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Find all articles across all article series listed in reverse chronological order.
In the last Article, we explored the use of contour plots and other tools (such as a response optimizer) to help us quickly find solutions to our models. In this article, we will look at the uncertainty in these predictions. We will also discuss model validation to ensure that technical assumptions that are inherent in the modeling process is satisfied. [Read more…]
“It was impossible to get a conversation going, everybody was talking too much.” – Yogi Berra
Based on actual surveys of FMEA team leaders, the most common concern is how to control discussion during team meetings. This article will provide insight into this critical facilitation skill, and is a companion to the previous article in this series: Facilitation Skill #1: – Encouraging Participation.
The floating drilling rig, operating in 6,000 ft. of water, pitched in rough seas 300 miles away from shore as the outer bands of the hurricane’s winds buffeted the drilling location. Per procedure, the crew installed the Storm Packer (SP) in the well to isolate its open wellbore from the ocean before the storm roared across the location – but it failed its pressure test. The crew then installed the back-up SP which passed the pressure test. The well was finally secured, and the 25,000-ton drilling rig was moved out of the hurricane’s path. Because the first SP failed, a 12-hour process took 30 hours to complete, while wind speed and wave heights increased.
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To those following this Series, I will apologize for the front-end redundancy. I am doing so for those that are NOT following the Series and will read these articles independent of each other. If you are following the series (Thank You!) and proceed past the front-end stuff and to the shaft pics below:-)
Abstract. In our last series highlighting the 4 primary Failure Modes (FM) of component failures (erosion, corrosion, fatigue and overload), we discussed how to read fractured surfaces. In this follow up series, we will take a look at tips on how to collect, preserve and examine such failed components.
Let’s face it, we as engineers, maintenance staff, etc. all love technology and the newest gadget. Our industry is never short of these new and shiny techniques, tools, sensors, etc. Now, this new tech can lead to significant improvements in availability, OEE, or uptime. But these are not the silver bullets many claim them to be… especially if the foundational elements are not in place.
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“Hi, I’m Andrew, and I have a weakness for data.”
There, I said it.
I love spreadsheets. I love national statistics. I love primary sources.
I could probably have completed my Master’s dissertation without an extension if I had just accepted that cited quotes were valid instead of looking for all the original sources*. And I don’t need to read the last three years of a company’s annual reports before I have a 20-minute call with them.
Do you really need an assessment? Will it help, or will it create problems?
Conventional consulting approaches begin with detailed assessments to determine your current state of affairs, judge what’s good and bad about it, give it a score, provide a long list of recommendations and then build an improvement strategy based on the outcome. A typical assessment can take up to a couple of weeks plus report generation time. Does it really add the value you might expect?
In the last Article, we learned how to work with predictive models to find solutions that solve for desired responses. We used some basic algebra to solve for solutions and looked at the use of contour plots to quickly visualize many solutions at a glance.
In this article, we further explore the use of contour plots and other tools to help us quickly find solutions to our models. We start by revisiting the battery life DOE example that was discussed in the previous article. The statistical output below shows the coded model that contains only the statistically significant (main and interaction) effects. [Read more…]
I recently attended a conference where I listened to a presentation on Human Performance Improvement (HPI) by Dr. Todd Conklin and other speakers advocating Dr. Conklin’s ‘Learning Team’ approach. This was the first time I had heard Root Cause Analysis (RCA) referred to as ‘old school’ and obsolete. This got me to thinking, given I have been in the RCA business for decades, is what I do for a living…obsolete?
A plan is a road map toward a destination. It provide guidance toward a goal. The idea of a plan is to consider the path forward, the knowledge necessary to acquire, and the decisions along the way.
No plan is perfect other than those that successfully accommodate the successes and setbacks along the way. No plan can anticipate all the information yet to be uncovered, yet it can set a course to deliberately uncover what is necessary to move forward.
An important part of the Reliability Centered Maintenance process, or used as a standalone approach for less critical assets; the Failure Mode Effect Criticality Analysis is vital reliability tool. However, a large percentage of organizations do not take advantage of the tool during the design phase of the asset, or to develop the maintenance strategy once the asset is installed.
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Many maintenance organizations invest in a CMMS/EAM in hopes that it will solve their maintenance management problems, only to discover that the software fails to deliver the desired results. Faulty software might be to blame for some CMMS implementation failures, but more often than not, the problems begin well before the software is even implemented. Here are 7 tips for avoiding CMMS failure:
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Entropy and maintenance are more related than you might think. What happens in maintenance and many operations can be explained with this simple thermodynamic concept. Entropy is a concept that represents chaos and degradation. It occurs naturally in any physical system and will naturally grow (i.e.: the system will become more chaotic) if we don’t do something to arrest its growth. Doing something requires the expenditure of energy, so energy is what counters entropy. Entropy and maintenance are seldom discussed together, we don’t speak of these thermodynamic terms and concepts in everyday language and conversation, but they are at work behind the scenes. For practical purposes, if we want something to remain orderly we need to put some form of energy (effort) into keeping it that way. If we don’t, then nature will steadily and relentlessly increase the state of chaos in which we exist. In maintenance that means moving from proactive (which requires energy) to reactive (which drains it away).
Studies show most projects fail due to poor management of known risks. The known risks on a project are:
The question is how do we manage known risks better? Known risks can be significantly mitigated with application of basic Project Management methods and processes.
This article will address scope. The other known risks will be addressed in separate articles.
At my job interview, the new product development director, an econometrician, explained that he tried to forecast auto parts’ sales using regression. His model was
sales forecast = Σ[b(s)*n(t-s)] + noise; s=1,2,…,t,
where b(s) are regression coefficients to be estimated, n(t-s) are counts of vehicles of age t-s in the neighborhood of auto parts stores. The director admitted to regression analysis problems, because of autocorrelation among the n(t-s) vehicle counts, no pun intended.
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