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.

中文翻译：

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使用机器学习和混合数据准备方法对飞机设备进行故障预测

航空业中有大量信息和维护数据，可用于在预测未来行动时获得有意义的结果。本研究旨在引入基于特征选择和数据消除的机器学习模型来预测飞机系统的故障。收集了两年时间的飞机设备维护和故障数据，并精心确定了九个输入变量和一个输出变量。提出了一种混合数据准备模型，以提高两个阶段的故障计数预测的成功率。在第一阶段，ReliefF，一种用于属性评估的特征选择方法，用于寻找最有效和无效的参数。在第二阶段，修改 K-means 算法以消除噪声或不一致的数据。混合数据准备模型在设备维护数据集上的性能通过多层感知器 (MLP) 作为人工神经网络 (ANN)、支持向量回归 (SVR) 和线性回归 (LR) 作为机器学习算法进行评估。此外，使用相关系数 (CC)、平均绝对误差 (MAE) 和均方根误差 (RMSE) 等性能标准来评估模型。结果表明，混合数据准备模型成功地预测了设备的故障计数。平均绝对误差 (MAE) 和均方根误差 (RMSE) 用于评估模型。结果表明，混合数据准备模型成功地预测了设备的故障计数。平均绝对误差 (MAE) 和均方根误差 (RMSE) 用于评估模型。结果表明，混合数据准备模型成功地预测了设备的故障计数。