This is the first annual survey to find what you recommend for those preparing for the ASQ CRE exam.
See the full list of reliability references for the CRE exam, for reliability and maintenance engineers at Accendo Reliability. [Read more…]
Your Reliability Engineering Professional Development Site
by Fred Schenkelberg Leave a Comment
This is the first annual survey to find what you recommend for those preparing for the ASQ CRE exam.
See the full list of reliability references for the CRE exam, for reliability and maintenance engineers at Accendo Reliability. [Read more…]
At first MTBF seems like a commonly used and useful measure of reliability. Trained as a statistician and understanding the use of the expected value that MTBF represented, I thought, ‘cool, this is useful’.
Then the discussions with engineers, technical sales folks and other professionals about reliability using MTBF started. And the awareness that not everyone, and at times it seems very few, truly understood MTBF and how to properly use the measure.
by Carl S. Carlson Leave a Comment
“I never teach my pupils. I only attempt to provide the conditions in which they can learn.” Albert Einstein
Reliability engineers, FMEA team leaders, and other quality and reliability professionals are often called upon to teach the principles of reliability or FMEA. This article is the beginning of a new series called “The principles of effective teaching.”
If you want to convey knowledge to another person, you are teaching. If you want to learn from another person who is teaching, you will benefit from learning these principles.
by Sanjeev Saraf Leave a Comment
Here’s a touching ad from WorkSafe, an Australian safety agency, that makes us realize the importance of safety at work.
[Read more…]by André-Michel Ferrari Leave a Comment
Maintenance and Reliability practitioners often need to find quick methods to estimate life distributions in order to get some urgent answers to a customer. The tempting solution and easy way out to this is to refer to a handbook or publication out there. Also known as “Reliability Data” handbooks. These publications would have “ready to go” life distributions. However, this can come with multiple pitfalls listed as follows.
[Read more…]by Mike Sondalini Leave a Comment
A certain Operations Manager started inventing production KPIs in order to measure reliability from a production perspective. So he got together his colleagues and they came up with this formula.
Reliability = Good Production / (Net Production Hours + Nominal Speed)
When asked to define ‘good production’, I was told that it was the saleable production remaining after losses such as speed losses, first time quality, downtime, change overs, etc. were taken off.
After 3 years of running TPM (Total Productive Maintenance) across 3 factories, they made the following observations. The mean time between failure (MTBF) of equipment in all 3 factories increased. The production volumes increased. However the Reliability remained flat. How can this be? Something is not right. Is the above formula incorrect?
Is there a better way to calculate Reliability from a production perspective?
[Read more…]by Greg Hutchins Leave a Comment
This article is the eighth of fourteen parts to our risk management series. The series will be taking a look at the risk management guidelines under the ISO 31000 Standard to help you better understand them and how they relate to your own risk management activities. In doing so, we’ll be walking through the core aspects of the Standard and giving you practical guidance on how to implement it.
In previous articles we’ve looked at the core elements of the risk management framework, as well as the role of leadership and commitment, integration, design, implementation, evaluation and improvement more specifically. In this article, we’ll be moving away from the framework and instead introducing you to the risk management process.
by Nancy Regan Leave a Comment
Maybe. Great care must be taken if any kind of template of failure mode library is used to complete an RCM analysis.
[Read more…]by Sanjeev Saraf Leave a Comment
Below is summary (annualized average) of 20-year pipeline incident data from 1990-2009. [Source: Pipeline and Hazardous Material Safety Administration,PHMSA]
[Read more…]How much should you pay attention to readability scores generated from tools like the Flesch-Kincaid Readability Formula? As a technical professional, you probably should pay careful attention. But remember, while improving the Flesch-Kincaid score is important for accessibility and readability, balancing this with accurately conveying the technical information is essential.
Readability is a quality of your business writing. People will be able to understand your sentences easily if your text’s readability is high. If the readability is low, people still might understand what you’re saying, but reading your text is likely a draining experience, but people may still understand it.
Big words and complex sentences aren’t bad. Using too many of them demands much more concentration from your reader. Big words and complex sentences are also harder if someone’s first language is not yours or the reader has some form of visual or hearing impairment.
[Read more…]by André-Michel Ferrari 2 Comments
Barringer Process Reliability (BPR) was developed by Paul H. Barringer, a fellow reliability engineer “extraordinaire” and an outstanding mentor for myself and countless others in this field of practice. BPR highlights operational issues. Not addressed and mitigated, those could have significant revenue impacts. A BPR analysis uses the Weibull probability plot which happens to be a very well-known tool in the field of Reliability Engineering. On one side of a sheet of paper only, the BPR plot can tell the true “story” on the operation.
[Read more…]by Greg Hutchins Leave a Comment
“Too many cooks spoil the broth” goes the Elizabethan poet George Gascoigne’s proverb. Although only written down in circa 1575 it had probably been around for many years beforehand. It is still used today and, far from being archaic, it’s become more and more relevant despite mankind’s predilection towards efficiency and effectiveness. But why?
[Read more…]by James Reyes-Picknell Leave a Comment
James Reyes-Picknell
Despite its well-documented successes, Reliability Centered Maintenance (RCM) has always drawn a lot of discussion and controversy. Much of it is because of a lack of understanding and “myths” generated to discredit RCM as a viable business solution. Here we attempt to fill in some of those gaps in understanding and debunk some of the myths.
[Read more…]by Mike Sondalini Leave a Comment
To help select which work orders to do first in situations of resource shortage many CMMS provide calculations for maintenance work order priority. Deciding maintenance work priority is a risk decision. The presence of risk totally changes the way to allocate maintenance job priority if you want to compare situations equally1. When you work with risk you cannot use a linear priority scale. Using linear priority ranking gives the wrong order of importance for doing maintenance work.
[Read more…]by Sanjeev Saraf Leave a Comment
EPA announced that the promulgation of National Emission Standards for Hazardous Air Pollutants (NESHAP) for industrial, commercial, and institutional boilers and process heaters is postponed to January 16, 2011. The regulation, commonly referred to as Boiler MACT, will affect approx. 13,500 boilers at various facilities deemed to be major sources of hazardous air pollutants (HAPs).
[Read more…]by Miguel Pengel Leave a Comment
In a previous article we covered how to perform a detailed Weibull Analysis in Excel. The outputs from a Weibull analysis are important because we can use them for a variety of Reliability calculations such as when to most economically maintain assets.
A common mistake we see made is Reliability Engineers determining the optimal maintenance interval as the MTBF. This is incorrect as it assumes that you will have failed approximately 50-60% of the components before you have maintenance performed on them. (Sounds ridiculous when you say it that way right!?)
The reality is that optimal maintenance intervals are a cost optimisation problem, which is dependent on the increasing failure probability with age, the cost of downtime and the cost of planned maintenance at each selected interval.
Since we’ve already covered the Weibull analysis in a previous article, let’s start this process of assuming we already have the Beta and Eta value of our component we’re analysing. It is important to note that when conducting the optimal maintenance interval calculation, we do this at the component level, not the system level- just like in the Weibull analysis.
During the course of this article we will be working through an example of a hydraulic cylinder on a mining excavator that we need to calculate the optimal maintenance interval on.
We can assume that if one cylinder out of the four cylinders fails, it renders the machine unusable.
The Beta value is: 2.0303 and the Eta value is 7848.9
Calculated MTBF= 6967 Hrs (This will be a discussion point later as to why we don’t use this metric).
Determine the cost model:
The cost model for a maintenance interval optimisation is relatively straight forward- we have two types of cost:
How these two will integrate in the model we will show below:
In the above image, we have laid out the cost of normal maintenance as well as that of corrective maintenance. The key difference here is that the cost of corrective maintenance includes the cost of downtime per hour. It is important the calculate this value on the principle of the Theory of Constraints. This means that the cost of downtime for a Mining excavator in a coal mine isn’t simply the tonnes/hr dig rate multiplied by the net profit per tonne of coal. The calculated number must take into account the main constraint in the value chain.
