{"id":2583,"date":"2017-12-20T15:09:46","date_gmt":"2017-12-20T04:09:46","guid":{"rendered":"http:\/\/yinghuiinternational.com\/?p=2583"},"modified":"2018-01-27T15:11:41","modified_gmt":"2018-01-27T04:11:41","slug":"mining-data-to-improve-safety-and-cut-maintenance-costs","status":"publish","type":"post","link":"https:\/\/yinghuiinternational.com\/index.php\/2017\/12\/20\/mining-data-to-improve-safety-and-cut-maintenance-costs\/","title":{"rendered":"Mining data to improve safety and cut maintenance costs"},"content":{"rendered":"
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We have over 3,000 machines in our operations<\/span>, 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.<\/p>\n

BHP has created the Maintenance Centre of Excellence to partner with our operations to deliver safe, sustainable improvement in our equipment performance. The Centre leverages BHP\u2019s scale to draw on the deep expertise, data and systems we hold across our business to reduce cost, cut unplanned downtime, improve production and ensure our equipment is safe and reliable for our people.<\/p>\n

Over the last 18 months we\u2019ve formed a team of data scientists to examine how we can predict problems and improve the reliability and uptime of our equipment.<\/p>\n

The team uses experience from outside the resources industry to find ways to improve our approach. For example, Boeing developed a tool that analyses real time black box data from aircrafts in-flight to predict what maintenance they\u2019ll need when they land. This is used by airlines to prevent cancellations and ensure equipment is in the right place at the right time. We\u2019re developing similar systems to improve BHP\u2019s maintenance.<\/p>\n<\/div>\n

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Our teams have developed algorithms that predict how a piece of equipment is likely to fail and when to best schedule preventative work.<\/h2>\n