Applying AI to Predictive Maintenance at Scale: A Senseye Perspective
Welcome to the Trend Detection podcast, brought to you by Senseye Predictive Maintenance – which gives you visibility and insights into all your assets, from single machines to full plants to help you reduce downtime, increase knowledge sharing and accelerate digital transformation across your organization.
In this special episode with David Humphrey, Director of Research, ARC Europe, we discuss:
•
How predictive maintenance has evolved from scheduled inspections to data‑driven decision‑making using connected machine data.
•
What Senseye Predictive Maintenance is, how it works as a cloud‑based analytics application, and where it fits within Siemens’ broader asset and maintenance portfolio.
•
How machine learning and generative AI are used to detect abnormal asset behavior and translate complex analytics into actionable maintenance guidance.
•
How historical machine data, maintenance records, and technical documentation are leveraged to speed diagnosis and reduce dependency on individual expert knowledge.
•
Why scalability, usability, and organizational adoption are critical success factors for predictive maintenance programs operating at hundreds or thousands of assets.
You can find out more about how Senseye Predictive Maintenance can reduce unplanned downtime and contribute towards improved sustainability within your manufacturing plants, by visiting: www.siemens.com/senseye-predictive-maintenance (http://www.siemens.com/senseye-predictive-maintenance)
Welcome to the Trend Detection podcast, brought to you by Senseye Predictive Maintenance – which gives you visibility and insights into all your assets, from single machines to full plants to help you reduce downtime, increase knowledge sharing and accelerate digital transformation across your organization.
In this special episode with David Humphrey, Director of Research, ARC Europe, we discuss:
•
How predictive maintenance has evolved from scheduled inspections to data‑driven decision‑making using connected machine data.
•
What Senseye Predictive Maintenance is, how it works as a cloud‑based analytics application, and where it fits within Siemens’ broader asset and maintenance portfolio.
•
How machine learning and generative AI are used to detect abnormal asset behavior and translate complex analytics into actionable maintenance guidance.
•
How historical machine data, maintenance records, and technical documentation are leveraged to speed diagnosis and reduce dependency on individual expert knowledge.
•
Why scalability, usability, and organizational adoption are critical success factors for predictive maintenance programs operating at hundreds or thousands of assets.
You can find out more about how Senseye Predictive Maintenance can reduce unplanned downtime and contribute towards improved sustainability within your manufacturing plants, by visiting: www.siemens.com/senseye-predictive-maintenance (http://www.siemens.com/senseye-predictive-maintenance)
