{"id":14295,"date":"2025-06-08T16:31:40","date_gmt":"2025-06-08T13:31:40","guid":{"rendered":"https:\/\/www.razor-labs.com\/fragmento-de-estudio-de-caso-fusion-de-sensores-identifica-degradacion-mecanica-en-ventilador-de-sinter-antes-de-la-falla\/"},"modified":"2025-09-05T12:07:02","modified_gmt":"2025-09-05T09:07:02","slug":"fragmento-de-estudio-de-caso-fusion-de-sensores-identifica-degradacion-mecanica-en-ventilador-de-sinter-antes-de-la-falla","status":"publish","type":"post","link":"https:\/\/www.razor-labs.com\/es\/fragmento-de-estudio-de-caso-fusion-de-sensores-identifica-degradacion-mecanica-en-ventilador-de-sinter-antes-de-la-falla\/","title":{"rendered":"Fragmento de Estudio de Caso: Fusi\u00f3n de Sensores Identifica Degradaci\u00f3n Mec\u00e1nica en Ventilador de Sinter Antes de la Falla"},"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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Fragmento de Estudio de Caso: Fusi\u00f3n de Sensores Identifica Degradaci\u00f3n Mec\u00e1nica en Ventilador de Sinter Antes de la Falla<\/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 8, 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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junio 8, 2025<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Unexpected sinter fan failures in alloys production can cause major operational downtime and financial losses. To avoid these risks, a leading site implemented DataMind AI<\/a>\u2122 to monitor critical equipment in real time and flag early signs of mechanical deterioration.<\/p>\n

In early March 2025, DataMind AI<\/a>\u2122 detected elevated axial vibrations in one of the site\u2019s high-capacity sinter fans. Traditional inspections had missed contributing factors: an open suction cowling left unsealed after a previous incident, which disrupted airflow and gradually increased load on the impeller.<\/p>\n

Additionally, a prior foreign object strike had likely caused undetected internal damage, further affecting system balance.
As vibration levels continued to rise,
DataMind AI<\/a>\u2122 escalated the fan\u2019s health status to Critical, prompting immediate inspection. The site\u2019s team identified mechanical degradation in two key components: a misaligned coupling and an imbalanced impeller – confirmed by the need for substantial weight correction during rebalancing.<\/p>\n

Thanks to early diagnostics powered by AI and multi-sensor fusion, the maintenance team intervened before failure occurred – avoiding equipment damage, preventing downtime, and protecting downstream assets.<\/p>\n

This case demonstrates how DataMind AI<\/a>\u2122 enables predictive maintenance that drives faster, smarter decisions with minimal manual effort.<\/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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Results at a Glance<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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