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 data

• How the demonstrator identified changes in machine behaviour

• Why non-intrusive monitoring could help when systems are difficult to modify

• Why manufacturers should consider the information their machines already display

• How cross-business collaboration changed the direction of the project

Listen 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 replace

• How reliance on a small number of experts creates operational risk

• Why standard procedures cannot capture every real-world maintenance scenario

• How predictive maintenance and AI can make experience more accessible

• Why technology should support experts and develop the next generation, rather than replace people

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.

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 Steel

• A success case that helped avoid 24 hours of unplanned downtime

• How Senseye Copilot was supporting the way teams worked at the time of recording

• Practical advice for manufacturers implementing predictive maintenance

This 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-maintenance

Please 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 ago

• How Highland Pellets identified the right assets to monitor first

• The story behind a critical failure that was detected before it shut down production

• Moving from scheduled inspections to condition-based maintenance

• Building trust in AI-driven insights among maintenance teams

• Connecting predictive maintenance with CMMS workflows and planning processes

• Lessons learned from the first deployment and plans to scale further

• Why the future of maintenance is data-driven, proactive, and predictive

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.

In this episode of the Trend Detection podcast, guest host Nienke Vergeer is joined by Vera Kaupmann, Global Marketing Manager for SITRAIN, to explore how industrial companies can keep workforce skills aligned with the accelerating pace of technological change.

They discuss the growing skills and knowledge gap created as experienced employees retire, why traditional classroom training must be complemented by more flexible digital learning, and how continuous learning can become part of an organization’s culture rather than an occasional activity.

The conversation also explores how digital learning can help organizations:

• Capture and transfer expertise before it is lost

• Accelerate onboarding for new employees

• Provide consistent training across teams

• Fit learning into the working day through short, self-paced sessions

• Prepare employees for emerging areas such as industrial AI, data and cybersecurity

The episode also introduces SITRAIN access, Siemens’ digital learning platform for industrial training, including its subscription-based learning membership and free introductory learning content.

Digital learning is not only your next competitive advantage, but it’s always at your fingertips – anytime, anywhere.

Try it out for free with Freemium | SITRAIN access:

https://sitrain.siemens.com/web/page/lex_auth_01452623641047040012

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 of the Trend Detection Podcast, we're joined by James Loach, Head of Research for Senseye Predictive Maintenance at Siemens, to explore what industrial AI means in practice and how it is changing the future of maintenance.

James explains how statistical systems, machine learning and generative AI can work together to monitor assets at scale, investigate potential problems and provide more prescriptive guidance to maintenance teams.

He also discusses why machine context, maintenance history and human expertise will become increasingly important as AI models grow more capable.

In this episode, you’ll learn:

• What industrial AI means beyond the marketing terminology

• How machine learning and generative AI work together at scale

• Why context is critical for improving AI-generated insights

• How agentic AI could support more prescriptive maintenance decisions

• Why the future of maintenance could become increasingly autonomous

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.

In this episode of the Trend Detection Podcast, Niall Sullivan is joined by Chris Smith, Head of Operations for Senseye Predictive Maintenance, to explore the relationship between predictive maintenance and computerized maintenance management systems, or CMMS.

Chris explains why organizations should consider establishing value and learning how predictive maintenance fits within their operations before fully integrating it into existing workflows. He also discusses how CMMS data can provide valuable context, reduce manual investigation and help maintenance teams focus their attention on the issues that require action.

In this episode, you’ll learn:

• When to integrate predictive maintenance with your CMMS

• How CMMS data adds context and accelerates decision-making

• Why trust and ownership matter more than technology alone

• How to turn predictive insights into maintenance action

• Why standardised integrations still require local flexibility

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.

In this episode of the Trend Detection podcast, we speak with Todd Martin from Siemens.

Drawing on experience as both a long-term Senseye user and managed services specialist, Todd explains how expert support can help maintenance teams turn predictive insights into action, reduce administration and build trust in the approach.

Listen to discover:

• What predictive maintenance managed services look like in practice

• How expert analysis and customer maintenance teams work together

• Why technology alone is not enough to deliver results

• The role of trust, feedback and internal champions

• Why an open mind remains one of the most valuable maintenance skills

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.

In the latest episode of the Trend Detection Podcast, we speak with Michael Schrapp, Director, Global Go-to-Market for Data & AI at Siemens, about the Eigen Engineering Agent and the connection between automation engineering, reliability and maintenance.

In the episode, we explore:

🔹 Why industrial AI needs domain and project context

🔹 How integration with TIA Portal reduces manual translation and context loss

🔹 How validated engineering can support reliability and maintainability

🔹 The changing roles of skills, governance and accountability

🔹 Why the engineer remains central to review and approval

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