Failure Prediction of Aircraft Equipment Using Machine Learning with a Hybrid Data Preparation Method
| Autor: | Harun Uguz, Onur Inan, Kadir Celikmih |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Correlation coefficient
Artificial neural network Mean squared error Article Subject Computer science business.industry Aviation 020206 networking & telecommunications Feature selection 02 engineering and technology Machine learning computer.software_genre Computer Science Applications Support vector machine QA76.75-76.765 Multilayer perceptron Linear regression 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Computer software business computer Software |
| Zdroj: | Scientific Programming, Vol 2020 (2020) |
| ISSN: | 1058-9244 |
| DOI: | 10.1155/2020/8616039 |
| Popis: | There is a large amount of information and maintenance data in the aviation industry that could be used to obtain meaningful results in forecasting future actions. This study aims to introduce machine learning models based on feature selection and data elimination to predict failures of aircraft systems. Maintenance and failure data for aircraft equipment across a period of two years were collected, and nine input and one output variables were meticulously identified. A hybrid data preparation model is proposed to improve the success of failure count prediction in two stages. In the first stage, ReliefF, a feature selection method for attribute evaluation, is used to find the most effective and ineffective parameters. In the second stage, a K-means algorithm is modified to eliminate noisy or inconsistent data. Performance of the hybrid data preparation model on the maintenance dataset of the equipment is evaluated by Multilayer Perceptron (MLP) as Artificial Neural network (ANN), Support Vector Regression (SVR), and Linear Regression (LR) as machine learning algorithms. Moreover, performance criteria such as the Correlation Coefficient (CC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) are used to evaluate the models. The results indicate that the hybrid data preparation model is successful in predicting the failure count of the equipment. |
| Databáze: | OpenAIRE |
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