Predictive Maintenance of Mining Machines Using Advanced Data Analysis System Based on the Cloud Technology

Nowadays, mines become more and more innovative and computerized. The operational conditions are harsh and varying; therefore, appropriate and powerful tools have to be applied. Typical mines possess huge infrastructure, which consists of various types of machines and devices, i.e. roadheaders, load–haul–dump (LHD) machines, belt conveyors, hoisting machines and others. Predictive maintenance is a crucial aspect in the proper mine operation; it creates opportunity for early damage detection and planning repairs for the most suitable period. However, the number of objects that need to be maintained is massive. Thus, proper maintenance is a challenging task. Due to rapid development in the field of instrumentation and cloud computing technology as well as the significant growth in predictive maintenance for industrial applications, it is possible to use multi-source information data fusion to carry out large-scale condition monitoring systems. Different approaches for the data gathering can be applied: stationary and portable systems or highly innovative mobile inspection robots. Recently, the European Union recognized the need to invest in robotics, automation, industrial Big Data and other new technologies in order to improve the heavy industry including mining industry development. In this paper, the application of the cloud computing technology in predictive maintenance for data mining and analysis is presented. The results show that cloud technology can highly boost mine operation and provide useful diagnostic and managing information.

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