Helicopter Turboshaft Engine Residual Life Determination by Neural Network Method
Abstract
:1. Introduction
1.1. Relevance of the Research
1.2. State-of-the-Art
1.3. Main Attributes of the Research
- Development of a mathematical model for helicopter TE residual life determination.
- Development of a neural network model for helicopter TE residual life determination.
- Development of a neural network model training algorithm for helicopter TE residual life determination.
- Conducting a computational experiment to determine helicopter TE residual life (using the example of determining TE compressor turbine blade residual life).
- Conducting a comparative analysis of the results obtained for helicopter TE residual life determination with those obtained by classical methods, based on classical statistical methods for experimental data processing (for example, the least squares method).
2. Materials and Methods
- The flexibility of including new factors (parameters) in the model allows you to integrate new factors that influence the operational status of the helicopter TE components using the function fi(Si). This makes it possible to take into account component degradation from various aspects, such as blade wear, compressor efficiency loss, and others, as they are researched and their impact on engine life is identified.
- The adaptability to changes in operating conditions indicates that the ΔTi coefficients in the model are adjusted for changes in operating conditions. For example, if the maintenance or operating conditions change, the factors may be revised to more accurately account for these factors.
- Neural networks allow you to customize a wear model for the specific characteristics of each engine.
- With increasing operating time, neural networks adjust parameter changes in the model until the limit value is reached, which makes it possible to adapt to the changing wear rate of components.
3. Results and Discussion
- The mean square error, as a model adequacy criterion, is a standard metric for assessing the predicted quality and models.
- Expression (19) takes into account degradation data at all available time points, which provides an overall model adequacy overview throughout its operation.
- Averaging the error over the entire sample allowed us to assess the overall adequacy of the model throughout the operation’s entire period.
- The model predicts the mean square error in real data and identifies deviations, which allows you to quickly detect and correct inaccuracies and inconsistencies.
- Expression (19) is a simple and understandable way to assess the model’s adequacy, which makes it convenient for use by engineers and equipment maintenance specialists.
- Zero expected value means that the average of all noise values is zero.
- Standard deviation σi = 0.025 means that it characterizes the spread of noise values relative to the average value. In this case, the standard deviation was 0.025, which indicated a small spread of values.
- A uniform distribution of the power spectral density means that the noise energy is equally distributed across all frequencies.
- Neural networks solve the helicopter TE compressor turbine blade residual service life determination task more accurately than traditional methods: the identification error at the output of the developed multilayer perceptron was 4.81 times lower than that of the regression model obtained using LSM.
- The error of solving the helicopter TE compressor turbine blade residual life determination task using the developed multilayer perceptron did not exceed 0.424%; for the classical RBF network, it was 1.079%, while for the LSM, it was 2.038%.
- Neural network methods are more robust to external disturbances: for a noise level σi = 0.025, the error in solving the helicopter TE compressor turbine blade residual life determination task when using the developed multilayer perceptron increased from 0.424 to 0.611%; for the classical RBF network—from 1.079 to 1.877%, and for the LSM—from 2.038 to 3.933%.
- The relevance of the helicopter turboshaft engine residual life determination method is substantiated, which lies in its critical role in ensuring flight reliability and safety, since timely assessment of engine conditions, taking into account many operating factors and environmental conditions, makes it possible to plan maintenance and component replacement, preventing emergencies, and increasing the resource prediction accuracy thanks to modern diagnostic systems.
- A method for determining the residual life of helicopter turboshaft engines has been developed based on a hierarchical system utilizing neural network technologies. The experimental results showed that using a multilayer perceptron within this hierarchical system yielded a maximum root-mean-square error of no more than 0.424 when applied to solving the task of estimating the residual life of the compressor turbine blades of helicopter turboshaft engines.
- Based on the backpropagation algorithm, a multilayer perceptron training algorithm has been developed, which, by introducing the initial parameter x0 to the output layer, improved the helicopter turboshaft engine residual life prediction accuracy and use of the adaptive Adam training rate provided high accuracy (up to 99.3%) in solving the helicopter turboshaft engine compressor turbine blade residual life determination task. It has been experimentally proven that use of the developed multilayer perceptron training algorithm made it possible, with 160 training epochs, to ensure an accuracy of 99.3% and reduce losses to 0.5% in solving the helicopter turboshaft engine compressor turbine blade residual life determination task.
- Based on the helicopter turboshaft engine compressor turbine blade residual life assessment through parameter prediction and similarities with patterns from the past, a method for constructing a degradation curve has been developed. This method integrates parameter prediction and historical data analogies to enhance the accuracy of service life prediction and maintenance planning, thereby mitigating failures. Experimental validation showed that the mean square error between predicted and observed residual resource over the entire operational period did not exceed 0.0058 (0.58%), approaching zero, indicating the high adequacy of the constructed degradation curve.
- The results of solving the helicopter turboshaft engine compressor turbine blade residual life determination task using the developed multilayer perceptron as a hierarchical system were compared with the classical RBF network and the least squares method, which made it possible to reduce the first and second types of errors by 2.23 times compared with the use of the classical RBF networks, and by 4.74 times compared to using the least squares method.
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Number | 1 | 2 | … | 72 | … | 133 | … | 205 | … | 256 |
---|---|---|---|---|---|---|---|---|---|---|
Ti value | 0.973 | 0.962 | … | 0.936 | … | 0.951 | … | 0.925 | … | 0.973 |
Metrics | Developed Multilayer Perceptron | Classical RBF Network | Least Squares Method |
---|---|---|---|
MSE | 0.611 | 1.877 | 3.933 |
MAE | 0.781 | 1.369 | 1.983 |
RMSE | 0.781 | 1.369 | 1.983 |
MAPE | 1.03% | 2.05% | 4.84% |
R2 | 0.993 | 0.971 | 0.837 |
ME | 0.053 | 0.116 | 0.634 |
MedAE | 0.068 | 0.132 | 0.311 |
sMAPE | 1.03% | 2.05% | 4.80% |
GMSE | 0.134 | 0.258 | 0.612 |
r | 0.997 | 0.986 | 0.928 |
RMSLE | 0.057 | 0.113 | 0.264 |
Hit Rate | 70.3% | 36.7% | 17.2% |
Max Error | 0.0385 | 0.0616 | 0.132 |
Huber Loss | 4.17 × 10−5 | 0.000118 | 0.000334 |
Metrics | Improvements When Using the Developed Multilayer Perceptron | |
---|---|---|
Classical RBF Network | Least Squares Method | |
MSE | 3.07 | 6.44 |
MAE | 1.75 | 2.54 |
RMSE | 1.75 | 2.54 |
MAPE | 2.00 | 4.70 |
ME | 2.19 | 12.0 |
MedAE | 1.94 | 4.60 |
sMAPE | 2.00 | 4.70 |
GMSE | 1.92 | 4.60 |
RMSLE | 2.00 | 4.60 |
Hit Rate | 1.92 | 4.10 |
Max Error | 1.60 | 3.40 |
Huber Loss | 2.80 | 8.00 |
Error Type | Developed Multilayer Perceptron | Classical RBF Network | Least Squares Method |
---|---|---|---|
Type I error, % | 0.754 | 1.681 | 3.574 |
Type II error, % | 0.447 | 1.063 | 2.119 |
Actual\ Predicted | Developed Multilayer Perceptron | Classical RBF Network | Least Squares Method |
---|---|---|---|
True Positives | 97 | 3 | 0 |
True Negatives | 3 | 88 | 2 |
False Positives | 1 | 6 | 8 |
False Negatives | 0 | 3 | 92 |
Actual\ Predicted | Developed Multilayer Perceptron | Classical RBF Network | Least Squares Method |
---|---|---|---|
True Positives | 96 | 89 | 0 |
True Negatives | 2 | 8 | 10 |
False Positives | 275 | 260 | 290 |
False Negatives | 25 | 41 | 100 |
TPR | 0.79 | 0.64 | 0 |
FPR | 0.010 | 0.029 | 0.31 |
AUC | 0.862 | 0.717 | 0.295 |
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Vladov, S.; Kovtun, V.; Sokurenko, V.; Muzychuk, O.; Vysotska, V. Helicopter Turboshaft Engine Residual Life Determination by Neural Network Method. Electronics 2024, 13, 2952. https://doi.org/10.3390/electronics13152952
Vladov S, Kovtun V, Sokurenko V, Muzychuk O, Vysotska V. Helicopter Turboshaft Engine Residual Life Determination by Neural Network Method. Electronics. 2024; 13(15):2952. https://doi.org/10.3390/electronics13152952
Chicago/Turabian StyleVladov, Serhii, Viacheslav Kovtun, Valerii Sokurenko, Oleksandr Muzychuk, and Victoria Vysotska. 2024. "Helicopter Turboshaft Engine Residual Life Determination by Neural Network Method" Electronics 13, no. 15: 2952. https://doi.org/10.3390/electronics13152952