
Maybe. Great care must be taken if any kind of template of failure mode library is used to complete an RCM analysis.
[Read more…]Your Reliability Engineering Professional Development Site
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 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 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 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.
A tutorial explaining the Physics of Failure method applied to regularly failing roller bearings in a dewatering press. After three years of exhaustive efforts to solve the cause of the bearing failures it was decided to test Physics of Failure Analysis with the aim of finding a lasting answer.
by Joe Anderson Leave a Comment
In the world of industrial and manufacturing enterprises, where precision and reliability are paramount, maintenance leadership stands as the unsung hero. Behind the scenes, these individuals and teams are the custodians of high-quality operations, ensuring that machinery hums with efficiency, downtime is minimized, and standards are not just met but exceeded. Let’s take a closer look at the essence of exemplary maintenance leadership and why it’s the cornerstone of a well-oiled operation.
[Read more…]by Nancy Regan Leave a Comment
Really? I don’t know any organization that has the time, money, and other resources to do so.
[Read more…]by Karl Burnett Leave a Comment
British oak forests provided the wood to build the fleets that fought the Seven Years’ War, American Revolutionary War, French Revolution, and the Napoleonic Wars. New trees had to mature for 80-120 years for shipbuilding. By the early 1800s, three-quarters of British oak forests had been harvested to fight a half century of naval wars. Additionally, a scourge of dry rot reduced the service life of Britain’s main battle ships from the historical 25 years to less than 7 years. Britain had a severe national security problem – the Timber Crisis.
[Read more…]by André-Michel Ferrari Leave a Comment
Operators need to estimate when in the future their equipment will attain their end-of-life state. Obsolescence is another word for equipment end-of life. Once they reach this stage, equipment replacement often leads to significant budgetary expenditures. In addition, if the operation has a large number of a particular equipment types, they could all reach their end-of life at the same time leading to even greater financial impact. In industry jargon, this is known as the “Tsunami” effect. Operators need to aware and prepared in order to avoid this “wave” of consequences. Managing future cash inflow and outflows happens to be crucial financial exercise in any organization. Therefore, end-of-life management in an important financial accounting exercise. However, estimating end of life this is not always as straightforward as it seems.
by Arun Gowtham Leave a Comment
Part 1 of this article is here. And it covered Tips# 1 to 3.
AI projects are R&D initiatives and not simply IT projects. Organizations miss the mark in realizing value from AI as they undertake AI projects just like their network update, or systems infrastructure upgrade.
[Read more…]by James Reyes-Picknell Leave a Comment
About two years ago Jesus Sifonte, CMMI, invited James Reyes-Picknell to be a speaker at a congress that he was holding in San Juan, Puerto Rico. They did not know each other, nor did they even know much about each other. They were recommended to each other by a mutual friend. At the congress, they presented their respective topics and in the evenings shared a few drinks and spoke about maintenance, reliability and asset management. They realized that they both had a shared passion for excellence and we learned about each-others’ experience.
[Read more…]Predictive Maintenance strategy uses condition monitoring techniques to observe plant and equipment health. Based on the equipment’s condition you plan, schedule and perform any necessary maintenance before the breakdown. To use Predictive Maintenance most successfully you must set-up and run your condition monitoring program correctly all the time—predictive maintenance is a never ending strategy that if not done right at every step will still lead to plant and equipment failures. Learn about the many ways your predictive maintenance program will fail and you won’t even know about it!
Keywords: predictive maintenance, condition monitoring, Con Mon
[Read more…]by Arun Gowtham Leave a Comment
There’s a quote one of my previous managers had in his cubicle: “Being a manager is easy, it’s like riding a bike. Except that the bike is on fire, you’re on fire, and everything is on fire”. I chuckle every time I read it and then reflect upon the reality of dealing with project deadlines, deliverables, and performance. Reliability Engineering & Asset Maintenance initiatives have a project management aspect to them.
[Read more…]by Miguel Pengel Leave a Comment
It’s no surprise that the majority of Australia Mining companies don’t leverage the full use of their data to manage their (expensive) assets better. Industry seems to think this is just an adoption issue, where the term “If it ain’t broke don’t fix it” comes to mind. But the reality is that there are far more underlying issues we need to consider why Mining is lagging behind other major industries such as Oil and Gas, Aviation and Power Generation, and yes.. what we do with all our data plays a big part.
[Read more…]