# The Method of Restoring Lost Information from Sensors Based on Auto-Associative Neural Networks
## 4 Results
Figure 13: The lost information restoration result diagrams: (**a**) in the failure case of one of the regular sensors; (**b**) in a single failure case of sensors (their breaks) (authors’ research, based on [9; 10; 11; 50]). To improve efficiency and reduce the lost information restoring error, new methods are used, improved based on the [PERSON] and [PERSON] research [9; 10; 11]. In particular, the data from the sensors are constantly recorded in the buffer where the operating value is fixed when the sensor is active, and the restored value is used when it fails. The system automatically performs the following steps: receives output data from the sensors using the previous failed sensor parameter value from the buffer, sets the maximum number of calculations, initializes the calculation counter to 0, calculates the ANN outputs in the emulator, increments the calculation counter by 1, and transmits the management system result. At the same time, the proposed system envisages an additional neural network used to identify the complex dynamic objects' parameters. The proposed system is implemented as an improved functional scheme developed by [PERSON] and [PERSON] in [9; 10; 11] in which the lost information from the sensors is automatically restored. When a sensor failure is detected, the integrated tolerance control system uses ANN calculations to write to the buffer (stack memory) (Figure 14). If the sensor readings are outside the acceptable range, the tolerance control system declares the sensor as faulty and uses the last recorded value in the buffer to restore its value. Figure 15 shows the process diagram of restoring lost information from the ANN sensor of the TV3-117 TE (using the example sensor of the gas temperature in the compressor turbine front) based on the proposed (modernized) approach where the \"white line\" is the parameter value, the \"green line\" is the restored signal value using a neural network, and the \"red line\" is the parameter value obtained from the sensor. The information restoration error from the sensor in its interruption event and gradual failure does not exceed 0.25%. Figure 14: Improved functional diagram for the lost information restoring system (authors’ research, based on [9; 10; 11; 50]). According to Figures 13 and 15, the information restoration error from the sensor in its interruption case and gradual failure does not exceed 0.25%. Therefore, it is recommended to use the [PERSON] and Musluhov methodology [9; 10; 11] for the neural network development, training, and testing for lost information restoration from sensors. This technique includes the following stages: data preprocessing (normalization, calibration, digitization) for neural network training; statistical processing of data to identify gross measurement errors; data scaling; the neural network architecture and structure choosing; shaft size determination based on the covariance analysis results using the principal components method; activation functions selection, training algorithm; neural network training; testing; and the developed neural network effectiveness evaluation in the process of restoring lost information from sensors. The ANN information restoration results in the gas temperature sensor event in the compressor turbine front of the TV3-117 TE in its failure event are shown in Figure 7:0  
If other sensors fail, the restoring error does not exceed the values shown in Figure 7:1 The obtained results analysis shows the high efficiency of ANN application in the information restoration process. Information restoration results in case of double (multiple) failures of \(n_{TC}\) and \(T_{G}^{*}\) sensors are shown in Figure 7:2  
Figure 7:3 The auto-associative neural network lost information restoration from the gas temperature sensor in the compressor turbine front for the TV3-117 turboshaft engine in the sensor failure event (authors’ research, based on [9; 10; 11; 50]).