A bearing fails. You replace it. Condition monitoring catches the next one before it breaks. Another work order is completed. Another bearing is replaced. 

The failure was predicted, but nothing changed. 

Too many organizations stop at detection, using condition monitoring as a tool to find failures instead of eliminating the conditions that cause them. The result? The same problems return, maintenance teams stay stuck in reactive mode, and reliability never improves. 

In Why Condition Monitoring Isn’t Enough: Escaping the Cycle of Doom, Trent McJunkin and Amissa Giddens will challenge the traditional view of condition monitoring and show how the highest-performing organizations use condition data to drive meaningful change across engineering, procurement, maintenance, and leadership. 

You’ll learn how to move beyond simply predicting failures and start using your data to improve asset design, maintenance strategies, decision-making, and organizational alignment, breaking the cycle of recurring failures and building a culture of continuous reliability. 

If you’re ready to stop treating symptoms and start eliminating root causes, this webinar is for you. 

Key takeaways include:

• Break the Cycle of Repeated Failures | Learn how to move beyond replacing failed assets and address the underlying conditions that cause recurring problems.

• Turn Condition Monitoring Data into Action | Discover how leading organizations use condition data to drive better engineering, maintenance and business decisions.

• Eliminate Root Cause, Not Just Symptoms | Understand how to use reliability insights to prevent failures before they develop, rather than simply predicting them.

• Align Teams Around Reliability Improvement | See how engineering culture, maintenance, procurement and leadership can work together to create lasting operational improvements.

• Build a More Reliable and Proactive Organization | Gain practical strategies for improving asset performance, reducing reactive work and fostering a culture of continuous reliability.

When: