Creative Thinking in Predictive Maintenance: A Conversation with Jordan Walters

Описание: 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 covers:

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The Essence of Creativity in Engineering: How creative thinking is crucial for innovating solutions to complex problems in predictive maintenance (PM), moving beyond established methods to develop bespoke approaches for each customer.




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Unconventional Problem-Solving with Existing Tools: Discover how seemingly limited data, like temperature readings from electric car charger pins, can be creatively manipulated using features like "derived measures" to detect degradation, even when traditional sensor deployment isn't feasible.




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Bridging the Gap: From Industrial Practice to Education: Learn about the "Connected Curriculum" initiative, which brings Senseye to university students, and the creative adaptations needed to teach real-world data challenges (like noisy or incomplete data) and PM principles in an academic setting.




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Debunking Misconceptions about AI and Data: Understand that perfect data is a myth and that effective AI in PM, like Senseye, thrives on curated, clean data focused on specific condition indicators, rather than a "big data" dump, to provide nuanced and accurate insights.




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AI as an Enabler for Human Creativity: Explore how AI serves as a powerful tool to support and amplify human ingenuity in engineering, emphasizing the importance of asking questions, providing context, and fostering a collaborative environment to drive innovation and personal growth in the field.


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-predic...)