Your Reliability Engineering Professional Development Site
We have statistics to describe the variation that occurs in our world. Statistics is the language of variation.
If each of your produced products were identical in every way to all products produced, with no variability, we wouldn’t be concerned with the effect of variation on the performance and reliability of our designs.
Yet, variation does happen and we have a range of tools to identify and minimize the naturally occurring variation. [Read more…]
Fault Tree Analysis (FTA) provides a means to logically and graphically display the paths to failure for a system or component. One way to manage a complex system is to start with a reliability block diagram (RBD). Then create a fault tree for each block in the RBD.
Whether a single block or a top level fault for a system the basic process to create a fault tree follows a basic pattern. [Read more…]
Is a logical, graphical diagram that organizes the possible element failures and combination of failures that lead to the top level fault being studied.
The converse, the success tree analysis, starts with the successful operation of a system, for example, and examines in a logical, graphical manner all the elements and combinations that have to work successfully.
With every product, there are numerous ways it can fail. Some more likely and possible than others. The FTA permits a team to think through and organize the sequences or patterns of faults that have to occur to cause a specific top level fault. The top level fault may be a specific type of failure, say the car will not start. Or it may be focused on a serious safety related failure, such as the starter motor overheats starting a fire. [Read more…]
I’ve recently received a couple of notes from individuals looking at starting a career in reliability engineering. One is a student looking at a career path, another a working engineer with an interesting in reliability.
Both asked how to land a position given no reliability engineering experience.
Hum, I don’t know anyone that had reliability engineering experience before they got started working in reliability engineering. Not counting taking apart the family toaster and trying to repair it before Mom got home as a kid.
When time is short and you just want a rough estimate of the standard deviation, turn to the range rule to quickly estimate the standard deviation value.
The standard deviation is approximately equal to the range of the data divided by 4. That’s it, simple. [Read more…]
There are times when you do not have a computer available and would like to visualize the distribution of a small set of data. With paper and pencil, you can create a representation that is similar to a probability density function plot. [Read more…]
The easy answer is very often. Each time you want to know how long a product will operate. The accompanying question on how well the estimate will match actual performance makes the real answer more difficult.
We regularly and intuitively do reliability predictions all the time. When starting a car at the beginning of a trip, we estimate the ability of the vehicle to complete the journey. When we purchase a phone, we expect it to operate for at least two years (your expectations may differ).
During the design process, we may have formal or informal useful life expectations. It is not knowing if our decisions related to the design will fulfill the lifetime expectations that leads to the desire to know how well the resulting system will operate. We also may need to estimate warranty or maintenance costs, thus knowing what is likely to fail becomes important.
In general, knowing how long something will operate without failure provides the feedback we need to create a viable system that meets our business and customer reliability expectations.
In short, we do reliability predictions regularly to gauge is we are making good decisions. [Read more…]
This nonparametric hypothesis test tests the equality of population medians. While not as powerful as the Kruskal-Wallis Test, it is useful for smaller sample sizes, when there are a few outliers or errors in the data as it focuses only on the median value. [Read more…]
Failure modes and effect analysis is a tool to identify potential failures and prioritize based on severity, occurrence, and detection. I like to describe FMEA as an organized brainstorm. You probably have some experience with FMEA.
In some industries, there is a high expectation or mandate to do an FMEA study. In some industries FMEA maybe just another tool to consider using during various stages of the product or asset lifecycle.
In my opinion, FMEA should be a part of your project plan when it is likely to add value.
Value in the sense that the organization will receive an adequate benefit based on the investment to conduct the FMEA study. [Read more…]
One of the considerations when planning maintenance is the timing of the replacement of parts of the system. In some cases it makes sense to wait for the item to fail, sometimes, it makes sense to place it on an interval or set an amount of operating time, and yet in others, we can measure indicators that a replacement is necessary.
In order to make these decisions, we need data.
According to O’Connor and Kleyner (Practical Reliability Engineering, 5th Ed.), we need specific information for each part. The following list outlines the recommended information. [Read more…]
HALT (highly accelerated life testing) is a method to reveal product weaknesses. Design prototypes experience the step-stress application of relevant stresses until failures appear.
The intent is to find design or process related weaknesses early in the design process thus providing time to economically address the issue. Using a build-test-fix approach does improve a product’s robustness and reliability.
Being a useful tool, should you conduct HALT on every project? It seems that revealing weaknesses is certainly useful. [Read more…]
In statistics and reliability, we use distributions to describe time to failure patterns. The four functions commonly used in reliability engineering include
We often use terms like, mean, variance, skewness, and kurtosis to describe distributions (along with shape, scale, and location). The mean is defined as the use of a moment generating function. First though let’s first back up to the concept of center of gravity (cog) from mechanics. [Read more…]
We see the terms ‘reliable’ and ‘reliability’ in daily advertising, business names and in casual conversations on a regular basis. Reliability has meaning and importance in our society. Product and brand reputations are made or broken by their product reliability performance.
Reliability testing and data collection is a messy business.
We rarely receive perfect data where all units involved have a precise time to failure record. Sometimes we do not the precise time to failure information or some of the units are still operating.
The data is censored.
There are a few common types of censoring, each of which has statistical techniques to appropriately account for the unknown elements. [Read more…]
“How do you know so much about our program?” was a question the quality manager asked after reading the assessment report. The assessment took one day with eight interviews.
The reliability that results is going to happen whether or not the team designing the product or production line deliberately use reliability engineering tools or not. The elements of a product or system will respond to the environment and either work or fail.
While working at Hewlett-Packard I had the opportunity to conduct reliability program assessment of about 50 product divisions. One hypothesis related the number of reliability tasks the team actively used would correlate to their warranty expenses.
That worked to a point. [Read more…]