Predictive Maintenance for Industrial Equipment in Big Data: An Implementation with Random Forest

This research explores the realm of predictive maintenance for industrial equipment, employing and comparing four prominent machine learning algorithms: “Random Forest, Support Vector Machines (SVM)”, Neural Networks, and Gradient Boosting. The study intends to offer information on their effectiveness regarding the ability to accurately predict failures of equipment and estimate maintenance time. Results indicate that Random Forest and Neural Networks emerge as top performers, exhibiting high precision (Random Forest: The only difference is in terms of precision (Random Forest: 0.65, Neural Networks: 0.72), recall (Random Forest: 0.8, Neural Networks was the best as opposed to other measures where Random Forrest performed well and Neurenl network did not over fit.). These algorithms also demonstrate superior accuracy in estimating maintenance times, as reflected in the Mean Absolute Error (Random Forest: 5.12, Neural Networks: 5.45). The comparative study can detect subtle strengths and weaknesses that are useful to practitioners in terms of algorithm selection according to specific industry needs. This study supports the continuous development of PMP, highlighting that choices in algorithms are essential to increase efficiency levels at the operation level, minimize downtime, and further encourage sustainable manufacturing.