Research on Air Conditioning Performance Monitoring and Trend Prediction of A320 Aircraft Based on Big Data Analysis

This paper compares the WQAR (Wireless Quick Access Recorder) data of nearly 2.6 million flights of an airline's A320 fleet in the past five years by TELEDYNE decoding software AirFASE and big data analysis platform EMS (Event Measurement System), explores the impact of single and multiple parameters on the key parameters TP (Pack outlet temperature) and COT (compressor outlet temperature) of aircraft air conditioning system performance. An abnormal data monitoring model based on dynamic time series data is proposed, and an air conditioning performance prediction health evaluation model is established. It enriches the content of predictive maintenance research based on data analysis, and widens the work scope and digital development field of aircraft maintenance.