Description
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.
Predictive maintenance can transform how manufacturers approach equipment reliability, but that doesn’t mean it’s the right solution for every asset, failure mode or situation.
In this episode of the Trend Detection podcast, we're joined by Jordan Walters, Delivery Lead at Siemens, to explore when predictive maintenance makes sense, and when a different maintenance approach might be the better choice.
In this episode, they discuss:
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Why the most critical assets aren’t always the best place to start with predictive maintenance
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The importance of having the right data, not simply more data
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How asset selection, failure frequency and available lead time can determine whether predictive maintenance will deliver value
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Why context is essential for AI to interpret machine behaviour effectively
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The difference between not being ready for predictive maintenance and predictive maintenance not being right for a particular use case
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Why predictive maintenance should complement, rather than automatically replace, other maintenance strategies
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The role of maintenance teams and human expertise in validating insights and turning predictions into action
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The three foundations for success: the right data, the right machines and the right 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 (http://www.siemens.com/senseye-predictive-maintenance)