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What’s Next with Predictive Technologies by Keith Crouch

When we think of modern equipment reliability concepts we immediately gravitate towards predictive technologies and condition monitoring. Advanced warnings for impending failures is such a competitive advantage in today’s business environment, and it comes to no surprise that condition monitoring is the foundation for solid equipment reliability.  Without it, the steps necessary to improving equipment reliability are shaky at best.

Fluke Connect® Condition Monitoring Software

Condition Monitoring (CM) is the process of monitoring a specific aspect of the condition of a piece of equipment.  Monitoring these aspects gives us the opportunity to detect a significant change that could be indicative of an impending failure.  This is normally visually illustrated with the P-F curve.  Effective condition monitoring and early detection allows maintenance to be scheduled proactively to avoid or mitigate the consequences of a full functional failure.

There are a wide variety of options available today ranging from your trusty thermography and ultrasound to advanced data analytics and even reaching into machine learning applications.  The amount of choices and applications we have available today can be quite overwhelming, and make selecting the correct tool a challenge.  This workshop will explore the whole spectrum of technologies available, talk about when to use them, and demonstrate how you can expect to improve your equipment reliability and ultimately impact your bottom line.  Learn more…

 

Weibull Analysis and Advantages by Carl Tarum, Director of Software Research, Fulton Findings

What is Weibull Analysis?

When you test parts to failure, this is called Life Data. There will be variation. For example, if you test a drone while flying in a hover mode, the flight time will vary.

In the 1950’s Dr. Weibull proposed the Weibull equation that is a useful tool for estimating life data behavior.

  • F(t)=1−exp[−(t/h)b], where
  • F(t)      cumulative distributionfunction
  • exp      exponentialfunction
  • t           time tofailure
  • h          “Eta”, Characteristic life
  • b           “Beta”, WeibullSlope

Sample Tests

Suppose you test a drone with fresh batteries, and get times of 375, 381, and 400 seconds. Then test 6 flights that have been 3 weeks from charging. Times are 262, 280, 304, 308, 321, and 356 seconds. Next you test with

extra weight, and get times of 246, 255, 287, and 290. (These are actual test data from this author’s testing.)

You expect that time after charge and extra weight will affect flight time. These data can answer the following questions:

  • Is flight time affected by time sincecharge?
  • How does weight impact flighttime?
  • Are there enough data?
  • Is Weibull a good fit for the data? SuperSMITH® software provides a Weibull plot of the data (Figure 1) Reading from the chart, 10% of flights with fresh batteries would only last 363 seconds, and 90% will end before 402

Analysis

Flight Time affected: From Figure 1, the average flight time for freshly charged batteries is 386 seconds, while flights 3 weeks since charge have 308 seconds. Extra weight reduces time to 271 seconds.

Further data can produce a model for change in flight duration with respect to time since charge, and extra weight.

Data Sufficiency: A likelihood ratio test and contour plot (Figure 3) show that tests with extra weight or time since charge are significantly different from the tests with freshly charged batteries.  Read More

Register for Weibull Course

 


			

​Students from the University of Tennessee Maintenance and Reliability program visited Arconic last week.

On May 17, Tennessee Operations hosted 20 college students from the University of Tennessee Reliability and Maintenance Center. The students were provided an overview of the operations, took part in a panel to learn more about equipment reliability and engineering careers and toured the Continuous Cold Mill.

Tickle College of Engineering Students tour Amazon’s Distribution Center (CHA1)

On Friday, September 15th, the UT Reliability & Maintainability Center (RMC) took over sixty Tickle College of Engineering students, faculty, and staff to tour Amazon’s Distribution Center (CHA1) in Chattanooga, TN.  Attendees were given a first-hand look at how orders are accepted, fulfilled, and shipped, as well as how inventory is stored and managed at the site.  Participants agreed that it was fascinating to see the working facility of a service that so many of us use on a regular basis.  Most were surprised to find that inventory is arranged randomly, based on space available, with each item’s precise location captured through bar-code technology.

The RMC would like to thank Kym Chavez, Amazon Program Manager – Technical Training and Development for North American Reliability & Maintenance Engineering, for arranging the tour.  A special thanks also goes to the Operations Team at CHA1: Heather Boles, Chris Scanlon, and Brad Allen, as well as the CHA1 Reliability & Maintainability Engineering Team: Tom Wintz, Mike Freeman and Jesse Bratcher, for guiding the tours and answering questions.

Amazon will be hiring 5+ reliability interns for Summer 2018, through the RMC program.  If you would like to be considered for these or other RME positions, please contact Kim Kallstrom at kkallstr@utk.edu.

Motor Circuit Analysis: An Introduction to De-energized Electrical Motor Testing

Managers, engineers, & electricians that are responsible for maintaining electrical motor system operation will benefit from attending this informative introduction to electrical motor diagnostics using Motor Circuit Analysis (MCA) at the University of Tennessee’s Reliability and Maintainability Center’s Parade of Technologies event. We will discuss why electric motors fail and why ohm meters & insulation to ground testers only detect ~10% of motor failures.

Many people think they don’t have time or personnel to dedicate to the task of electric motor testing. However, motor testing using an MCA tool is very easy to implement and the MCA test takes <3 minutes. We will demonstrate why you should grab the MCA tool instead of the Meg-ohmmeter next time a motor system unexpectedly quits running at your facility. MCA can identify a ground fault, but it also evaluates the connections, cables, and detects internal stator winding faults. Moreover, MCA motor testing can be used for Quality Control & Commissioning, Trouble-shooting, and Predictive Maintenance.

This short course will provide you with valuable information to help you understand motor system faults and how to diagnose them. It includes hands-on interactive learning activities to teach what different fault indications mean and how this new information can help you make better decisions about the condition of your rotating equipment.  Case studies from actual field testing will be reviewed and discussed to bring it all together to help you better understand the applications and benefits motor testing will bring to your facility.

ALL-TEST Pro is excited to be back and we look forward to helping you better understand Electrical Motor Diagnostics and how it can benefit you.

 

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