Mathematical Framework for Enhancing Machine Failure Prediction in Aviation and Beyond: Leveraging Deep Convolutional Neural Networks and Visual Analytic

Abstract

In industries such as aviation, accurate prediction of machine failures is essential to prevent costly and potentially hazardous breakdowns. Traditional methods rely on detailed degradation models of components, which are often not available, posing challenges to precise predictions. To overcome this limitation, the study proposes the use of deep convolutional neural networks (DCNN), sophisticated algorithms capable of learning from extensive datasets without the need for intricate degradation models. DCNNs employ a time window approach to discern significant patterns in data, eliminating the requirement for specialized knowledge in failure prediction and increasing their effectiveness across diverse scenarios. Evaluation using aircraft engine data illustrates that DCNNs surpass traditional methods in reliably predicting failures, representing a significant advancement in predictive maintenance. The study utilizes scatterplots and histograms to visually analyze the distribution of fault cycles among engine units. One scatter plot subplot displays each engine unit as a point, revealing patterns or trends in fault cycles. The other subplot features a histogram illustrating the frequency of fault cycles across all units, including statistical measures such as minimum, average, and maximum cycles. These visualizations provide a thorough understanding of fault cycle distribution, aiding in deeper analysis and interpretation of engine performance data. The performance evaluation compares several models: Linear Regression, Random Forest, Improved Linear Regression, Improved Random Forest, and the Cox Proportional Hazards model. Evaluating RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), and R² values for both training and test datasets reveals that the Improved Random Forest and Improved Linear Regression models demonstrate lower RMSE and MAE values and higher R² values. This indicates superior predictive accuracy and data fit. The Cox Proportional Hazards model offers unique insights into risk factors but may vary in performance depending on the dataset, emphasizing the importance of model refinement and tuning for precise predictions. This comprehensive approach underscores the potential of DCNNs and enhanced traditional models to advance predictive maintenance across industries.