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Most failures in lockout/tagout procedures are not where reliability teams expect to find them. The dangerous moment is not the application of energy isolation. It's the return to service.

This is the half of the procedure that gets less attention in training, less rigour in execution, and almost no scrutiny in audits. The lockbox came off. The work was completed. The line was restarted. As far as most paperwork is concerned, the procedure worked. The trouble is that paperwork rarely captures what happened in the last few minutes.

Every year, The RELIABILITY Conference® brings together the organizations doing the real work. The ones modernizing aging infrastructure, transforming maintenance cultures, and proving that reliability isn’t a buzzword but a measurable business advantage.

Reliability organizations today are not short of data—they are short of clarity. Modern gas turbines and rotating equipment generate vast amounts of operational data, yet translating that data into timely, confident decisions remains a persistent challenge. Engineers are often faced with multiple interacting variables, conflicting signals, and limited time to diagnose emerging issues. Artificial intelligence (AI) and machine learning (ML) offer a way to bridge this gap—not by replacing engineering judgment, but by accelerating it.