Hitachi Construction and TU Delft collaborate on predictive maintenance for mining machinery

In a groundbreaking collaboration, Delft University of Technology (TU Delft) and Hitachi Construction Machinery (Europe) NV (HCME) have launched a two-year research project to transform mining equipment maintenance procedures.

Delft University of Technology in the Netherlands and Hitachi Construction Machinery (Europe) NV (HCME) have jointly decided to undertake an important research project on mining equipment maintenance.  The world’s leading maker of ultra-large excavators and dump trucks has agreed to share vital data and experience with the largest Dutch public technical institution for a two-year study.

The research aims to identify and predict the remaining life of crucial components on mining machines.  It will assist engineers in planning maintenance before parts need to be replaced, hence improving the availability, dependability, and safety of dump trucks operating in some of the world’s harshest conditions. It will also greatly reduce operational downtime and life-cycle expenses.

As part of the collaboration, the HCME digital solutions team for mining operations shares sophisticated data collected from on-site machines. Their major components are fitted with sensors, allowing precise data on indicators such as temperatures and pressures to be collected and studied.

Malihe Goli, a Control and Automation Engineer and PhD candidate at TU Delft’s Department of Geoscience and Engineering, is leading the project. This section collaborates with the Intelligent Sustainable Prognostics Group at the Faculty of Aerospace Engineering to supervise the project.

Predictive maintenance strategies

Ms. Goli intends to create a realistic model that would capture degradation trends in components such as pumps, cylinders, and brakes. The condition monitoring data provided by HCME will allow her to enhance the model and provide more accurate predictions of when a component will fail.  This data can then be utilised to develop predictive maintenance methods.

 Daan van Berkel, HCME’s Manager of Mining Projects and Sustainable Mining, outlines the study’s implications: “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.

For more information, visit: https://www.hitachicm.com/eu/en/