Neural Network System for Predicting Anomalous Data in Applied Sensor Systems

Abstract

This article advances the research on the intelligent monitoring and control of helicopter turboshaft engines in onboard conditions. The proposed neural network system for anomaly prediction functions as a module within the helicopter turboshaft engine monitoring and control expert system. A SARIMAX-based preprocessor model was developed to determine autocorrelation and partial autocorrelation in training data, accounting for dynamic changes and external factors, achieving a prediction accuracy of up to 97.9%. A modified LSTM-based predictor model with Dropout and Dense layers predicted sensor data, with a tested error margin of 0.218% for predicting the TV3-117 aircraft engine gas temperature values before the compressor turbine during one minute of helicopter flight. A reconstructor model restored missing time series values and replaced outliers with synthetic values, achieving up to 98.73% accuracy. An anomaly detector model using the concept of dissonance successfully identified two anomalies: a sensor malfunction and a sharp temperature drop within two minutes of sensor activity, with type I and II errors below 1.12 and 1.01% and a detection time under 1.611 s. The system’s AUC-ROC value of 0.818 confirms its strong ability to differentiate between normal and anomalous data, ensuring reliable and accurate anomaly detection. The limitations involve the dependency on the quality of data from onboard sensors, affected by malfunctions or noise, with the LSTM network’s accuracy (up to 97.9%) varying with helicopter conditions, and the model’s high computational demand potentially limiting real-time use in resource-constrained environments.