Your Reliability Engineering Professional Development Site
Find all articles across all article series listed in reverse chronological order.
If there is one graphical technique that deserves a lot more attention that it gets and that every engineer needs to utilize in their day to day, my vote would definitely go for the mighty Youden Plot.
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Proponents of the Continuous Improvement method often quote the dictum ‘what gets Measured, gets Improved’. I’d like to modify it by adding ‘what gets Monitored…’ to its beginning. Here I’m referring to the Monitoring of the physical assets in their usage conditions and being Measured & Improved for their Reliability (Availability %, Cost $, MTBF, etc.) and Safety metrics.
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You read that headline right: everything does suck! And in this article, I’m going to give you the solid reasons why this makes me both optimistic and excited about our future!
Think to yourself: On any given day, how many times do you hear someone grumbling about their phone not working well, traffic patterns on their way to work driving them insane, how a business of any kind inconvenienced them by having poor customer service, or a government institution – commonly one like the DMV – putting them in a sour mood?
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Use Ben Franklin’s Distribution Curves for Performance Monitoring and Fast Improvement. Ben Franklin used statistical analysis to change his life. By measuring his ‘gaining of Virtue’ he quickly became rich and famous. You can change your business performance in the same way. But be cautious – Franklin’s method changed his behaviour forever.
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The (age-specific or actuarial) force of mortality drives the demand for spares, service parts, and most products. The actuarial demand forecast is Σd(t‑s)*n(s), where d(t-s) is (age-specific) actuarial demand rate and n(s) is the installed base of age s, s=0,1,2,…,t. Ulpian, 220 AD, made actuarial forecasts of pension costs for Roman Legionnaires. (Imagine computing actuarial demand forecasts with Roman numerals.) Actuarial demand rates are functions of reliability. What if reliability changes? We Need Statistical Reliability Control (SRC).
Actuarial demand forecasts require updating as installed base and field reliability data accumulates. Actuarial failure rate function, a(t), is related to reliability function, R(t), by a(t) = (R(t)-R(t-1))/R(t-1), t=1,2,… If products or parts are renewable or repairable, then actuarial demand rate function, d(t), depends on the number of prior renewals or repairs by age t [George, Sept. 2021].
Should you implement the manufacturer’s recommended maintenance schedules? Not without sanity-checking them first.
Be sure you consider your operating environment, which the manufacturer is unable to do.
[Read more…]A few weeks ago, I had written a post comparing threshold exposure values for SO2 and H2S. After that post, Lee Pharis, one of the blog readers, forwarded me an email describing the new EPA standard for SO2 exposure.
EPA has set the one-hour SO2 health standard at 75 parts per billion (ppb), a level intended to protect against short-term exposures ranging from five minutes to 24 hours.
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Reliability, in its academic root, is defined as the probability that a system will perform its intended function in a specified mission time and within specific process conditions. Reliability (R) is related to the Probability of Success as opposed to the Probability of Failure (F), and the relation between R and F is:
$$ \displaystyle \large R = 1 – F (t) \ \:\:\:\: \:\:\:\: (for \:mission\: time\: t) $$
In the above context, a reliability example would be: What is the probability that a centrifugal pump in a sheltered enclosure will push 3,000m3/day of sweet crude oil without unplanned failures for a period of 8,760 running hours?
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‘Fleet’ is the representation of a population of repairable products that are currently in use out in the field by customers. Repairable is the keyword differentiating a ‘fleet’ from commonly known consumer product ‘units’. The differentiation is key in understanding the type of reliability program needed.
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[Last month I mentioned that the next article would be on the subject of the application of models in the FMEA process. I am postponing that important topic, in order to do more research. Stay tuned . . .]
This month, I want to discuss one of the most common problems that FMEA teams face: getting confused about the difference between failure modes, effects and causes.
“Things are not always as they seem; the first appearance deceives many.” Phaedrus
This article is the fifth of fourteen parts to our risk management series. The series will be taking a look at the risk management guidelines under the ISO 31000 Standard to help you better understand them and how they relate to your own risk management activities. In doing so, we’ll be walking through the core aspects of the Standard and giving you practical guidance on how to implement it.
In previous articles (1st, 2nd, 3rd, & 4th) we’ve looked at the core elements of the risk management framework generally, as well as the role of leadership and commitment, integration and design more specifically. In this article, we’ll be looking at how to effectively implement the risk management framework into your organisation.
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To be a great Maintenance Planner and Scheduler you need to know a lot about your equipment’s’ engineering, how to manage complicated projects well, use world class maintenance processes for creating equipment reliability, and a whole lot more.
Recently I was asked what skills and competencies a Maintenance Planner and Scheduler needs to master to do world class maintenance planning and scheduling.
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In 1956, Harvard Business Review published a landmark article by economist and business leader Armand Feigenbaum titled “Total Quality Control” that summarized the quality control system he developed during his long tenure at General Electric and gave prominence to many concepts still used in quality management today. One of those concepts was cost of quality measurement.
The goal of a cost of quality measurement system is to provide manufacturing leaders with a tool that can help drive process improvements. By understanding the magnitude and sources of their quality costs, manufacturing executives, managers, engineers, and technicians can more effectively direct their efforts, improvement strategies, and capital budgets toward reducing them.
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Chemical processes and designs are increasingly being evaluated for inherent safety – i.e. reduce the hazard rather than the risk. The philosophy behind inherent safety is ‘What You Don’t Have, Can’t Leak’ and so you take necessary steps to reduce the hazard.
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A common error when performing a life analysis for an asset is to confuse repairable and non-repairable assets. The mathematical determination of the life characteristics for each model is different, so throwing a simple “Weibull analysis” at them might lead to the wrong results but also the loss of valuable information.
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