Trend Detection Podcast

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.This is the second episode in Rethinking Predictive Maintenance, a Trend Detection podcast series exploring the people, data and decisions behind successful predictive maintenance.Host Niall Sullivan is joined by Tom Jacques to explore how an International Tech Talents project turned existing PLC status lights into a new source of machine data. Using a camera, the team monitored the lights controlling automated railway points and combined the information with Senseye Predictive Maintenance to identify changing behaviour, without adding sensors or altering the existing control logic.Tom discusses what the demonstrator could mean for manufacturers whose machines may already display useful information that is not currently being captured as data. He also explains where non-intrusive monitoring could be valuable, what further testing would be required for operational use and how collaboration across Siemens helped transform the original idea.In this episode, you’ll learn:How a camera turned existing PLC status lights into usable dataHow the demonstrator identified changes in machine behaviourWhy non-intrusive monitoring could help when systems are difficult to modifyWhy manufacturers should consider the information their machines already displayHow cross-business collaboration changed the direction of the projectListen to discover why some of the machine signals manufacturers need may already be hiding in plain sight.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

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.This episode launches Rethinking Predictive Maintenance, a new Trend Detection podcast series exploring how manufacturers can retain expertise, capture new forms of machine information, evaluate predictive maintenance and expand successful approaches across multiple sites.In this first episode, host Niall Sullivan is joined by Rachel Green to explore what happens when experienced manufacturing and maintenance professionals leave the workforce, taking decades of knowledge with them.Rachel explains why technical information alone cannot replace the experience, judgement and credibility built over a long career.She discusses how manufacturers can reduce their dependence on a small number of experts by capturing maintenance history, adding context to machine data and using predictive maintenance and AI to help people access the right knowledge when they need it.In this episode, you’ll learn:Why critical maintenance knowledge is so difficult to replaceHow reliance on a small number of experts creates operational riskWhy standard procedures cannot capture every real-world maintenance scenarioHow predictive maintenance and AI can make experience more accessibleWhy technology should support experts and develop the next generation, rather than replace peopleYou 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

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.From the Trend Detection archive - a factory-floor perspective on AI-based predictive maintenance.Tobias, Head of Maintenance and Improvement at Siemens, shares the predictive maintenance journey of Siemens’ highly automated factory in Bavaria.The discussion explores how smart hardware, OT modernisation and AI-driven analytics were brought together in a live brownfield production environment. It also examines how Senseye Predictive Maintenance helped maintenance teams focus on critical assets, act on emerging equipment issues and reduce reactive firefighting.This episode was recorded previously, so references to the project and product reflect the position at the time of recording.

Welcome to the Trend Detection podcast, brought to you by Senseye Predictive Maintenance – the platform which enables predictive maintenance at scale across all of your assets, across all of your plants.This week, Trend Detection revisits a standout episode from the archive.In this conversation, Chris Wonson shares the story behind the deployment of Senseye Predictive Maintenance at BlueScope Steel and explores how predictive maintenance was being applied across its operations.The episode covers:How Senseye Predictive Maintenance was deployed at BlueScope SteelA success case that helped avoid 24 hours of unplanned downtimeHow Senseye Copilot was supporting the way teams worked at the time of recordingPractical advice for manufacturers implementing predictive maintenanceThis archive episode provides a valuable customer perspective on scaling predictive maintenance and turning asset data into practical maintenance action.Find out more about how Senseye Predictive Maintenance can help manufacturers reduce unplanned downtime and improve maintenance efficiency across their plants by visiting: www.siemens.com/senseye-predictive-maintenancePlease note that this conversation was originally recorded and published previously. Some product details may have evolved since the original recording.

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 episode we're joined by Andrew Rehm, Director of Reliability and Planning at Highland Pellets, to explore how predictive maintenance is transforming industrial operations.Andrew shares Highland Pellets' journey from traditional inspection-based maintenance to a more proactive, data-driven approach powered by AI. We discuss how the company increased plant uptime from 60% to 89%, uncovered critical issues before they became failures, and built trust in predictive maintenance across operations, maintenance, and reliability teams.The conversation goes beyond technology, covering change management, workforce adoption, maintenance planning, and why predictive maintenance is becoming a core part of Highland Pellets' long-term strategy.In this episode you will learn:Why predictive maintenance looks very different today than it did ten years agoHow Highland Pellets identified the right assets to monitor firstThe story behind a critical failure that was detected before it shut down productionMoving from scheduled inspections to condition-based maintenanceBuilding trust in AI-driven insights among maintenance teamsConnecting predictive maintenance with CMMS workflows and planning processesLessons learned from the first deployment and plans to scale furtherWhy the future of maintenance is data-driven, proactive, and predictiveYou 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