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Research Papers

Fuel Cell Diagnostics Using Identification Measurement Theory

[+] Author and Article Information
K. T. Koshekov

Department of Energetics and Instrumentation,
North-Kazakhstan State University,
ul. Pushkina, 86,
Petropavlovsk KZ-150000, Kazakhstan
e-mail: kkoshekov@mail.ru

Yu. N. Klikushin

Department of the Technology of Electronics,
Omsk State Technical University,
pr. Mira, 11,
Omsk RU-644050, Russia
e-mail: iit@omgtu.ru

V. Yu. Kobenko

Department of the Technology of Electronics,
Omsk State Technical University,
pr. Mira, 11,
Omsk RU-644050, Russia
e-mail: kobra_vad@rambler.ru

Yu. K. Evdokimov

Department of Radio Electronics
and Measuring Instruments,
Kazan National Research
Technical University,
ul. K. Marksa, 10,
Kazan RU-420111, Russia
e-mail: evdokimov1@mail.ru

A. V. Demyanenko

Department of Energetics and Instrumentation,
North-Kazakhstan State University,
ul. Pushkina, 86,
Petropavlovsk KZ-150000, Kazakhstan
e-mail: demianenkoav@mail.ru

1Corresponding author.

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF FUEL CELL SCIENCE AND TECHNOLOGY. Manuscript received March 5, 2014; final manuscript received March 18, 2014; published online May 2, 2014. Editor: Nigel M. Sammes.

J. Fuel Cell Sci. Technol 11(5), 051003 (May 02, 2014) (9 pages) Paper No: FC-14-1027; doi: 10.1115/1.4027395 History: Received March 05, 2014; Revised March 18, 2014

The possibility to use instruments of identification measurement theory to solve the problems of diagnostics of fuel cells according to their noise characteristics is considered in this paper. The offered techniques of diagnostic signals processing are based on the identification measurement of time and probabilistic characteristics, the comparison of model signals with the ones under analysis according to the reading values, the classification of signals according to the waveform parameter and characteristic frequency, the building of hierarchical structures, and the assessment of the signal structures by the fractal indices. All proposed techniques are applicable for the diagnosis of a fuel cell, but thanks to graphical representation of classification trees of noise signals, the more efficient method for experts is the one based on the building of hierarchical structures.

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Copyright © 2014 by ASME
Topics: Signals , Fuel cells
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References

Figures

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Fig. 1

Identification measurement logic

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Fig. 2

Visual patterns of signal time characteristic (waveform graph) and probabilistic characteristic (histogram) files: (a) R1_0.4.txt, (b) R1_1.txt, (c) R1_2.4.txt, (d) R1_4.4.txt, (e) R1_10.4.txt, and (f) R1_OC.txt

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Fig. 4

The example of assessment algorithm for rank distance between the scales

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Fig. 5

General tree of FC noise signals

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Fig. 3

Classification tree algorithm

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