A recent report by Bassan et al. (2008) indicates that network-centric operations will play a major role in mines of the future. Part of the impetus for this change comes from successes with network-centric operations by the American military and the Australian military, where improved operational awareness and business-wide performance optimisation is obtained by implementing a network-centric framework (Australian Defence Force 2002; United States of America Department of Defence 2005).
It is predicted that network-centric operation, when applied to a mining operation, will:
• Facilitate substantial productivity increases;
• Allow for greater utilisation and spreading of expert analysis; and
• Integrate routine tasks into systems for the purpose of shifting human resource to more important work (decision making) (Alberts & Hayes 2005; Bassan, Knights & Dunn 2008);
A key part of the network-centric framework is integrated performance and condition monitoring, where information regarding the instantaneous state of processes, and likelihood of failure, is available and easily accessible for the entire network. Current mining techniques for integrated performance and condition monitoring, however, fall short of the requirements for successful integration into a network-centric operation.
Predictive model based performance and condition monitoring is presented as a viable solution to improve current mining diagnostics.
There currently exists significant literature pertaining to the use of predictive models for performance and condition monitoring of continuous processes in fields such as power generation (Jaw & Van 2004), oil and gas, and others (Chantler et al. 1995). There is considerably less information available regarding the application of predictive models for diagnostics of discrete processes, such as those in the mining industry.
Regardless, the benefits of network-centric operation can still be had through implementing predictive models, albeit in slightly different ways to those systems already implemented in industries such as power generation.