Decision Framework for Predictive Maintenance

This chapter delves into the application of machine learning models for predictive maintenance, with a particular emphasis on estimating the Remaining Useful Life (RUL) of industrial equipment. The study employs the C-MAPSS dataset, a recognized benchmark in predictive maintenance research, to evaluate the performance of various regression and classification algorithms. Key topics include the comparative analysis of regression models such as Linear Regression, Support Vector Regression (SVR), and Random Forest Regression, as well as classification models like Support Vector Classification (SVC), Random Forest, Naïve Bayes, and K-Nearest Neighbors (K-NN). The chapter highlights the strengths and limitations of each model, providing insights into their suitability for different predictive tasks. The results demonstrate that SVR offers the best generalization capability among regression models, while SVC achieves the highest accuracy in classification tasks. The study concludes by underscoring the importance of selecting appropriate machine learning models based on the nature of the predictive task, whether it be precise numerical estimates of RUL or interpretable risk assessments for real-time decision-making. The findings suggest that future research could explore hybrid approaches integrating regression and classification techniques to further enhance predictive maintenance strategies.