The Environmental Control System (ECS) of an aircraft provides thermal and pressure control of the engine bleed air for comfort of the crew members and passengers onboard. For safe and reliable operation of the ECS under complex operating environments, it is critical to detect and diagnose performance degradations in the system during early phases of fault evolution. One of the critical components of the ECS is the heat exchanger, which ensures proper cooling of the engine bleed air. This paper presents a wavelet-based fouling diagnosis approach for the heat exchanger. The approach is composed of three main steps: i) Feature extraction from the sensor data, where the data is preprocessed using wavelets for improving the information content of the features for fouling classification, ii) Pattern classification, where a classification algorithm is trained from the faulty and nominal datasets, and iii) Classifier evaluation, where the trained classifier is evaluated to determine the Correct Classification Rate (CCR). The fouling diagnosis process is built and tested with the sensor data generated from an experimentally validated ECS model provided by our industry partner.