Development of an Intelligent System for Enhanced Maintenance of Automobiles using Machine Learning

In the ever-changing automobile industry, sustainable practices are of utmost importance. It is essential to perform preventative maintenance to accomplish sustainability objectives. Vehicles will have fewer unanticipated breakdowns and last longer as a result. This study introduces a machine learning model that has been optimized to enhance preventive maintenance operations in the automotive sector. To improve the efficacy of predictive maintenance in intelligent manufacturing systems, this paper presents an improved AdaBoost algorithm linked with big data analytics. First, in the proposed framework, massive datasets are gathered and preprocessed from sensors, IoT devices, and other sources in the industrial setting. Then, the meta-algorithm AdaBoost is used to improve the efficiency of subpar learners, allowing for reliable failure and deterioration prediction in machinery. Adjusting hyperparameters like the number of iterations and the learning rate is part of the algorithmic optimization process to strike a good balance between model accuracy and computational efficiency. The proposed model gains an accuracy level of 0.972 value, Precision level of 0.977 value, Recall level of 0.972 value and F1-score level of 0.974 value. By analyzing historical data, our algorithm can predict when problems will occur, enabling us to take quick action and minimize downtime. Improved maintenance scheduling and reduced environmental effects are outcomes of the proposed model’s use of cutting-edge optimization techniques, which boost the model’s predictive capabilities. The model achieves better results than the state-of-the-art methods in extensive trials conducted on a dataset from a leading automaker. It achieves significant improvements in maintenance efficiency and prediction accuracy. Sustainability in the automobile sector is a wider purpose of this study, which proposes a data-driven plan for maintenance that is strong and in line with economic and environmental aims.