Ya que las compañías trabajan en optimizar sus eficiencias operacionales, hoy en día existe una demanda creciente a nivel global por sistemas de automatización industrial. A medida que los procesos de manufactura y producción industriales se realizan cada vez más de manera automática, en el futuro habrá un aumento en la necesidad de desarrollar tecnologías de mantenimiento predictivo (tanto hardware como software) que puedan ayudar a mantener el funcionamiento de la maquinaria con un desempeño óptimo. A su vez, estas tecnologías tendrán que identificar el remplazo de partes dañadas y los problemas al instante antes que una falla interrumpa la producción y cause costosos periodos de inactividad no planeados.

El IIoT y el aprendizaje profundo jugarán una función importante en el avance de la analítica predictiva y en la superación de estos dos grandes desafíos: la calidad de datos y la brecha entre los humanos y las máquinas. Además, el IIoT y el aprendizaje profundo también serán críticos para ayudarnos a pasar y salir de la fase Trough of Disillusionment (abismo de desilusión) con el fin de establecer las soluciones de mantenimiento predictivo del IIoT como prácticas normales.

​Amnon Shenfeld

Amnon Shenfeld is founder and CEO of 3DSignals, a company developing a disruptive predictive maintenance IoT system that uses deep learning algorithms to analyze the airborne sounds of machines and predict malfunctions. Before 3DSignals, Amnon was the VP R&D of a computer vision company, Eyesight Technologies, and prior to that, held several R&D roles for global corporations and research laboratories. www.3dsig.com

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Nuestra nueva publicación, Estudio de Mejores Prácticas de CMMS.
Con este estudio ustedes tendrán una amplia comprensión del uso de los sistemas computarizados de gestión del mantenimiento (CMMS), qué oportunidades de crecimiento a future tienen y qué mejoras pueden hacerse.
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