Comparative Analysis of Algorithms of Machine Learning for Predicting Pre-Failure and Failure States of Aircraft Engines
| Autor: | S. S. Abdurakipov, E. B. Butakov |
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| Rok vydání: | 2020 |
| Předmět: |
010302 applied physics
Artificial neural network Computer science business.industry Decision tree Condensed Matter Physics Machine learning computer.software_genre 01 natural sciences Convolutional neural network Autoencoder Predictive maintenance 010309 optics Multiclass classification 0103 physical sciences Unsupervised learning Artificial intelligence Electrical and Electronic Engineering business Instrumentation computer Analysis of algorithms |
| Zdroj: | Optoelectronics, Instrumentation and Data Processing. 56:586-597 |
| ISSN: | 1934-7944 8756-6990 |
| DOI: | 10.3103/s8756699020060023 |
| Popis: | The developed classical machine learning models based on linear models and decision trees, the modern algorithms of convolutional neural networks, and the neural network autoencoder are compared in solving the problem of predictive detection of pre-failure and failure states of aircraft engines. The NASA data set includes the sensor readings reflecting the life cycle of aircraft engines. Several problem formulations are investigated in the study: (i) the problem of binary and multiclass classification, where the normal, pre-failure, and failure states of aircraft engines are predicted; (ii) the regression problem intended to predict the accurate number of working cycles to engine failure; and (iii) the unsupervised learning, where the neural network autoencoder is applied to detect abnormal cycles of aircraft engine operation. The obtained algorithms are combined in a framework useful in analyzing a wide spectrum of data of predictive maintenance. |
| Databáze: | OpenAIRE |
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