Abstract
The subject matter of the article is TV3-117 aircraft engine and methods for monitoring and diagnosing its technical condition. The goal of the work is development of fault-tolerant algorithms for identification of the onboard mathematical model of the TV3-117 aircraft engine as part of its automatic control system in flight modes. The following tasks were solved in the article: recovering of lost information by an auto-associative neural network in case of a single sensor failure, recovering of lost information by an «optimal» auto-associative neural network in case of single sensor failures of the on-board control and diagnostic system, recovering of lost information by an auto-associative neural network and an on-board control and diagnostic system from the gas temperature registration sensor in front of the turbine compressor in case of failure. The following methods used are – substantiation of expediency of Kalman-filtration use in TV3-117 aircraft engine automatic control system, determination of Kalman filter transfer function, determination of algorithm of detection and localization of channel failure of two-channel sensor, determination of frequency characteristics of TV3-117 aircraft engine automatic control system, proof of equality of unit coefficient fault-tolerant filter unit . Conclusions: The frequency properties of the automatic control system of the aircraft engine TV3-117 are investigated, the equality of the unit of the gain of the developed fault-tolerant filtering unit and the absence of additional phase shifts introduced by the possible net delay due to the peculiarities of the implemented algorithms mathematical model. The absence of their influence on the stability of aircraft engine TV3-117 automatic control system is proved. Approbation of the developed algorithms showed that the average relative error in dynamics does not exceed 0.15 %, and in statics at the maximum expense – decreases to 0.01 % that corresponds to modern requirements of accuracy of algorithms of identification on a contour of dosing needle . Keywords: aircraft engine, neural network, Kalman filtering, failure detection and localization
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