Your Reliability Engineering Professional Development Site
As part of the 35th Anniversary of the Governor’s Scholars Program in Kentucky, I was invited to participate in their Alumni Day at Northern Kentucky University. So naturally, I shared with this year’s Scholars what my experience taught me about engineering leadership and life!
Below are the slides and lessons learned that I shared. [Read more…]
In my last article, we reviewed a proposed Product Life Cycle process, which starts with a “Define” phase. In the “Define” phase, we are defining the project as well as the product.
We previously discussed the ‘technical leg’ of this process with the market analysis, identifying customer needs, product requirements, verification and validation, etc. [Read more…]
A common progression (stages of awakening) for an organization that has a minimal application of reliability tools to one that is large market holder with the right reliable product is often like this
Stage 1) “Reliability” testing is mostly re-labeled verification and validation testing. It measures if the design does what it is supposed to do
at the end of the program. Tests are executed with the intent of passing, not learning about the product or it’s performance under variability. The organization experiences a large field failure surge due a single or multiple issues. The pain of this experience in dollars, market image, and lost resource through “recovery phase” awakens them to the high ROI of incorporating reliability tools early and throughout the product development process
In prior articles on process capability, sample statistics and SPC statistics were assumed to be population parameters and ignored sampling variability. This article reviews the analytic methods that can be used to develop confidence bounds on the process capability indices.
The Pp index calculation requires an estimate of the parameter σ. The index is calculated as:
I’m using this week to get my office in order. This morning I found this series of drawings I did back around 1987. While I know my old friends from Building 317 will get a kick out of this, I also know that we weren’t the first and won’t be the last team of people who worked to make our plant a better place to work. I also know the last slide from this series is missing that showed the team alone saying “If you want to know how it happened, it pays to give credit where credit is due.” [Read more…]
Many organizations try to improve performance by just creating PM routines and letting the technicians loose to perform the work. This often has negative effects on plant performance. This has been proven through studies conducted by Ledet at numerous DuPont sites. This study looked at the impact of Planning, Scheduling and Preventative Maintenance on Plant Performance.
Ledet had found that by just implementing a PM / PdM program, organizations lost 2.40% of uptime (on a baseline of 83.50%). Not quite the results to expect when implementing a strategy to improve plant performance. When the PM / PdM program is implemented with Planning & Scheduling, the plant saw an increase of 5.10%. Now that is an improvement.
But what about the remaining 11.40% of uptime? How does one address the remaining downtime?
[Read more…]
Albert Einstein once said that … “Insanity is doing the same thing over and over again and expecting different results.” If you find yourself doing a root cause analysis on the same problem repeatedly, it may be time to revisit the root cause analysis from a framework point of view rather than as a tool. In this article, I will review an example of root cause analysis as a risk management framework. This is different than examining one of the tools or processes you use for troubleshooting a problem.
There are several root cause analysis frameworks that you may come across. In my research, the top five root cause analysis frameworks related to healthcare are from the:
Let’s say you have shipped 1,000 products to your customer on January 1st. All are immediately placed into service. And each month since you have received a few product returns, what we are going to call failures. We also have fitted the data to a Weibull distribution. Then in May, your boss asks you to estimate how many failures to expect in June.
This is a simple example as we’re not shipping units every month, nor changing the product design or assembly process. We also have worked out the fitted Weibull parameters already. That leaves the calculation of how many failures we should expect over the next month. [Read more…]
Many product development teams use a contract manufacturer (CM) to develop and manufacture their product. It’s the “develop” term that has limitations that may be unforeseen when engaging. Many companies use the CM to assist or even eventually entirely execute the product development. This can have great results or be problematic. But let’s talk about the arrangements that work well. Troublesome CM’s are a topic for a different time.
I like to say Reliability is all of quality over time. Quality professional tend to say reliability is an element of quality. David A. Garvin of the Harvard Business School suggests there are eight dimensions to quality, including reliability.
Either way one relates quality and reliability we need to remember that quality or reliability is not a department, team, the engineering down the hall. Quality and reliability is part of the culture of the organization. It is how we make decisions the impact how the product or service performs for customers. [Read more…]
This video provides the live audio for our Decision Making with Data presentation. The audio had issues at the beginning but gets better. [Read more…]
With 2016 having come to an end, I thought it would be fun to start 2017 with some laughs about when your company realized it was time to focus on reliability. I have a 3-person team who will vote for the best submission on Friday the winner will receive a signed copy of my book Reliability Centered Maintenance Using RCM Blitz™ [this is a reposting of Doug’s article and let’s see if he has another book to give away.) [Read more…]
I was supposed to be in Boston presenting at “The Disaster Conferences” a few years ago on 28 January 2015. Well, the weather just put us out to 19 March 2015 for the now, rescheduled Boston conference. I guess that they are still feeling the effects of this week’s blizzard, now named “Juno”; that left Boston with over 24 inches of snow.
According to the Weather Channel Winter Storm Juno pounded locations from Long Island to New England with heavy snow, high winds and coastal flooding late Monday into Tuesday. The storm is now winding down. The National Weather Service has dropped all winter storm and blizzard warnings for Juno.
A U-tube manometer is the simplest of the pressure measurement devices. Its name comes from the U-shape formed when the two ends of a flexible tube full of liquid are raised to keep the liquid from coming out the ends. A U-tube manometer is a ‘liquid’ balance.
A spring balance used in the kitchen weighs a load by matching the force produced by the weight of the load with the force produced from the tension of the balance spring. The change in length of the spring is a measure of the load’s weight and is shown on a graduated scale by a pointer attached to the spring. Similarly, a U- tube manometer is used to balance the weight of the liquid in one leg of the ‘U’ against the pressure introduced into the other leg. The difference in height between the two legs of liquid represents the pressure pushing the liquid down one leg and up the other. The height difference is measured on a graduated scale. [Read more…]
“How many samples do we need?” is a very common question. It is one you will receive when planning nearly any kind of reliability testing. It is a great question.
Having too few samples means the results are likely not useful to make a decision. Too many samples improve the results, yet does add unnecessary costs. Getting the right sample size is an exercise starting in statistics and ending with a balance of constraints.
There are six elements to consider when estimating sample size. We will use the success testing formula, a life test with no planned failures, to outline the necessary considerations. [Read more…]
