Evaluating the Performance of Machine Learning Algorithms in Predicting Industrial Equipment Maintenance Costs

Decision-making in the management of industrial machinery and equipment has always been of great importance, and proper maintenance strategies can significantly reduce operational costs. Selecting an effective maintenance approach requires a high level of accuracy, and one of the key factors contributing to this accuracy is the ability to estimate future maintenance and repair expenses. This study aims to present a precise and scientifically grounded method for predicting maintenance costs using the accuracy and computational efficiency of artificial intelligence. A polynomial regression model is employed as the predictive algorithm to estimate future maintenance expenditures. Finally, a related case study on forecasting maintenance costs for agricultural tractors is presented to demonstrate the applicability of the proposed approach.

کلیدواژه ها:

Machine learning ، predictive models ، Maintenance