Predictive asset availability optimization for underground trucks and loaders in the mining industry

Abstract

With an increased focus on operational optimization, cost rationalization and increased advances in sensors, data storage and machine learning, mining Original Equipment Manufacturers are in need of intelligent machines to create an unassailable competitive advantage. To address this, Sandvik Mining and Rock Technology (Sandvik) and IBM have developed a customized service for fleet management and predictive maintenance for Sandvik’s mining equipment for the improvement of Overall Equipment Efficiency. In this endeavour, this paper aims to discuss the application of analytics to reduce machine downtime by predicting equipment failure in advance thus improving asset utilization of the fleet used in the mining industry. It details the journey of the analysis using the IoT data, data exploration, statistical approximations employed, various machine learning algorithms considered, and final selection of the techniques based on the industry recommended criteria. Predictive models for component failures (engine, brakes and transmission) and predictive models for equipment time to failure were built for underground mining trucks and loaders. With increased availability of additional sensor data and the need to interpret the outcomes that are actionable, supervised machine learning algorithms like decision trees were considered. Our work highlights various challenges encountered, the workarounds and solutions used to overcome them. The resulting models (built with IBM’s predictive analytics capability) of this work are augmented with Sandvik’s analytical offering, OptiMine® Analytics. This paper also highlights as to how our work has made a significant impact in financial terms and the client testimonials received.