{"id":14293,"date":"2025-06-22T09:35:38","date_gmt":"2025-06-22T06:35:38","guid":{"rendered":"https:\/\/www.razor-labs.com\/estudio-de-caso-deteccion-de-problemas-de-lubricacion-de-rodamientos-en-un-compresor-con-datamind-ai\/"},"modified":"2025-09-05T12:06:11","modified_gmt":"2025-09-05T09:06:11","slug":"estudio-de-caso-deteccion-de-problemas-de-lubricacion-de-rodamientos-en-un-compresor-con-datamind-ai","status":"publish","type":"post","link":"https:\/\/www.razor-labs.com\/es\/estudio-de-caso-deteccion-de-problemas-de-lubricacion-de-rodamientos-en-un-compresor-con-datamind-ai\/","title":{"rendered":"Estudio de Caso: Detecci\u00f3n de Problemas de Lubricaci\u00f3n de Rodamientos en un Compresor con DataMind AI™"},"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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Estudio de Caso: Detecci\u00f3n de Problemas de Lubricaci\u00f3n de Rodamientos en un Compresor con DataMind AI™<\/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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junio 22, 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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junio 22, 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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At a major coal mining site, DataMind AI<\/a>\u2122 was deployed to monitor critical equipment, including compressors essential for plant auxiliary operations. After the deployment, the system flagged one compressor\u2019s drive-end bearing for abnormal vibration levels – well above those observed in identical units.<\/p>\n

While traditional tools may have detected a generic fault, DataMind AI<\/a>\u2122 went further: using AI-driven diagnostics, it identified the underlying cause – abnormal friction likely resulting from lubrication issues. Rather than recommend immediate replacement, the system guided a structured troubleshooting process that avoided downtime and unnecessary intervention.<\/p>\n\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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AI-Guided Diagnosis<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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  1. Friction Detected: <\/strong>DataMind AI<\/a>\u2122\u00a0identified a consistently elevated noise floor at high frequencies – an early sign of bearing friction. The pattern suggested a lubrication-related issue.<\/li>\n
  2. Flow Integrity Verified:<\/strong> The system recommended checking grease flow paths. The site confirmed that inlet and purge ports were clear, ruling out blockage as the cause.<\/li>\n
  3. Quantity Mismatch Identified:<\/strong> AI insights prompted a review of the applied grease volume. The team discovered only 20g had been used – well below the OEM-recommended 50g.<\/li>\n
  4. Corrective Action Taken:<\/strong> After applying the correct amount, vibration levels dropped significantly – validating that lubrication was directly linked to the issue.<\/li>\n<\/ol>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Detection & Comparative Analytics<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    In addition to identifying the local issue, DataMind AI<\/a>\u2122 compared performance across similar compressors:<\/p>\n

    Spectrum Analysis:<\/strong> Revealed a raised noise floor consistent with internal friction.<\/p>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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    Spectrum analysis with elevated noise floor<\/em><\/h6>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Comparative View:<\/strong> Compressor performance was significantly worse than peers under similar load<\/p>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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    Compared to unaffected compressor<\/em><\/h6>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Greasing Event Correlation:<\/strong> Instead of decreasing, vibration levels actually increased after greasing – indicating that the applied grease quantity was potentially incorrect and inconsistent with the required specification.<\/p>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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    A comparison between the grease quantity recorded in the work order and the OEM recommendation revealed that only 20g of grease was applied on-site, whereas the OEM specifies 50g.<\/p>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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    Resolution<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    Thanks to DataMind AI<\/a>\u2122, the site avoided premature replacement and optimized planned maintenance instead. The system\u2019s layered diagnostics – leveraging its integration with work orders to compare actual greasing with required quantities – enabled DataMind AI<\/a>\u2122 to detect human error by cross-checking what was performed against what was needed. This enabled comprehensive root cause investigation.<\/p>\n

    The real-time feedback also aligned internal and external teams around the same data, helping the team stay coordinated while minimizing operational disruption.<\/p>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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    Conclusion<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    DataMind AI<\/a>\u2122 provided more than just early alerts – it delivered structured, data-backed decision-making. By guiding the site through friction detection, root cause investigation, and corrective action, the system enabled smarter maintenance planning.<\/p>\n

    As a result, the site avoided 5 hours of unplanned downtime, saved approximately $140,000, and gained new clarity into compressor health – highlighting the power of AI to improve reliability, reduce cost, and drive smarter industrial performance.<\/p>\n

    Request a Demo >><\/strong><\/a><\/p>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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    Fill in the form to read the entire case study<\/strong><\/h4>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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