The winner of the 2018 Emerson “Reliability Program of the Year” Award was Detour Gold mining, one of the largest gold mines in Canada. The mine in Ontario operates 24/7/365 and had a throughput increase of 30%. The finalists represented three very good programs. The other two were Bayer (previously Monsanto, Muscatine-Iowa facility) and Sadara refinery (Saudi Aramco and Dow Chemical Alliance) all with different, yet impressive accomplishments. But the winner demonstrated the best operational journey (from the ground up) with data. The award was established in 1989 to recognize the best reliability and maintenance practices globally.
The judges were Klaus Blache – Director of the Reliability & Maintainability Center and Research Professor, University of Tennessee College of Engineering, Paula Hollywood – Senior Analyst, ARC Advisory Group, Ghaith Alghamdi – Lead Engineer, Asset Maintenance, Saudi Aramco Shell Refinery Co. (SASREF) and 2017 winner, and Howard Penrose – President, MotorDoc, LLC (and current SMRP Chairman).
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…
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
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
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
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.
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 email@example.com.