{"id":14429,"date":"2022-12-22T14:09:27","date_gmt":"2022-12-22T12:09:27","guid":{"rendered":"https:\/\/www.razor-labs.com\/tres-mitos-mas-comunes-sobre-el-mantenimiento-predictivo-en-la-industria-minera-parte-1\/"},"modified":"2025-09-08T14:41:41","modified_gmt":"2025-09-08T11:41:41","slug":"tres-mitos-mas-comunes-sobre-el-mantenimiento-predictivo-en-la-industria-minera-parte-1","status":"publish","type":"post","link":"https:\/\/www.razor-labs.com\/es\/tres-mitos-mas-comunes-sobre-el-mantenimiento-predictivo-en-la-industria-minera-parte-1\/","title":{"rendered":"Tres Mitos M\u00e1s Comunes sobre el Mantenimiento Predictivo en la Industria Minera – Parte 1"},"content":{"rendered":"\t\t
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Blog<\/a><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Tres Mitos M\u00e1s Comunes sobre el Mantenimiento Predictivo en la Industria Minera – Parte 1<\/h1>\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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diciembre 22, 2022<\/div>\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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diciembre 22, 2022<\/h2>\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

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

Myth 1. The data\u00a0 collected by the mining companies can be easily used for Predictive Maintenance<\/strong><\/h5>\n
Myth 2. Models that predict failures give (enough) value.<\/strong><\/h5>\n
Myth 3. Deploying sensors is enough for Predictive Maintenance.<\/strong><\/h5>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Myth 1. The Big Data miners collect can be easily leveraged for Predictive Maintenance.
Big Data vs. the Right Data<\/h1>

The first common misconception is that the big data that miners collect can be easily leveraged for predictive maintenance. Unfortunately, the reality is much more complex. <\/p>

The existing information is mainly based on PLC Tags that are stored historically. These are hundreds of gigabytes of data, not fully relevant to perform predictive maintenance properly. <\/p>

Let\u2019s take a look at the automation pyramid below.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The Automation Pyramid<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The automation pyramid shows all the organization’s data sources and information layers. The main parts of the data come from the lower layers of the pyramid.<\/span><\/p>

First and foremost, these are the sensors installed on the machines that measure temperatures, vibration, power, and so on, in addition to other values calculated by the PLC systems, the machine\u2019s computers, and the alarms. <\/span><\/p>

The upper layers of the pyramid contain additional data sources, like ERP and MES, which are the tags used to train predictive maintenance models. These tags tell the model whether the machine had malfunctioned, and if it did, what was the reason, how the machine functioned when it was new, or how it behaved just before the replacement. This is the information available in most mining companies today.<\/span><\/p>

However, if we were to talk to field condition monitoring experts whose primary focus is increasing the machines\u2019 reliability, they would say they use entirely different data. Yes, they will refer to the Historian\u2019s data mentioned above, but they will treat it as a secondary data type where predictive maintenance is concerned. <\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Data Sources Used by the Mining Condition Monitoring Experts <\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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These are the data sources they would look at first:<\/span><\/p>\n