006: Confidence Modeling for Flight Mode Predictions

Different phases of flight have different engine operating conditions, temperatures, and performance requirements. Identifying the flight phase is critical for health monitoring, fault detection, and deterioration calculations. To be robust to measurement fidelity, signal variation, and different engine types the objective is to identify flight phase using only the rotational shafts speeds. The current study uses two shaft speeds generated from historic engine development tests and classifies flight phase into four main modes (Taxi, Cruise, Idle, and Other) by using several machine learning methods, LSTM, KNN, and SVM. By comparing evaluation criteria such as Accuracy, Precision, Recall, F1-Score, and AUC Score among these three methods, KNN has been selected as the most successful method in this study. Also, this paper investigates the confidence score for the classification results by using a recently proposed confidence modeling technique, MACEst [1]. Moreover, the sensitivity analysis is conducted to determine specific thresholds for for the low, medium, and high level of confidence for the estimated confidence scores obtained from the MACEst method. This phase identification process is beneficial for engine fault, performance, and maintenance analytics.

Flight Phase

Machine Learning

Prediction Models

Confidence Models 

Implementation and Analysis

Conference on Statistical Practice (CSP) 2023