Hitachi Construction Machinery and TU Delft join forces on mining research project

The Delft University of Technology in The Netherlands and Hitachi Construction Machinery (Europe) NV (HCME) have agreed to collaborate for a vital research project on the maintenance of mining equipment. The globally renowned manufacturer of ultra-large excavators and dump trucks has agreed to share valuable data and expertise with the largest Dutch public technical university during a two-year study.

The aim of the research is to identify and predict the remaining life of critical components on mining machines. It will help engineers plan maintenance before parts need replacing to improve the availability, reliability and safety of dump trucks that operate in some of the world’s toughest environments. It will also help to significantly reduce operational downtime and life-cycle costs.

As part of the collaboration, the HCME digital solutions team for mining operations is sharing complex data gathered from its machines working on site. Their key components are equipped with sensors, which allows for detailed information on indicators, such as temperatures and pressures, to be collated and analysed.

The research is led by Malihe Goli, a Control and Automation Engineer, and PhD candidate at the Geo-Resources Section of TU Delft’s Department of Geoscience and Engineering. The project is jointly supervised by this section and the Intelligent Sustainable Prognostics Group within the Faculty of Aerospace Engineering.

Predictive maintenance strategies

Ms Goli aims to build a robust model that will capture degradation trends in components such as pumps, cylinders and brakes. The condition monitoring data supplied by HCME will enable her to improve the model and provide more accurate estimates of when a component might fail. This information can then be used to inform predictive maintenance strategies.

Daan van Berkel, Manager Mining Projects and Sustainable Mining for HCME, explains the implications of the study: “We will be able to plan when a truck needs to come into the workshop more precisely, and order any parts that may be required ahead of time. Moreover, addressing potential problems before they occur reduces the risk of a major issue that could also damage other parts and put a machine out of action for weeks.”

According to Ms Goli, the support from HCME has been fundamental to the progress of this research: “Access to large-scale, real-world datasets – including detailed failure records, maintenance logs, and sensor measurements – has enabled the development of accurate , data-driven models for component degradation.”

In addition to data, the HCME digital solutions team is also sharing its collective expertise with Ms Goli and her colleagues at TU Delft. “I sincerely appreciate their ongoing collaboration and the valuable technical insights they share into component behaviour, which have been instrumental in guiding model development and interpretation,” she adds.