Application of XGB-based metaheuristic techniques for prediction time-to-failure of mining machinery

Abstract

Mining equipment is a critical component in the success of mining operations, and unplanned downtime can be costly. Efficient and intelligent maintenance planning is therefore essential to minimize downtime and maximize productivity. Consecutive time-to-failure (TTF) is an important indicator of machine reliability, and accurate TTF predictions enable effective preventive maintenance planning. Therefore, this study proposes hybrid models of extreme gradient boosting (XGB), optimized by particle swarm optimization (PSO) and gray wolf optimization (GWO), for the prediction of TTF in mining machinery. Additionally, validation of the hybrid model was conducted using support vector regression (SVR) method. Historical data on mining machine failures were collected, and a case study was conducted to investigate shovels in an open-pit mine. The PSO-XGB method was found to be the most accurate predictor of failure time with R2 values of 0.99, RMSE values of 50.66 and 51.77, MAE values of 4.52 and 10.81, and AARE values of 1.15 and 1.24 in the training and testing phases. This research highlights the importance of efficient and intelligent maintenance planning to minimize downtime and optimize productivity in mining operations.

Keywords

• XGB-based hybrid model
• Failure time data
• extreme gradient boosting (XGB)
• particle swarm optimization (PSO)
• gray wolf optimization (GWO)
• Metaheuristic optimization