{"id":12380,"date":"2025-07-02T11:27:36","date_gmt":"2025-07-02T08:27:36","guid":{"rendered":"https:\/\/www.razor-labs.com\/?p=12380"},"modified":"2025-08-08T13:43:55","modified_gmt":"2025-08-08T10:43:55","slug":"case-study-datamind-ai-detects-lubrication-fault-in-ventilation-fan","status":"publish","type":"post","link":"https:\/\/www.razor-labs.com\/es\/case-study-datamind-ai-detects-lubrication-fault-in-ventilation-fan\/","title":{"rendered":"Case Study: DataMind AI™ Detects Lubrication Fault in Ventilation Fan"},"content":{"rendered":"\t\t
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Blog<\/a>, Case studies<\/a><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Case Study: DataMind AI™ Detects Lubrication Fault in Ventilation Fan<\/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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julio 2, 2025<\/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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By Razor Labs<\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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7 min read<\/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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julio 2, 2025<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Overview<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Razor Labs deployed DataMind AI<\/a>\u2122 to monitor critical rotating equipment at a major coal mining site with underground operations – including a key ventilation fan essential for maintaining airflow and ensuring safe working conditions below ground.<\/p>

In May 2025, DataMind AI<\/a>\u2122 detected rising bearing friction through multi-sensor analysis, pinpointing lubrication failure as the root cause. The system initially classified the trend as an Alarm and later escalated it to Critical as the condition worsened. Through multi-sensor analysis, the system helped isolate the mechanical root cause: a progressive rise in
friction levels leading to lubrication failure.<\/p>

The site team confirmed that the fan\u2019s automated greasing system had malfunctioned – a jammed grease cartridge had prevented lubricant from reaching the bearings. This fault had gone undetected until DataMind AI<\/a>\u2122 flagged the trend. Based on these insights, the team manually re-greased the bearings and restored proper lubrication –
preventing premature bearing wear, unplanned downtime, and potential airflow disruption.<\/p>\t\t\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

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Fan flagged as Critical<\/i><\/h6>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Detection & Diagnosis<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Sensor Fusion Analytics allowed DataMind AI<\/a>\u2122 to distinguish real mechanical changes from operational noise. The fan operates in fluctuating conditions \u2013 flow, pressure, and motor load \u2013 which often mask early signs of mechanical deterioration.<\/p>

By synchronizing vibration, tacho, and motor current signals, along with advanced vibration analysis using envelope demodulation algorithms, the system confidently isolated the degradation pattern and identified its mechanical origin.<\/p>

Key insights included:<\/strong><\/p>