Your Reliability Engineering Professional Development Site
Find all articles across all article series listed in reverse chronological order.
Proper control chart selection is critical to realizing the benefits of Statistical Process Control. Many factors should be considered when choosing a control chart for a given application. These include:
When I was working at a university, I was involved in a conversation with a representative of an energy company. He was having all manners of problems with a valve. It was failing too often. He wanted us to look at what we could do in terms of optimizing the preventive maintenance (PM) or servicing regime to hopefully fix these problems. But … there was a catch.
He had heard about ‘deep learning’ and ‘artificial intelligence’ from another university. And he wanted some of it. [Read more…]
No matter where we work, we will experience failures or ‘undesirable outcomes’ of some kind. As long as we work with other humans, this will indeed be the case. These failures may surface in the form of production delays, injuries, customer complaints, missed deadlines, lost profits, legal claims and the like. [Read more…]
While in the US Army many years ago, I took command of an artillery battery. My first day included sitting down with my boss, the battalion commander. I’ve used the advice he gave me every day since.
He said that I needed to make decisions. It’s great when they are good decisions, you can learn from decisions that don’t work out well. He also said the only thing not allowed is not making a decision.
I learned that decision-making is a process and that I could get better at it with practice and feedback, and I did. [Read more…]
How often does your facility run out of raw materials? Chances are it is not very often, if ever. Why is this? It may be because the organization has invested heavily in gathering the right data, analyzing and developing contracts for the materials. This prior work ensures a steady supply of materials.
So why is it that within the same organization there is virtually no data to support the spare parts? Not having a spare part can dramatically impact an organization in the same way as not having raw materials. The result is no production. [Read more…]
It’s easy to become overwhelmed with the vast amount of terminology used to describe maintenance concepts. Even those who are familiar with various maintenance management terms know there is a lack of consistency among sources. This guide to maintenance management terminology serves to help teams better understand the differences between key phrases used in the industry. Browse through our maintenance glossary below. [Read more…]
Are we on the right track with the right train? Your job is to improve reliability and you have a plan. It focuses on bad-actors, having the right data, cleaning up some parts data that is known to be causing delays in work execution, a bit of training in reliability methods, and your adding engineers. You are certainly on the right track with your plan and the actions you will take should indeed make some improvement. But are you on the right track? [Read more…]
The point of risk management is to understand and react to the threats and opportunities that might affect your business. The problem is that risk management can often become dislocated from the mainstream business processes. Instead of being integrated into the organization, risk management takes place in a parallel but separate workstream: one that decision-makers dip into occasionally but generally look at as a specialized, technical process. [Read more…]
This strange word andragogy was popularized in the early 1970’s by educational researcher, Malcolm Knowles. It is etymologically rooted in the Greek language from two words “aner”, which means “man” and “agogos”, which means “to lead”. Fused together, andragogy means “leading men”, or to paraphrase, leading or educating adults. Andragogy is often contrasted with pedagogy, typically referring to the education of children. [Read more…]
Thems that die’ll be the lucky ones.
~ Robert Louis Stevenson
This post is a continuation of the series “Is the HALT a Life Test or not?”
Test two samples and demonstrate reliability R=0,99 over a lifetime with CL=0,99. This is real. But what payment will we pay, besides the duration of the test on triple lifetime, which can be significantly accelerated by the way? [Read more…]
Statistically based DOE provides several advantages over more simplistic approaches such “one-factor-at-a-time” experimentation. These advantages include:
This article will explore the first two advantages in a bit more detail. The second two advantages will be discussed in the next article post. [Read more…]
A histogram is a graphical representation of a set of data. It is useful to visually inspect data for its range, distribution, location, scale, skewness, etc. There are many uses for histogram, there you should know how to create one.
Let’s explore a set of data and create default histograms using a variety of methods. If you have a way to create a histogram using some other method or software package please send it over and we’ll add it to the article. [Read more…]
Myron Tribus’ UCLA Statistical Thermodynamics class introduced me to entropy, -SUM[p(t)ln(p(t))]. (p(t) is the probability of state t of a system.) Professor Tribus later advocated maximum-entropy reliability estimation, because that “…best represents the current state of knowledge about a system…” [Principle of maximum entropy – Wikipedia] Caution! This article contains statistical neurohazards.
Claude Shannon wrote that entropy (log base 2) represents information bits, “…an absolute mathematical limit on how well data from the source can be losslessly compressed onto a perfectly noiseless channel.” [Beirlant et al.]
Maximum likelihood estimation is one way to estimate reliability from data. It maximizes the probability density function of observed data, PRODUCT[p(t)], e.g., for observed failures at ages t. It is equivalent to maximize -SUM[ln(p(t)]. Maximum entropy reliability estimation maximizes entropy -SUM[p(t)ln(p(t)]. That’s same as maximizing the expected value, -SUM[p(t)ln(p(t)], of the log likelihood -ln(p(t). Fine, if you have life data, ages at failures t censored or not. [Read more…]
In an era of rapidly advancing technology, the need for training to keep up is imperative. But training alone is not the panacea to a facility’s problems. Management’s must be aware that the environment in which their people work, will either progress or obstruct any training that is provided to them. We will refer to our need to address the human element, as the “soft side” of technology. It is estimated that over $60B U.S. is spent on industrial training a year and that only 20% of that training investment is ever applied. Are we getting our money’s worth from our training investment? If not, here are some things to consider when training our personnel and using their valuable time from the field. [Read more…]
If you have ever been involved in manufacturing or quality-related conversations, you may have heard of ‘Statistical Process Control’ or SPC. And if you Google SPC you will find a bunch of ‘textbooky’ definitions which are likely going to make you run away and never think of it again.
But you shouldn’t. [Read more…]
