Авіаційно-космічна техніка та технологія (Aug 2025)
Definition of requirements and stepwise development of a diagnostic model algorithm for the TV3-117V gas turbine engine
Abstract
This study presents an approach to the formalized construction of a diagnostic model for the TV3-117V gas turbine engine, aimed at post-flight analysis of operational data. The model is designed to detect potentially hazardous deviations in engine performance based on the comparison of actual parameters with reference values, considering the operating mode. The relevance of developing a dedicated post-flight diagnostic tool is substantiated by considering the specific features of the TV3-117V engine and limitations of existing approaches, which are mostly empirical or statistical and lack structured mode-dependent logic. A set of diagnostically significant parameters (exhaust gas temperature, compressor shaft speed, torque, fuel consumption, and logical signals) is defined and divided into three functional groups: mode-identifying, diagnostic, and auxiliary. A modular structure of the diagnostic model, comprising blocks for preprocessing, mode identification, parameter normalization, reference characteristics, normative comparison, and logical inference generation, is proposed. The mode identification algorithm is based on a combination of logical indicators and key parameter threshold values to detect engine operation in one of five defined modes: ground idle, cruise, takeoff, 2.5-minute power, and preservation/depreservation. Formulating criteria for identifying potentially dangerous deviations is given special attention. Accounting for not only absolute deviations but also inter-parameter relationships and dynamic trends enhances diagnostic reliability. Using the "ground idle" mode as an example, a set of criteria is implemented to detect temperature, torque, and rotor speed instability. The justification for using dedicated templates of reference behavior for each operational mode to enable context-sensitive comparison is provided. The proposed diagnostic model structure lays the groundwork for the further development of an automated expert system for analyzing post-flight engine data.
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