Machine learning-based fault prediction is one of the most promising approaches to enhancing the reliability, safety and operational efficiency of modern industrial systems. The rapid development of Internet of Things (IoT) technologies means that industrial environments now generate large volumes of high-frequency sensor data. This enables data-driven techniques to identify abnormal behavioral patterns before critical failures occur. This chapter introduces a comprehensive framework for fault prediction using turbofan engine sensor datasets, focusing on the widely adopted NASA CMAPSS benchmark. The proposed methodology integrates data preprocessing, exploratory analysis, feature engineering and supervised machine learning models to predict degradation states and remaining useful life (RUL).

The chapter begins with a detailed overview of the CMAPSS dataset, highlighting its operational settings, multivariate time-series nature and fault progression characteristics. Several pre-processing steps including normalization, noise reduction, outlier inspection and temporal feature extraction are performed to prepare the data for modeling. Domain-specific statistical features such as rolling mean, standard deviation, RMS, kurtosis and skewness are computed to capture degradation trends in sensor measurements. Machine learning models including Random Forest, Gradient Boosting (XGBoost), Support Vector Machines and Artificial Neural Networks are trained and evaluated under consistent experimental settings.

Model performance is assessed using standard metrics such as accuracy, precision, recall, F1-score, ROC-AUC and confusion matrices. Results confirm that ensemble learning methods particularly Random Forest and XGBoost achieve high predictive accuracy and robustness in identifying degradation states. The analysis demonstrates that data-driven fault prediction can significantly reduce unexpected downtime and facilitate proactive decision-making in industrial operations. The chapter concludes with practical implications, limitations and future research directions, emphasizing opportunities for integrating deep learning and real-time IoT-based predictive maintenance systems.