Abstract
Heat exchangers are critical components of the environmental control system (ECS) of an aircraft. The ECS regulates temperature, pressure, and humidity of the cabin air. Fouling of the heat exchangers in an ECS may occur due to the deposition of external substances (e.g., debris) on the fins that obstruct the air flow, which increases the pressure drop across the heat exchanger and degrades its efficiency. Fouling is a critical issue, because it necessitates time consuming, periodic, and expensive maintenance. In this regard, this paper presents a two step process for fouling diagnosis of the heat exchanger: 1) optimal sensor set selection that contains the most relevant information for fault classification and 2) robust data analysis and sensor fusion in the presence of various uncertainties for the inference of fouling severity via different machine learning tools. This process of heat exchanger fouling diagnosis is implemented and tested on the data generated from an experimentally validated high-fidelity Simulink model of the ECS provided by an industry partner.
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