Let’s say for our example our calculated cost of downtime is $5,000/hr. So why is it that during planned maintenance we don’t add the cost of downtime?
Production loss on planned maintenance here is considered to be $0. This is because when production sets the Budget for machine availability, this includes the maintenance of the machine. Thus theoreticaly, while the machine is scheduled for Preventative Maintenance, it wasn’t required to produce any direct/indirect income. However if the machine was planned to operate and breaks down, it incurs a production loss as the plan may have not included any backups, and the budget is missed.
In the real world, the cost of labour and parts in a breakdown situation is usually higher than in a planned sense. How could that be so when technically the same parts and labour are required to complete the repair in each instance?
While it is true that both planned and unplanned maintenance may involve the same repair work, there are several factors that contribute to the difference in costs between the two:
Calculate the cost per unit time for each potential maintenance interval:
As mentioned earlier, optimal maintenance intervals are really an optimisation problem, where we want the minimum total cost per operating hour. This is a balance between the cost of corrective maintenance at earlier intervals vs the higher cost of unplanned maintenance at higher intervals.
The most important aspect of this calculation is the Failure rate function from the Weibull analysis. Here we predict the probability of failure at each stage and assign the Corrective maintenance cost.
\[\text{Failure Rate (F(t))} = (\frac{\beta}{\eta})\times(\frac{t}{\eta})^{(\beta-1)}\]The cost per unit time is given by the below formula:
\[\text{Cost per Unit Time} = \frac{\text{Cost of PM + Cost of CM}}{T}\] \[=\frac{\text{Cost of PM}+ (\lambda(t)\times\text{Cost of CM}\times t)}{T}\]In a practical sense, how we do this in Excel is by creating 3 columns beside out costing calculations and Weibull parameters:
Let’s take a step back and really understand what this is calculating…
Remember that for each maintenance interval I select, I will of course incur the cost of performing the planned task. The failure rate factors in that even though the part may be in its infancy stage, there is still a possibility (albeit small) for a failure, which will then incur the unplanned maintenance cost. The total cost of unplanned maintenance is fractionalized by this failure rate.
As the maintenance interval increases, the failure rate will as well, and although the planned maintenance cost/unit time is decreasing, the cost of unplanned maintenance /unit time is increasing. When combining them together, there will be a minima of which in practice is the best Risk vs Economic Reward – The optimal maintenance interval!
So now we can see that the optimal maintenance interval of 3,500 hours (which in this case was a component replacement due to the high β value) is significantly lower than the MTBF we calculated of 6,967 Hours.
According to this specific component’s Survival Function, if we put MTBF as our maintenance interval we would have failed 55% of all our cylinders!!
So if MTBF is 6976 Hrs, shouldn’t 50% of cylinders have failed instead of 55%?
When it comes to the Weibull distribution, MTBF doesn’t always provide an accurate representation of the expected life of a component. This discrepancy arises from the fact that the Weibull distribution can be a highly skewed distribution, depending on its shape parameter (β).
In the case where the shape parameter β > 1, the distribution is right-skewed, meaning that the majority of failures occur later in the component’s life, with some components lasting significantly longer. In this situation, the MTBF is higher than the time by which a large proportion of components have failed.
Remember, the goal is to make the most economical decision in terms of maintenance strategy. This will involve replacing parts that have life in them in order the reduce the chance an unplanned event will impair the asset and prevent it from generating revenue. But is important to note here that for this to be valid you must accurately model the true cost of downtime for the asset. These data driven decisions are important in FMECAS’s.
For those wishing to download the worked version of the Excel sheet, you can download them after filling in the form on the page HERE.
by Greg Hutchins Leave a Comment
The current state of the quality profession is affected by shifting business infrastructures and changing definitions of brand quality.
Businesses need to react and change against external pressures like increased frequency of consumer communications, the availability of big data, expanding regulations and standards, and the expectations to innovate quickly. The quality profession is at risk of losing its effectiveness in the overall business operations if it does not proactively change with the business.
[Read more…]