An Ensemble Deep Learning Model for Vehicular Engine Health Prediction

In the realm of predictive maintenance for vehicular engines, the accurate assessment of health conditions plays a pivotal role in enhancing reliability and reducing operational costs. This project introduces an ensemble deep learning approach aimed at predicting engine health based on diverse machine learning algorithms. The ensemble comprises Decision Trees, Random Forest, K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Adaboost, and Logistic Regression, each contributing uniquely to the predictive model.

Among these algorithms, Random Forest emerged as the primary predictor, achieving an accuracy of 84% in health condition predictions. This ensemble strategy harnesses the strengths of individual models to mitigate weaknesses and enhance overall predictive performance. By combining the diverse predictions from multiple models, the ensemble leverages their collective wisdom, resulting in robust predictions that are resilient to varying data conditions and outliers.

The methodology involves preprocessing engine sensor data to extract relevant features, followed by training each model on historical data to capture patterns indicative of engine health states. Subsequently, the ensemble framework aggregates individual model outputs to provide a consolidated prediction, yielding superior accuracy compared to standalone algorithms.

This project contributes to advancing predictive maintenance practices in automotive engineering, offering a reliable framework for early detection of potential engine failures and proactive maintenance scheduling. The results underscore the efficacy of ensemble deep learning approaches in complex predictive tasks, demonstrating their applicability and effectiveness in real-world scenarios.

Keywords: Predictive maintenance, Ensemble learning, Vehicular health, Engine diagnostics,

Machine learning algorithms, Predictive modelling, Random Forest.