An Intelligent Self-Validated Sensor System Using Neural Network Technologies and Fuzzy Logic Under Operating Implementation Conditions

Abstract

This article presents an intelligent self-validated sensor system developed for dynamic objects and based on the intelligent sensor concept, which ensures autonomous data collection and real-time analysis while adapting to changing conditions and compensating for errors. The research’s scientific merit is that an intelligent self-validated sensor for dynamic objects has been developed that integrates adaptive correction algorithms, fuzzy logic, and neural networks to improve the sensors’ accuracy and reliability under changing operating conditions. The proposed intelligent self-validated sensor system provides real-time error compensation, long-term stability, and effective fault diagnostics. Analytical equations are described, considering corrections related to influencing factors, temporal drift, and calibration characteristics, significantly enhancing measurement accuracy and reliability. The fuzzy logic application allows for refining the scaling coefficient that adjusts the relationship between the measured parameter and influencing factors, utilizing fuzzy inference algorithms. Additionally, monitoring and diagnostics implementation for sensor states through LSTM networks enable effective fault detection. Computational experiments on the TV3-117 engine demonstrated high data-restoring accuracy during forced interruptions, reaching 99.5%. A comparative analysis with alternative approaches confirmed the advantages of using LSTM (Long Short-Term Memory) neural networks in improving measurement quality.