Determining the expected life of equipment can be difficult. Expected life vs actual equipment life is used when determining total cost of ownership. I have read several root cause analyses that checked “full life wear out” for the physical cause. But full life and equipment wear out are not necessarily the same thing.
True full life calculations require a lot of data and analysis. ISO 281 details the calculations for rolling bearings. This, is just for one component. Equipment or system life is usually more relevant. The life of these has 4 factors
Let’s examine ways that the average maintenance and reliability group can determine life expectancy of their equipment.
First, let’s go back the point about full life, vs equipment wear out. Let’s say my brother and I buy identical cars on the same day. We both have similar driving requirements. We put about 100 miles per week on the vehicles. We each fill fuel around the time the refuel light clicks on. That is where our similarities end.
I ensure the oil and filter are changed every 5,000 miles. The fluids and air filter are checked at the time of service. I have the tires rotated at oil changes, and monitor tread wear. The brakes are checked when the tires are rotated and I have the pads changed before they completely wear out. I wash the car at least once/month. More often in the winter. I vacuum and wipe down the interior every time I wash the car.
After 10 years, the check engine light on my brother’s vehicle comes on, and he brings it in for service. The tech says he needs an engine rebuild; which will cost more than the vehicle is worth. The car is worn out. My car is fine and over 90% of similar vehicles (10 years / 50,000 miles) are all perfectly road worthy, no major repairs. In fact, the average vehicle is valued at or above 30% of its original value. Therefore, even though one vehicle is worn out, it did not live its full life. This is determined using the definition that full life is when 90% of equipment is still in working condition.
So, in order to determine if equipment is attaining full life, it is necessary to determine when 90% of like equipment is, or reasonable should be still operating. Determining the equipment life of a large population is much easier than a small population. So using the tools and information available how to determine life expectancy.
Start with the OEM or design information. When the equipment was selected, a life expectancy was used in the capital requisition. That number should be the starting point.
Next, mine your CMMS data. Do not use built in MTBF calculators as they have trouble calculating from null values. That means that CMMS built in reports only calculate MTBF for equipment that has the failure code marked against it. Instead, create your own calculation using population data. The population consists of all the similar assets. This is best done by using an asset type characteristic in the CMMS database. Run the report to determine the asset type population for the site, or organization. Next, determine what will be considered a failure in CMMS data. Ideally, the failure code is checked. However numerous studies and empirical data shows that very few organizations use failure codes, and fewer still use them rigorously. If you are in one of those less rigorous organization, determine a factor that is used regularly that can be used as a trigger of equipment health. Consider any work orders that are not generated through the PM system, or work orders over a certain dollar amount. Determine how many of the assets in the population hit the trigger in a 12 month period. Divide the number of assets that triggered by the total population. If that number is close to 10%, then the life expectancy is 12 months. Change the timeframe to find a calculation that is close to 10%. As the time frame increases, the same asset may be in the trigger population more than once. This is acceptable for this calculation.
Compare your calculated equipment life to your projected life at time of capital requisition. This is how to determine life expectancy in years.
Equipment life can also be determined in usage. For instance, vehicle life is more commonly thought of in miles, rather than years or time. This calculation would be more complicated. It would be easiest to calculate this from production data or the OEE system. Production numbers or dollar value of product produced over the time frame of the asset before replacement or overhaul. This would be factored as $ or assets produced, similar to mileage.
Standardized data is available for some equipment, see the list below.
Equipment life cycle charts
- BOMA organization
- Depreciation Schedule
Once your actual equipment life is determined, you can monitor it and determine how to improve it. My next post will go over how the four factors affect equipment life (Design, Installation, Operation, and Maintenance).
Does anyone have other methods for calculating equipment life?
One thought on “Useful Life”
Methods for determining remaining life are as varied as types of equipment. The approach for static equipment is quite different from rotating, electrical or control devices. They all start with assumptions about operations and maintenance. Things get very complicated when actual conditions vary from assumptions or change. The same is true when estimating life of new components.
Experience has taught me to first ask how the life estimates will be used and what is the impact of errors. You can often get estimates from vendors and books, and these are frequently adequate if the impact of errors is not great. Similarly, it is easy to spend too much time and money chasing a better answer still based on a lot of assumptions when the cost of added accuracy is not really justified.
Although the details of particular approaches are beyond this response, I will flag a particular issue I faced when working life issues for the Alyeska pipeline and other major long life assets. Ignoring the big impact of duty cycle evolution during the asset life, the issue of obsolesence is especially challenging for predicting the economic life of control systems.