Predictive maintenance of industrial equipment using machine-learning

Predictive maintenance has appeared as a vital strategy for enhancing the reliability and efficiency of industrial equipment by expecting failures before they occur. This approach uses machine learning algorithms – including Random Forest, Decision Trees, and KNN to analyze data from sensors checking various parameters like temperature, vibration, and pressure. By detecting patterns and trends in historical failure data, these models enable early detection of potential faults, reducing downtime and perfecting maintenance schedules. The use of machine learning also eases correct identification of critical factors influencing equipment degradation, allowing for more targeted maintenance interventions. In this study, we explore the application of Random Forest, Decision Trees, and KNN for predictive maintenance, comparing their performance in terms of prediction accuracy, computational efficiency, and robustness. Each algorithm offers unique strengths: Random Forest provides high accuracy and robustness against overfitting, Decision Trees deliver interpretability, and KNN excels in classifying complex, high-dimensional data. This research aims to prove how predictive maintenance can enhance operational efficiency, extend equipment lifespan, and minimize unexpected breakdowns in industrial settings through the implementation of these models.