AI Case Study
BHP saves $5.5M by predicting mining truck maintenance requirements with machine learning
BHP has established a Maintenance Centre of Excellence to analyse data from its machinery in order to predict equipment maintenance needs, improving the maintenance of trucks at several of its sites, saving $5.5 million in costs at one mine alone.
Mining And Metals
Watt Electrical News reports that BHP's Maintenance Centre of Excellence (MCoE) "will standardise maintenance systems and processes for BHP’s worldwide operations, replacing the previous model of having 40 different maintenance organisations globally, each with its own way of working. One of the keys to the MCoE model is its reliance on data science techniques, such as machine learning, to understand how maintenance is performed at each site and where improvements can be made. It is focusing on the “highest value opportunities” first, which for BHP have been improvements to the Caterpillar 793F haul trucks - a fixture at its Western Australian iron ore mines but also in use across its worldwide operations. Analytics of engine performance in its global 793F fleet turned up “a large degree of variance” between mines in how long engine components lasted before they needed to be replaced.
BHP said it had created machine learning algorithms to analyse component failure history and to analyse the wear on engine components in real-time, enabling it to 'better predict failures' and to plan maintenance further in advance, with greater degrees of accuracy."
On its website, BHP claims it has "over 3,000 machines in our operations, from trucks the size of houses to shovels that move millions of tonnes of material every year. When equipment fails it can put our people at risk, disrupt production, increase costs and reduce our ability to provide resources to customers. So, maintenance is a priority, and we spend US$3.5 billion a year on the upkeep of our plants and equipment."
Watt Electrical News reports on the results of 793F maintenance program improvements at the Yandi mine: "The accuracy of maintenance planning for the trucks at Yandi had improved significantly: 'from only 10 percent of jobs planned more than two weeks in advance being accurate to 85 percent'. BHP also managed to reduce instances of parts not being available when trucks needed maintenance. Supply chain accuracy has improved, from 13 percent of parts being missed for when we need to do work or service these trucks, to only one percent of parts being missed, and for planned work this is typically zero percent... The project had generated $5.5 million in cost savings at Yandi in FY17, and truck availability for work at the mine was 'well ahead of 90 percent'."
According to Watt Electrical News, BHP uses "machine learning algorithms to analyse component failure history and to analyse the wear on engine components in real-time, enabling it to 'better predict failures' and to plan maintenance further in advance, with greater degrees of accuracy".
Sensory data from trucks, some real-time, some historic maintenance record data