Predictive Maintenance Model for Industrial Equipment

In industries, unforeseen machine failures result in substantial losses due to extended downtime, expensive repairs, and reduced productivity. Existing predictive maintenance methods often fall short due to their reactivity, lack of proactive capabilities, and poor prediction accuracy. To address this challenge, this paper presents a trained classification model that combines logistic regression and random forest algorithms. Our model's effectiveness is proven through extensive experiments, outperforming established techniques in our evaluation, the Logistic Regression model achieved an impressive 97.08% accuracy using a 0.5 decision threshold on the training dataset. Furthermore, we explore a random forest model, which attains a remarkable 98.52% accuracy and solidifies its status as a robust predictive maintenance tool. Our approach has the potential to minimize downtime, and enhance operational efficiency, making it invaluable for predictive maintenance across diverse industries.