MTBF

The Mean Time Between Failure (MTBF) is a well-known reliability metric, It’s widely used despite the fact of lacking the ability to assess the change in the reliability, this is one of many issues faced while using the MTBF.

For further information about the MTBF issues check this article.

Weibull

Weibull analysis is a parametric analysis that gives two parameters \(\beta\) and \(\eta\) so-called shape (slope) and scale (characteristic life) respectively. The Weibull analysis is a powerful reliability distribution used to model degradation process (e.g. fatigue, corrosion, abrasion, etc). and since it’s used to model failure mode this imply it’s weakness of modeling mixed failure modes not to mention a whole equipment or a system.

Step 1: Create a query

I’ll assume you are using some kind of CMMS system (SAP in my case) where all of the failure records exist, now you might be lucky enough to have an SQL interface or you are familiar with the system to be able to get a table looking like the one below.

Step 2: Clean your data

I’ll suggest to clean your data programmatically and avoid manually cleaning since this can take ages, minimize the manual cleaning as much as you can. this can be done in your SQL code:-

• Check the activity: you may want to limit your data to (repair and replace) something like calibration or cleaning doesn’t sound like a failure of the function.

• Adding PM01 notifications: Some of the PM01 are valid failure of function you might want to add these after the limiting the activity in the point before, this is depending on how your people treating their CMMS in some cases you might end up with 0 PM02(break down) and that not because you have reliable assets!

• Filter the repairable parts: When there are no parts listed in the parts field it can be wise to remove this entry, anyhow this again depending on how the people using the system, is it usual to have the empty field ? or that mean to replacement is done?

• Text mining: based on the patterns you see in work orders you can set some filtration rules on the work order text(e.g. “paint”, “painting”,“lighting”,“cleaning” when people used to raise notification on equipment instead of functional location). this is the biggest of the field of improvement for the data quality. If you wait for the data to be clean by itself then be ready to take blind decisions

Step 3: Export the result

After you have established a query that clean your data now you can export your data to a spreadsheet. in future, you should start from this point when you want to update your result unless you wanted to adjust your filtration.

Step 4: Create your RGA code

In my case I’m using R code, the could is simple enough for the beginner to understand. My excel sheet name is “example” and I want to end up with 2 columns “Event#” and “t” for cumulative time.

1. order your data based on the event date.
2. t = event date - installation date

that was very simple.

Step 5: Test your code

Having your code now you can test result, I suggest to go the link in fig 1.1 and check one of the examples and see if your data matching the result. herewith a starter code for that.

example$`log(t)` <- log(example$t)
B_hat <- length(example$t)/(length(example$t) * (max(example$`log(t)`)) - (sum(example$`log(t)`)))
L_hat <- length(example$t)/(max(example$t)^B_hat)
L_hat_inst <- L_hat * B_hat * ((max(example$t))^(B_hat - 1))
example$L_hat_inst <- L_hat * B_hat * (example$t)^(B_hat - 1)
example$MTBF <- 1/example$L_hat_inst

Step 6: Make your code massive

Now we’ll use what called “functional programming” will insert a function inside another function. the first function is “sapply” what want to do is to take the list with multiple nodes each node represents a table and every table represent a single RCM system. The second function is your RGA function…so you got the picture…yes we’ll apply the RGA function on all the RCM system in this and that all folks.