<p> Proton exchange membrane fuel cells are a promising technology for the automotive industry. However, it is necessary to develop effective diagnostic tools to improve system reliability and operational life to be competitive in the automotive market. Early detection and diagnosis of fuel cell faults may lead to increased system reliability and performance. An efficient on-line diagnosis system may prevent irreparable damage due to poor control and system fatigue. Current attempts to monitor fuel cell stack health are limited to specialized tests that require numerous parameters. An increased effort exists to minimize parameter input and maximize diagnostic robustness. Most methods use complex models or black-box methods to determine a singular fault mode. Limited research exists with pre-processing or statistical methods. This research examines the effectiveness of a Naïve Bayes classifier on determining multiple states of health; such as healthy, dry, degraded catalyst, and inert gas build-up. Independent component analysis and principal component analysis are investigated for preprocessing. An automotive style fuel cell model is developed to generate data for these purposes. Since automotive applications have limited computational power, a system that minimizes the number of inputs and computational complexity is preferred.</p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:1586450 |
| Date | 24 April 2015 |
| Creators | Rukas, Christopher J. |
| Publisher | Rochester Institute of Technology |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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