{"id":14421,"date":"2023-01-23T18:28:18","date_gmt":"2023-01-23T16:28:18","guid":{"rendered":"https:\/\/www.razor-labs.com\/?p=14421"},"modified":"2025-12-19T11:21:34","modified_gmt":"2025-12-19T09:21:34","slug":"tres-mitos-mas-comunes-sobre-el-mantenimiento-predictivo-en-la-industria-minera-parte-3-2","status":"publish","type":"post","link":"https:\/\/www.razor-labs.com\/es\/tres-mitos-mas-comunes-sobre-el-mantenimiento-predictivo-en-la-industria-minera-parte-3-2\/","title":{"rendered":"Tres Mitos M\u00e1s Comunes sobre el Mantenimiento Predictivo en la Industria Minera – Parte 3"},"content":{"rendered":"\t\t
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In Part 1<\/a> and Part 2<\/a> of this blog series, I have shared my view on the Top 2 most common misconceptions about Predictive Maintenance in the Mining industry – 1. The Big Data can be easily leveraged for Predictive Maintenance, and 2. Models that predict failures provide value.\u00a0<\/span><\/p>\n We have seen that the collected data doesn\u2019t come from the right sources and is inefficient in running proper root cause analysis that can prevent unplanned shutdowns or recurring failures and that AI models that predict failures might not be able to pinpoint the exact root cause of the malfunctions, perpetuating the loop of unplanned shutdowns. Today, I will discuss the third most common myth.<\/span><\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t