda,et al.  Lattice Expansion of Acceptor-doped Lanthanum Chromites under High-temperature Reducin'g Atmospheres , 2000 .

[6]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[7]  Alan Atkinson,et al.  Chemically-induced stresses in ceramic oxygen ion-conducting membranes , 2000 .

[8]  Péter András Kernel-Kohonen Networks , 2002, Int. J. Neural Syst..

[9]  S. Singhal Solid oxide fuel cells for stationary, mobile, and military applications , 2002 .

[10]  Karen Margaret Holford,et al.  Automatic Classification of Acoustic Emission Patterns , 2003 .

[11]  Nuno Vasconcelos,et al.  A Kullback-Leibler Divergence Based Kernel for SVM Classification in Multimedia Applications , 2003, NIPS.

[12]  Brian W. Sheldon,et al.  Stresses due to oxygen potential gradients in non-stoichiometric oxides , 2004 .

[13]  Jon M. Kleinberg,et al.  Bursty and Hierarchical Structure in Streams , 2002, Data Mining and Knowledge Discovery.

[14]  R. Gaertner,et al.  Influence of hydrolytic ageing on the acoustic emission signatures of damage mechanisms occurring during tensile tests on a polyester composite: Application of a Kohonen’s map , 2006 .

[15]  Hajime Omura,et al.  Tracking the Onset of Damage Mechanism in Ceria-based Solid Oxide Fuel Cells under Simulated Operating Conditions , 2006 .

[16]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[17]  Masayuki Numao,et al.  Combining Burst Extraction Method and Sequence-Based SOM for Evaluation of Fracture Dynamics in Solid Oxide Fuel Cell , 2007 .

[18]  Fabrice Rossi,et al.  Batch kernel SOM and related Laplacian methods for social network analysis , 2008, Neurocomputing.

[19]  Tomoyuki Higuchi,et al.  Dynamic spectrum classification by kernel classifiers with divergence-based kernels and its applications to acoustic signals , 2009, Int. J. Knowl. Eng. Soft Data Paradigms.

[20]  Masayuki Numao,et al.  Kullback-Leibler Divergence Based Kernel SOM for Visualization of Damage Process on Fuel Cells , 2010, 2010 22nd IEEE International Conference on Tools with Artificial Intelligence.

[21]  Keiji Yashiro,et al.  Fracture process of nonstoichiometric oxide based solid oxide fuel cell under oxidizing/reducing gradient conditions , 2010 .