Helicopter turboshaft engines combustion chamber monitoring neural network method - Vladov, Serhii; Bulakh, Maryna; Baranovskyi, Denys; Sokurenko, Valerii; Muzychuk, Oleksandr; Vysotska, Victoria (Measurement : journal of the International Measurement Confederation, 2025)
•Helicopter turboshaft engines’ combustion chamber monitoring neural network method.•ANFIS neuro-fuzzy network with Sugeno-Takagi inference modification was developed.•The proposed method high accuracy is confirmed by the T-patterns absence.•A model confirms fuel combustion completeness as a diagnostic criterion for defects. The article proposes an innovative method for helicopter turboshaft engines’ combustion chamber monitoring based on a neural network, significantly impacting the aircraft engines’ diagnostics and monitoring subject area. This article’s main contribution is to improve defect detections’ accuracy and timeliness, which in turn contributes to the aircraft equipment operations’ reliability and safety. The developed method, leveraging an enhanced adaptive neuro-fuzzy inference system (ANFIS) neuro-fuzzy network with Sugeno-Takagi inference, significantly improves defect detection accuracy and reduces diagnostic errors, thereby enhancing the reliability and safety of flight operations. To implement it, the combustion chamber mathematical model was created based on the heat balance equation, which showed that the fuel combustion completeness coefficient is a diagnostic criterion for defects. It is mathematically substantiated that this coefficient determines combustion efficiency and directly affects thermal energy. The ANFIS with Sugeno-Takagi fuzzy inference architecture has been improved, making it possible to achieve the defects diagnosis and prediction accuracy of 99.65 %. The ANFIS neuro-fuzzy network with Sugeno-Takagi inference proposed in this work improves quality metrics for determining helicopter turboshaft engines’ combustion chamber defects, enhancing the fuel combustion completeness coefficient by 1.01 to 4.64 times. Experimental results show that optimal network training with a loss of 0.35 % is achieved in 60 epochs, which is 2.0 to 9.5 times faster than traditional algorithms (genetic algorithm, traditional backpropagation algorithm, traditional inverse gradient descending method, modified inverse gradient descending method, hybrid algorithm). The absence of hidden T-patterns in the fuel combustion completeness coefficient dynamics (the value does not exceed 10–4) is also substantiated, confirming the proposed method’s high accuracy. The accumulated heat experimental surfaces’ dependences on the dynamics and fuel consumption were obtained, which makes it possible to analyze the defects’ influence on the combustion chamber’s thermal characteristics. The proposed ANFIS network use reduces the 1st and 2nd types’ errors by 1.44…6.15 times when determining combustion chamber defects compared to other architectures and classical methods, such as the least squares method.