Your Reliability Engineering Professional Development Site
The ASQ CRE Exam is daunting.
While you may desire to become certified, you also know the CRE body of knowledge is broad. The exam is 150 questions in four hours that may tax your ability to achieve a passing score.
There is a lot to know in reliability engineering. Your journey of learning really never ends. [Read more…]
Data and the analyses that use the data can be tricky to manage at best, let along extremely difficult.
In this last post of the series on using the maintenance data you have, Fred and James will answer many of the common questions asked about data and the analyses. [Read more…]
By the time a product fails in the field, the design team is focused on the next design.
They are looking to the future and not looking for field reliability feedback. We know that each failure contains valuable information.
We, as reliability professionals, often work to create as much useful information concerning failure modes and mechanisms as possible. We want to improve the design.
Yet, what happens when the design team has moved on to the next project? When the expertise to effectively make changes to the design to improve product reliability performance is no longer paid to work on the previous design?
What can you do to engage the right people to implement the necessary changes?
Here are a few ideas that I’ve seen used to effectively make good use of field failures to create meaningful field reliability feedback. [Read more…]
When I joined Hewlett-Packard in 1988, I was assigned to a team that was working on a design for manufacturability manual for printed circuit board designers.
Our primary objective was to provide performance and cost information that could be used to guide decisions about different design options.
My favorite project during that time was a predictive model to estimate the manufacturing yield of a PCB design based on a composite “complexity” metric.
Because we were an internal supplier, I was able to look at the actual lot yields for hundreds of active part numbers with known design parameters, so it seemed like a fairly straightforward exercise to experiment with different regression models to find an optimum fit between complexity and yield.
We know that there are no “quick fixes” or “silver bullet solutions” when it comes to improvements in maintenance management.
In my last article, I pointed out that many separate conditions and events must come together properly to achieve “schedule success” – i.e.: the high level of compliance to a schedule of planned work as produced by your planners.
That list of includes: [Read more…]
What you will learn from this article.
Fixing plant and equipment about which you know little is daunting.
Here are a few ideas to help you successfully fault find failed equipment. [Read more…]
Guest Post by James Kline (first posted on CERM ® RISK INSIGHTS – reposted here with permission)
On March 28, 1979, there was a cascading failure in reactor number 2 at Three Mile Island.
This failure allowed large amounts of nuclear reactor coolant to escape. The accident coalesced the anti-nuclear movement and ultimately caused the decline in nuclear plant construction in the United States. [Read more…]
As seen in the previous articles, you can easily use the data you already have to conduct a meaningful analysis. This includes Weibull, Crow-AMSAA or a Mean Cumulative Function analysis.
Digging into a well manage dataset promises to reveal insights, trends, and patterns that will help improve the line, process, or plant.
Creating a plot or calculating summaries is pretty easy with today’s tools. Yet, are you doing the right analysis or are the various assumptions valid?
One critical step in the data analysis process is making sure you are doing a valid and appropriate analysis. [Read more…]
I see many organizations that feel a great sense of victory when they solve a current issue that has been plaguing their product.
It may be a field issue that took a product that was performing flawlessly to a steeply increasing failure rate “out of nowhere”.
Customers are angry, management is angry, everyone is angry and afraid. It seems to go on forever and the end is unknown. Then just as suddenly as the issue arose, a solution is found that puts the universe back in order.
The team celebrates, goes back to the day to day tasks that keep the machine running and there is a sense of calm. But the unspoken nervousness of waiting for the next issue to pop up is there.
There is nothing to do but just wait. A new one will arise, maybe tomorrow, maybe in six months, maybe next year.
“But that is the way it is and there isn’t much that can be done about it”.
That is the mindset of a Mid-maturity reliability culture. A High-maturity reliability culture doesn’t have to just sit and wait for the next “gremlin strike”.
The reason is that not only do they know what caused the last issue, they also know why they currently aren’t having any issues. It is a subtle but big difference.
The High-maturity reliability culture has in place an ongoing program that studies the variabilities that can occur in their product manufacturing and usage. This program uses methods such as specialized testing and analysis that supports mitigations in design, manufacturing, and product usage.
Now the organization isn’t just not having issues, they know why they aren’t having issues!
But in reality, it’s just pitfalls we fall into because we simply aren’t willing to study the road up ahead.
5 Steps to Building a Reliability Culture (article)
Purpose of a Reliability Program (article)
How to Assess Your Reliability Program (article)
The definition of reliability includes four elements.
One of them is the intended environment where the device or system will experience a range of stresses.
The knowledge of where and how an item will operate enables:
A Petri net graph is a depiction of a system using a symbolic language.
The modeling permits the analysis of complex systems or networks of systems.
It is possible to include elements of the system that are neither function or failed. In other words, it permits modeling a system when one or more of the elements are in a degraded state or under repair.
Petri net modeling is useful when the repair/restore times are long compared to operating times, as reliability block diagrams and fault tree analysis approach assume short or insignificant repair times, in most cases. [Read more…]
Today’s world contains a myriad of choices for instant gratification.
Regardless of our age, we have grown used to getting what we want, when we want it, and how we want it limited only by our ability and willingness to pay for it. [Read more…]
In a recent post, I wrote about suppliers who claim to be committed to quality but may not actually behave that way.
Before getting too carried away with improving quality in the supply chain, it’s probably a good idea to understand your own company’s commitment to quality, although I see nothing wrong with holding your suppliers to a higher standard.
It may seem impressive when businesses highlight quality as a core value, something that’s published on their website and displayed on their walls, but is that just for show?
Some quality paradigms are expensive.
Quality is a mindset! When a wise man is given the chance to buy quality items he does so because quality pays for itself.
A quality item lasts longer, runs better and looks good when others fade. To change the way you think about quality takes a lot of experience with using poorer options. When you are sitting down with your head in your hands wondering what can be done to get costs down, to get production up and how you are going to hit the key performance indicators, remember the importance of quality equipment, quality systems, quality training and your quality mindset!
Keywords: quality control [Read more…]
Guest Post by John Ayers (first posted on CERM ® RISK INSIGHTS – reposted here with permission)
Reliability is designed into a product. Quality is built into a product. Poor reliability is long term, difficult and expensive to rectify because it is woven into the fabric of the product.
Quality is a relative short term problem because once the badly written procedure, non-compliant material or poor workmanship is identified, it usually can be fixed relatively quickly with minimal impact to the program. [Read more…]
