{"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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Tres Mitos M\u00e1s Comunes sobre el Mantenimiento Predictivo en la Industria Minera – Parte 3<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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23 de enero de 2023<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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by Michael Zolotov<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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23 de enero de 2023<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Although the potential is immense, studies show that most Predictive Maintenance programs fail to drive real value for mining companies.<\/h4>\n

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In this blog, I will discuss three common myths about predictive maintenance and how mining companies can leverage AI sensor fusion to transform their predictive maintenance programs into value drivers.<\/h4>\n

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Myth 1. The data\u00a0 collected by the mining companies can be easily used for Predictive Maintenance<\/strong><\/h5>\n

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Myth 2. Models that predict failures give (enough) value.<\/strong><\/h5>\n

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Myth 3. Deploying sensors is enough for Predictive Maintenance.<\/strong><\/h5>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\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

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