PeakVue Analytics for More Reliable Machinery Performance

Emerson's Robert Skeirik


Yesterday, in a post, How to Prevent Bearing Failures, we described how PeakVue analytics technology can provide early warning for bearing problems.

In this 6-minute YouTube video, PeakVue Signal Processing Demo, Emerson’s Robert Skeirik describes how these advanced analytics can spot bearing problems much earlier that traditional vibration measurements.

This extra time allows the operations and maintenance teams to plan and resolve the issue before a failure occurs.

In the video, Robert compares and contrasts simple vibration data measured in inches or centimeters/second versus PeakVue analytics measuring peak impact in g’s (g-force). He shows data of a bearing heading for failure.

The vibration data shows little change as the bearing fault advances in severity until the point of failure. The PeakVue analytics show a steady progression for good, to alert maintenance, to approaching end of life, to failure imminent, to failure. For most equipment, this progression occurs over time to allow maintenance to be scheduled to avoid unplanned downtime.

PeakVue Analytics Rules of 10

The PeakVue analytics can also be used for gearbox analysis, as well as two leading root causes of failure—insufficient lubrication and cavitation on process pumps.

You can connect and engage with vibration and reliability experts at the October 1-5 Emerson Exchange conference and/or in the Reliability & Maintenance group in the Emerson Exchange 365 community.

Posted Friday, September 14th, 2018 under Asset Optimization, Reliability.

One comment so far

  1. Jonas Berge says:

    PeakVue is a great example of edge analytics. Alanlytics should be done as close to the sensor as possible. Analytics and dashboards are key part of digital transformation. The architecture for analytics fits nicely with the control system architecture already in the plant. They are both layered architectures using the same protocols like fieldbus and wireless, so it easy for the I&C team to manage both. Real-time analytics at the edge as close to sensors as possible. Non-real-time analytics can be in the cloud. Analytics ties up to ERP just like the control system, for integrated workflow. Learn how other plants do it from this essay:
    https://www.linkedin.com/pulse/layers-analytics-edge-cloud-better-results-jonas-berge/

Leave a Reply