The aim of this article is to introduce a haul truck availability improvement multi-objective computer model developed by using artificial intelligence methods (AIMs) to complete predictive analytics for haul trucks’
This model will be developed as an online service or a software package, working with existing data collection and analysis systems to predict serious and catastrophic failures in haul trucks’ braking systems. This model makes a priority list for future maintenance activities to reduce unscheduled mishaps, avoid catastrophic failures and provide a platform for ongoing proactive maintenance. All recommendations created by the model will be filtered and sent to operators, supervisors and mine managers for making better decisions (Figure 1).
Haul trucks move the main part of mine materials in open-cut mines. One of the most important components of trucks is the braking system. This system plays a primary role in a haul truck’s availability and, as a result, mine productivity. Moreover, between 30 and 40 per cent of total open-cut mines’ costs are on scheduled and unscheduled maintenance annually. Based on the collected data from some big open-cut mines in the United States and Australia, the main part of maintenance costs is just for haul truck repair. Furthermore, one of the effective means of increasing safety in open-cut mines is improving haul truck activities in the fleet.