How AI is optimizing factory maintenance – Part 3

By Spencer Acain

Predictive maintenance has long been a topic of interest in industry but implementing and scaling theoretical models into the real world has proven to be fraught with challenges. However, by approaching the problem from a different angle, Senseye seeks to develop a scalable, general-purpose solution that can easily apply to the often less than ideal real-world data coming from factories. With intelligent use of AI models, predictive maintenance can be achieved without the use of the costly and difficult to scale bespoke models that have dominated the field for many years.

In this final episode on predictive maintenance, host Spencer Acain is joined by Dr. James Loach, Head of Research for Senseye Predictive Maintenance, to discuss Senseye’s unique approach, the struggles of adopting predictive maintenance and AI in the real world, and what the future for AI holds.

In this episode you will learn:

  • General purpose decision support (1:06)
  • Challenges of adoption (6:20)
  • A rapidly changing world (10:02)
James Loach

James Loach

Head of Research for Senseye Predictive Maintenance

Spencer Acain

Spencer Acain

Technical Writer for Global Marketing at Siemens Digital Industries Software

AI Spectrum Podcast

AI Spectrum

This podcast features discussions around the importance of AI and ML in today’s industrial world.

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This article first appeared on the Siemens Digital Industries Software blog at https://blogs.sw.siemens.com/podcasts/ai-spectrum/how-ai-is-optimizing-factory-maintenance-part-3/