# Neural Network System for Predicting Anomalous Data in Applied Sensor Systems
## 4 Discussion  
This article represents a continuation in the research field of the intelligent monitoring and operational control of helicopter TEs under onboard conditions. The proposed neural network system for anomaly prediction can function as a neural network module within the helicopter TE monitoring and operation control onboard expert system. It is well-known that predicting the helicopter TE thermogasdynamics parameters based on data obtained from standard sensors during flight is the critical aspect of helicopter flight performance. The developed system allows for real-time data analysis and decision making based on the predicted parameters, thereby enhancing the reliability and efficiency of helicopter operations. Implementing this neural network system into helicopter TE monitoring and control onboard expert systems can significantly improve the fault diagnosis and prediction quality. By employing a modified LSTM network with a long short-term memory layer as the predictor, which also implements the reconstructor model, the developed system predicts the helicopter TE thermogasdynamics parameters with an accuracy of up to 97.9%. This allows the data cleaning module to accurately (98.2% accuracy achieved) predict normal values and detect outliers in the data. The mean squared error does not exceed 0.025 (2.5%). Although the achieved prediction accuracy of 97.9% is sufficient for asset maintenance planning, it may not be reliable enough for decision making in safety-critical operations, where higher precision and lower error margins are essential. Further refinement is needed to ensure the system's robustness in scenarios requiring stringent operational safety [59; 60] standards. The application of the anomaly detector model in the developed system enabled the detection of two anomalies in the TV3-117 engine gas temperature before the compressor turbine sensor readings during helicopter flight, based on two minutes of sensor activity. The first anomaly (\"Top 1 dissonance\") indicates a brief sensor malfunction, while the second (\"Top 10 dissonance\") reflects a sharp drop in gas temperature before the compressor turbine due to increased airflow speed and changing external pressure. The false positive rate does not exceed 1.12%, the false negative rate does not exceed 1.01%, and the anomaly detection time does not exceed 1.611 s, which highlights the high efficiency of the developed system. Figure 12: AUC-ROC curve for the developed neural network system (author’s research). The AUC-ROC value of 0.818 suggests that the developed system has a strong ability to differentiate between normal and anomalous data, making it a reliable tool for accurate anomaly detection. This means the system effectively minimizes both false alarms and missed anomalies, confirming its high performance in real-world operational conditions. The limitations related to the obtained results include the dependency on the accuracy in predicting and detecting anomalies, itself dependent on the quality of data provided by onboard sensors, which may be affected by technical malfunctions or data noise. The proposed system utilizes a modified LSTM network that demonstrates high accuracy (up to 97.9%); however, this accuracy may vary depending on helicopter operating conditions and sensor status. Additionally, the model requires significant computational resources for real-time data processing, which may limit its applicability in environments with restricted computational capacity. The prospects for further research in the field of helicopter TE intelligent monitoring and operational control include optimizing and adapting the proposed neural network system to work with a wide range of aircraft and operational conditions. Particular interest lies in integrating various data processing methods and improving the reconstruction and prediction algorithms, which will enhance anomaly detection accuracy and speed.