{"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
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 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 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 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 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 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\t\t\t\t\t\t\t\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\t<\/div>\n\t\t<\/section>\n\t\t\t\t
AI-Guided Diagnosis<\/h2>\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
\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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
\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
Affected compressor<\/i><\/h6>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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
\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
Acceleration RMS trend<\/em><\/h6>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
Resolution<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
Conclusion<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t