Your Reliability Engineering Professional Development Site
All articles listed in reverse chronological order.
The last post in this series dealt with management in design and the influence that leadership in design and product development has on the product reliability process. This post will turn to quality and reliability (Q & R) engineers, again looking at the role of that position in relation to product reliability. The next post in this series will take a look at the role of quality and reliability managers. [Read more…]
The chi-square (Χ2) test provides the basis for the second case of hypothesis tests for variances. In this case, we want to compare observed and expected frequencies, or counts, of outcomes when there is no defined variance. In other words, we are working with attribute data. [Read more…]
The last post in this series dealt with the design team, and the influence that team has on the product reliability process. This post will turn to product development and design managers, again looking at the role of that position in relation to product reliability. The series of posts on reliability participants will continue after today’s post with the roles of quality and reliability engineers and managers.
There are Alternatives to using MTBFJust a quick note this week to pass along a message from Michael. He’s conducted a bit of research on Mean Cumulative Function and found a few papers. And, according to Michael’s request I’m working on a short introductory tutorial for those new to MCF. [Read more…] |
Hypothesis testing of data may include two populations that have un-equal standard deviations. The t-test for differences considered in a previous post used the assumption of equal variances to pool the variance value. In this test, we want to consider if one population is different in some way than the other and we use the samples from each population directly even if the population have difference variances. [Read more…]
An effective product reliability process requires a strong team, at every level. The team of employees within an organization that participate and impact product reliability is a vast and widespread group of people. They include members of the design team, design managers, quality and reliability engineers and managers, procurement engineers and managers, warranty managers, failure analysis specialists, members of the marketing and sales staff, members of the finance and manufacturing teams, and field service and call center staffs. [Read more…]
Last week I discussed how the aspects of the structure of an organization relate to product reliability. Related to that, this week the discussion will remain on the level of a reliability organization but will look at something a bit more intangible – how decision-making policy and practice affects product reliability. [Read more…]
Both organizational structure and decision-making policies have an impact on improving product reliability. The former is more quantifiable whereas the latter involves more intangible subtleties. First, in this post, I’ll discuss the connection between organizational structure and reliability, and in a follow-up post, I’ll address reliability and decision focus, still within the structure of an organization. [Read more…]
MTBF for electronics life entitlement measurements is a meaningless term. It says nothing about the distribution of failures or the cause of failures and is only valid for a constant failure rate, which almost never occurs in the real world. It is a term that should be eliminated along with reliability predictions of electronics systems with no moving parts. [Read more…]
Hypothesis testing of paired data may include two populations that have the equal standard deviations. The t-test for differences considered in a previous post used the standard deviation of the differences. In this test, we want to consider if one population is different in some way than the other and we use the samples from each population directly. [Read more…]
Hypothesis testing previously discussed (link to past posts) generally considered samples from two populations. Maybe the experiments explored design changes, different component vendors, or two groups of customers. Occasionally you may find data that has some relationship between the samples, or where the samples are from the same population. Paired (or matched) data involves samples that are related in some meaningful way. [Read more…]
Reliability professionals today have a challenge. Engineering and operations staff members are taught to think for themselves, to make decisions, to get things done. The entire staff is highly educated, motivated and willing to lead a team or organization to results. In order to be effective as a reliability profession, we have to engage those independent and fast moving individuals. We have to compel others to listen to and understand reliability predictions, risk assessments and models. If they listen and act on the information we provide, they then may fully consider the impact of decisions on reliability performance. [Read more…]
Let’s Create a NoMTBF Non-User’s Group Today
Those that read this blog regularly know that I, along with many others, have some passion for the eradication of MTBF from common use. We make arguments, create examples, describe the errors and encourage using other methods. The campaign seems to be going along well. Yet MTBF is still in use – in standards, in certifications, and even taught by those that should really know better. We’re making progress, but there is much more yet to do. [Read more…]
A product reliability program is a process. Like any process, it has inputs and outputs, generally some form of an objective, and feedback. Furthermore, the process may or may not be controlled, or even a conscious part of the organization. Reliability may just happen, good or bad. Results may or may not be known or understood.
In some organizations, the reliability program may be highly structured with required activities at each stage along the product lifecycle. In other organizations, reliability is considered as a set of tests (e.g. environmental or safety compliance). In some organizations, reliability is effectively a part of everyone’s role. [Read more…]
Statistics is the language of variation. Everything varies, and we use variance (σ2) to describe the spread of the data. For any experimental work aimed at making improvements, whether in the design, manufacturing process or field performance, there are two ways to make improvements. Move the center of the distribution, or reduce the spread of the data. [Read more…]