Predictive maintenance is essential to achieve reliable autonomous mining, avoiding unplanned stops of mining machinery such as wheel loaders and drills.
The difficult environment for mining machinery, e.g., for drilling equipment, not only makes life-time for components short compared to, e.g., personal car components but it also makes it difficult to equip the machines with reliable high accuracy sensors. This means that methods must be robust and rely on available sensor data from sensors not necessarily placed near the critical component. To reach the project’s research objectives, availability of data is therefore key and ambitious data collection campaigns on multiple mines in different countries for extended amount of time has been initiated. This is also complemented with planned experiments on research drill rigs using experimental and new sensor technology.
Detailed physical models of machines are not expected to be available, mainly due to the difficult operating conditions and the low-volume of machines. Therefore, a main research question is the development of methods for fusing coarse maintenance information and operational data, high-resolution and high-frequency on-board measurements, and basic physical models. This process requires development of techniques in signal processing and machine learning together with statistical models and techniques from survival and reliability analysis.
A fundamental research question for the project is thus how all available data and physical models is to be used in order to facilitate individual-based predictive maintenance, and in particular for mining products.