NEURAL NETWORK COMPUTER FOR RECOVERING LOST INFORMATION FROM STANDARD SENSORS OF THE ON-BOARD SYSTEM FOR CONTROL AND DIAGNOSTICS OF TV3-117 AIRCRAFT ENGINE

Abstract

The subject matter of the article is TV3-117 aircraft engine and methods for control and diagnostics its technical condition. The goal of the work is the development of a neural network computer for recovering lost information from standard sensors of the on-board control and diagnostics system of TV3-117 aircraft engine technical state in real time. 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 the lost information by an auto-associative neural network and an on-board control and diagnostic system from the gas temperature registration sensor before the turbine compressor in case of its failure. The following methods were used: methods of probability theory and mathematical statistics, methods of neuroinformatics, methods of the theory of information systems and data processing. The following results were obtained: The urgent task of recovering lost information from standard sensors in real time has been solved. Various computer architectures and recovery algorithms were investigated. An engineering technique for recovering lost information using a neurocomputer is proposed. As a result of the use of the neurocomputer, effective and high-quality information recovery from standard sensors was ensured under the conditions of the on-board control and diagnostics system of TV3-117 aircraft engine. Conclusions: The use of an auto-associative neural network in the on-board control and diagnostics system for information recovery makes it possible to ensure fault tolerance of the measuring channels of control systems, in particular, the TV3-117 aircraft engine. The main advantage of using neural networks as part of an on-board control and diagnostics system is the possibility of training and learning in real time, taking into account the individual characteristics of a particular engine. Information recovery in case of sensor failure using an auto-associative neural network provides data recovery error of no more than 0.45 % for single failures and not more than 0.6 % for double failures. At the same time, the time of one data recovery cycle is 1589.544 ns for the Raspberry Pi NanoPi M1 Plus calculator and 196.246 ns for the specialized Intel Neural Compute Stick 2 neuroprocessor, which meets the requirements of onboard implementation as part of an onboard control and diagnostic system.