Predictive Maintenance for Industrial Machinery Using Machine Learning Models

In this capstone case study project, the predictive maintenance system used AI4I 2020 dataset to develop machine learning solutions for industrial machinery through this project. The main purpose of this capstone case study project was to forecast machine breakdowns in advance, which results in minimized unexpected downtime as well as maintenance cost reduction. The dataset holds 10,000 records containing measurements from sensors and their parameters, including air temperature, along with process temperature, rotational speed and torque as well as tool wear results. Data preprocessing comprised several steps, which involved handling missing values, performing outlier detection and feature standardization followed by obtaining balanced classes through the use of SMOTE. This project used EDA (exploratory data analysis) as well as Principal Component Analysis (PCA) approaches on AI4I 2020 dataset and showed trends and minimize data dimensions. The prediction process involved building various machine learning models starting with Logistic Regression followed by Support Vector Machines (SVM) model then Neural Networks followed by Random Forest to achieve evaluation outcomes through metrics like accuracy and precision and recall and F1-score and ROC-AUC.

The Random Forest model emerged as the top performer with 98.2% accuracy rating and excellent scores throughout all metrics to become the primary tool for failure prediction. This capstone data science project determined that machine failure prediction primarily depended on tool wear measurements and torque applied and rotational speeds achieved during operation. The system allows organizations to manage maintenance activities in advance and thus increase operational uptime and extended equipment lifetime and improved overall business performance. The AI system used SHAP explainable methods for model interpretation purposes which served to assist executive decision-makers by enhancing their understanding of predictive failure risks. This project contributed to Industry 4.0 objectives through sensor analytics and automated maintenance systems; that indeed develop scalable data-based solutions for contemporary industrial needs.