Abstract
The monitoring of engineering systems is becoming more common place because of the increasing demands on reliability and safety. Being able to diagnose a fault has been facilitated by technology developments. This has resulted in the application of methods yielding an earlier detection and thus prompted mitigation of corrective measures. The level of maturity of monitoring systems varies across domain areas, with more nascent systems in newly emerging technologies, such as fuel cells. With the increasing complexity of systems comes the inclusion of more sensors, and for expedient on-line diagnosis utilizing the information from the most appropriate sensors is key to enabling excellent diagnostic resolution. In this paper, a novel sensor selection algorithm is proposed and its performance in polymer electrolyte membrane (PEM) fuel cell on-line diagnosis is investigated. In the selection procedure, both sensor sensitivities to various failure modes and corresponding fuel cell degradation rates are considered. The optimal sensors determined from the proposed algorithm are compared with previous sensor selection techniques, where results show that the proposed algorithm can provide more efficient sensor selection results using less computational time, which makes this method better applied in practical PEM fuel cell systems for on-line diagnostic tasks.
Highlights
The degree of automation in operation and monitoring of systems has increased drastically in the last few decades, fueled by increases in computer processing capability, monitoring hardware functionality and cost, and the drive for more reliable and safer systems
This paper proposes a novel sensor selection algorithm based on the fuel cell failure mode effects on system degradation, and investigates the performance of these selected sensors in on-line diagnosis of a practical polymer electrolyte membrane (PEM) fuel cell system
It should be mentioned that the same diagnostic process is used except that the kernel principal component analysis (KPCA) will only be applied to the optimal sensors measurements
Summary
The degree of automation in operation and monitoring of systems has increased drastically in the last few decades, fueled by increases in computer processing capability, monitoring hardware functionality and cost, and the drive for more reliable and safer systems. It is highly desirable to propose an effective sensor selection algorithm which can determine the optimal sensors with minimum computational cost, and provide reliable on-line diagnostic results for practical fuel cell applications.
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