# The Method of Restoring Lost Information from Sensors Based on Auto-Associative Neural Networks
###### Abstract  
The research aims to develop a neural network-based lost information restoration method when the complex nonlinear technical object (using the example of helicopter turboshaft engines) sensors fail during operation. The basis of the research is an auto-associative neural network (autoencoder), which makes it possible to restore lost information due to the sensor failure with an accuracy of more than 99%. An auto-associative neural network (autoencoder)-modified training method is proposed. It uses regularization coefficients that consist of the loss function to create a more stable and common model. It works well on the training sample of data and can produce good results on new data. Also, it reduces its overtraining risk when it adapts too much to the training data sample and loses its ability to generalize new data. This is especially important for small amounts of data or complex models. It has been determined based on the computational experiment results (the example of the TV3-117 turboshaft engine) that lost information restoration based on an auto-associative neural network provides a data restoring error of no more than 0.45% in the case of single failures and no more than 0.6% in case of double failures of the engine parameter registration sensor event. associative neural network; autoencoder; regularization coefficients; helicopter turboshaft engines; restoring; lost information restoration; error; training +
Footnote †: journal:resistance to failures. This is ensured during the failure state detection, failure localization, and operability restoration. One of the key tasks of a complex nonlinear technical project operation management is the parametric failure detection of sensors (degradation of their characteristics). In this case, as a rule, the majority control method can be used [7]. It consists of the fact that if one of the sensors has a characteristic different from the reference one, the additional measurement channel introduction in the mathematical model form allows for calculating the average value of this measurement (median) and, thereby, restoring the information lost from the sensor. The solution to the information restoration task using a neural network model of the engine is decomposed into two sub-problems: the mathematical model identification problem and the information restoration problem. The solution to the information restoration problem using neural network technologies (given in [7]) is directly related to the sensor failure detection and the lost information restoration based on neural network methods and engineering techniques through the following implementation steps: statistical data processing (preprocessing stage); choosing the architecture of the neural network and its structure; the neural network training algorithm selection; neural network training; neural network testing; and the neural network work efficiency evaluation [8]. One of the neural network methods to solve the lost information restoration problem is based on a neural network as a mathematical model of a complex nonlinear technical object (for example, helicopter TE), obtained during its characteristics identification process. In [9; 10], the lost information restoration possibility from aircraft gas turbine engine (GTE) sensors is stated, but at the same time, there is no method based on which the lost data are restored. In [11], a technique for the lost information restoration process from GTE sensors as part of the onboard system for monitoring and diagnosing its operational status was proposed and software was created. The analysis shows that the task range that needs to be solved is constantly growing. This is explained by the constant improvement of complex nonlinear technical objects (including helicopter TE) and, as a result, the functions they perform, as well as the corresponding increase in the number of controlled and diagnosed parameters [12].