Your Reliability Engineering Professional Development Site
Statistical process control, SPC, is a set of tools to enable monitoring the stability of a process. SPC is also the first step to checking process capability with measures such as Cpk. Many consider SPC a quality or manufacturing tool. Yet, having and maintaining a stable process is also essential to creating a reliable product. Let me explain why. [Read more…]
In the situation where you have a sample and would like to know if the population represented by the sample has a mean different than some specification, then this is the test for you. Oh, you also know, which is actually rather rare in practice, the actual variance of the population you drew the sample. [Read more…]
Very few reliability decisions are made by reliability engineers. Yet, reliability engineers are asked many questions concerning reliability. How reliable will this design be for customers? How will the system likely fail? How many failures should we expect next month?
How you answer these and the many other questions received does impact the reliability of your product or system. The information provided those that ask reliability related questions, with useful and practical information, can make better decisions which improves reliability performance.
In system modeling and fault tree analysis (FTA) we use a set of similar calculations based on Boolean logic, the AND and OR gate probability calculations. Within FTA, the AND and OR gates are just two of many possible ways to model a system. Within system modeling, often reliability block diagrams (RBD) we model parallel and series elements of a system.
In order to do these basic calculations, we need to consider a few assumptions then proceed to the math.
An immediate purpose is to earn a living. You also may suggest the work is to improve the reliability of the product or system. Reduce downtime, reduce warranty, increase profit, etc.
That is fine for the overall purpose of reliability engineering work, yet in the day to day work, the specific task level, what is the purpose behind what we do?
Yesterday had the chance to review the long list of Design for X topics. Assembly, environment, maintainability, and of course reliability, plus about a dozen other areas of focus. How is a design team to navigate all these different sets of constraints and objectives along with crafting a solution that works?
With a little creativity, you could relate every Design for X topic to reliability. Easier to assembly, fewer assembly errors leading to field failures, for example.
Another way to think about the Design for X space is to consider a superset instead. What are all these design considerations really about? What is common, including the design for reliability topic? With a little thought, it seems clear to me that we all are really considering how to identify and manage risk. [Read more…]
“Speak with Data!” commanded my former general manager. “Let the Data Sing” is an article I wrote long ago. We all have data, often too much data. We like to present based on what the data says. Yet, sometimes the speaking with data is not clear.
If you’ve done the data gathering, the analysis, the summary, all based on the data, how can you best reveal what the data says to enhance your results and recommendations?
Just because you can add a 3D pie chart doesn’t mean you should. Crafting meaningful charts is only one part of the puzzle. You also have to present the data in a clear and meaningful manner. Here is a set of suggestions to consider the next time you are preparing and presenting when data is involved. [Read more…]
Not all risks the same. Some are minor with little consequence, while others are not. Every organization or reliability program facings a plethora of risks and being able to communicate the range of identified risks is helped by using a risk matrix.
The risk matrix is a simple two-dimensional grid that lays out on one access the expected consequence of risk, from minor to catastrophic. The other axis has the likelihood or occurrence of the risk becoming realized, ranging from rare too certain.
The boxes within the grid then contain classifications ranging from low to extreme, which provide a prioritization to address the risk in some fashion. Low-risk items are those with rare occurrences and insignificant consequences. The other end of the spectrum are extreme risks that are almost certain to occur and have catastrophic results. [Read more…]
As reliability professionals, we have a lot to do. Risks to identify, failures to analyze. Plans to draft, numbers to crunch. Meetings, writing, research, and leading fill the day.
The list of tasks that you have before you each day is impressive and daunting. So, how do you focus on what actually requires your attention and not just the tasks that get your attention?
When confronted with a stack of data, do you think about creating a histogram, too? Just tallied the 50th measurement of a new process – just means it’s time to craft a histogram, right?
There isn’t another data analysis tool as versatile. A histogram (bar chart) can deal with count, categorical, and continuous data (technically, the first two graphs would be bar charts). It like a lot of data yet reveals secretes of even smaller sets. A histogram should be on your shortlist of most often graphing tools. [Read more…]
If we set a product reliability goal of 99% reliable over two years in the requirements document, what are we supposed to do tomorrow? On the other hand, if our goal is to write 1,000 pages for the next great novel by the end of the year and we have no pages written so far, well, we should write a page or two tomorrow.
A good goal provides a vision, a measurable milestone, a target. What it lacks is what we do from now till achieving that goal. If the goal is 1,000 pages in 365 days, we may want to set up a process to write at least 3 pages per day.
So, given a reliability goal, what do we do tomorrow, next week, and each week between now and when the goal is due?
In previous work, I’ve written about setting reliability goals, connecting the goal to customer expectations, technical capability, and business needs. Plus, have written about the four (five) parts of a complete goal, including Function(s), Environment, Duration, and Probability (and all four continue to get more difficult as customers expect more).
A well-stated reliability goal provides direction and a measurable target for the entire team. It provides a basis to compare progress and to help frame “is the design reliable enough yet” discussions.
This is all well and good, yet is it enough? [Read more…]
The way we learn, prove our worth, and the nature of work are all changing. Professional societies are struggling to adjust to these changes. Universities and employers likewise are experimenting and exploring new ways to operate.
Over the past few months, I’ve received about a dozen inquiries on how to prepare for the ASQ Certified Reliability Engineer (CRE) exam in order to obtain the ASQ CRE certification. Many also asked if I had a course available. Thus, I decided to run a live course (learn more at the live course page – note: if you see this after the course start date we’ll have a sign up for those interested in future classes). [Read more…]
I often joke that being a reliability engineer makes it difficult to get on an airplane. Yet air travel is by far the safest method of transportation. Maybe I just think about failure too much.
When a project manager views the day’s tasks, she sees timelines, connections, dependencies. When a marketing manager views a product idea, she sees benefits, sales channels, and profits. When a reliability engineer views a prototype, she sees the many ways it can fail.
Underlying how we view the world includes our assumptions, reasoning, and experience. We understand the world around us via the set of filters we use. We form conclusions and make decisions much the same way. Quickly and mostly automatically. [Read more…]
One of my standing searches revealed an article that has shows a nice example of reliability data analysis. The author analyzed the time-to-violent-death of Roman emperors. The article is interesting in a historical sense plus illustrates a few key points for any life data analysis.
The article, “Statistical reliability analysis for the most dangerous occupation: Roman Emperor” by Joseph Homer Saleh takes a look at the 69 Roman emperors and 62% of them that suffered a violent death. The idea of the study was to determine if there is some pattern to the deaths and if the analysis would reveal any insights for those studying the era of the Roman emperors. [Read more…]
Some may argue that just enough reliability data is just the right amount. Too much may lead to confusion, too little doesn’t inform well. The reliability work we do helps others make decisions, and recent work in how humans make decisions may help us prepare and present our results effectively.
If preparing reliability data-based recommendations, consider using less information. Ed O’Brien and Nadav Klein have found decision-makers tend to use much less data or information to make a decision than they think they will need.
If using data and the derived information to make a decision consider the situation carefully to know when to use a structured decision-making approach or to simply go with your gut. Daniel Kahneman and Gary Klein provide some insights and basic guidelines for decision making. [Read more…]