In precision shaft alignment, repeatability is the cornerstone of both confidence and credibility. Without consistent, repeatable data, even the most advanced alignment tools can produce misleading results, leading to improper corrections, increased vibration, premature wear, and costly downtime. This webinar will explore the critical role of data repeatability in shaft alignment and how it directly impacts maintenance efficiency, equipment reliability, and overall operational performance.
Steam turbines are the workhorses of thermonuclear power plants and large ships, but their efficiency and safety hinge on one critical component: the rotor blade. Last-stage rotor blades (LSRBs) are prone to failure due to prolonged exposure to high-speed wet steam (HSWs). These failures aren’t just costly—they can lead to catastrophic accidents, with fractured blades causing cascading damage to other components running at high speeds.
Back by popular demand, our panel of subject matter experts return to provide advice, answer questions, and discuss the hot topics in all things vibration, lube oil, ultrasound, thermography and data science. Join us in 2025 to hear about what’s new in our fields of condition monitoring and where we are headed.
Seemingly, one advantage is gathering data at a vastly improved rate. Rather than a single reading every quarter, data can now be captured multiple times every hour. The advantages are obvious: much greater visibility on developing faults and a trail to follow to track existing known issues.
This webinar teaches you how to calculate the economic impact of ignoring oil analysis data in a simple-to-understand format. You'll also explore several case studies to show what a true problem-to-failure curve looks like when the data is ignored.
Taking a look at the next step in electric motors. In this episode, Todd and Noah talk about today's technology, how it looks forward to tomorrow in continuous monitoring and the benefits of doing so.
Most wireless vibration sensors used in IIoT machine health applications today will have MEMs technology under the hood; however, many vibration analysts are still skeptical whether this is truly a viable alternative to traditional accelerometers.
Wireless sensors, artificial intelligence and machine learning offer the promise to deliver advances in predictive maintenance by automating the monitoring of your machine health.
Maintenance (like many other functions) depends greatly on data. Maintenance data has always been a concern for most of the organizations due to many reasons and factors during the different phases of the asset life cycle.
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