A simple MS Excel based predictive maintenance optimization modelling spreadsheet program for selecting optimum condition monitoring inspection intervals
Use the condition maintenance optimization tool to find the optimal inspection frequency to minimise maintenance costs and reduce plant and equipment failures
A condition maintenance optimization tool in a Microsoft Excel modelling spreadsheet to select the best on-condition inspection period for plant and equipment. Condition based maintenance optimization (CM-optimization) is useful for on-condition maintenance strategy selection and to review your current PM inspection schedule to minimise maintenance costs while minimising equipment failures.
With a condition maintenance optimization model your inspection and condition monitoring strategies no longer need to remain static after they are initially set, instead they can be optimized based on actual site equipment maintenance and failure data. The condition based maintenance inspection interval optimisation tool lets you use site specific information on failure mode risk, condition monitoring warning times and practical maintenance windows. You can do ‘what-if’ scenarios to find the optimal time between condition monitoring inspections.
Condition Maintenance Inspection Interval Optimization Modelling Tool (CM-optimization) (MS Excel Spreadsheet for selecting condition monitoring intervals that minimise maintenance costs and the number of equipment failures)
How to Use the Predictive Maintenance Optimization Tool and Modelling Software
For example, in the Condition Maintenance Optimization Tool model below, a gearbox breakdown costs the operation a $100,000 DAFT Costs (Read the article on how to calculate Defect and Failure Total (DAFT) Costs), but only $8,000 to repair if an emerging failure is found early. We know from its operating history the gearbox fails about every 10 years, or 0.1 times per year. The cost for condition monitoring the gearbox using a combination of vibration analysis and oil analysis is $600 for each inspection, plus a site overhead cost of $200 per year for administration. It takes two weeks to get the inspection report back during which the gearbox condition is unmonitored.
This condition inspection regime we selected (vibration analysis and oil analysis) will detect 80% of impending failures and 20% of failure modes will not be observed. We also know from historic records that 5% of inspection results are wrong. Once we find an emerging failure our maintenance history indicates there is a 5% chance the gearbox will fail in six weeks and a 50% chance it will last for 40 weeks. This information is entered in the table and the tool produces a cost curve model of the scenario. You then use the tool to look at the effects of changing predictive maintenance strategy and condition inspection intervals.
If we did nothing and let the gearbox run to failure we would have a breakdown every 10 years. So every ten years we would spend $100,000; an annualised cost of $10,000. The model informs us that the optimal period to do the CM inspection is every 28 weeks. This frequency produces an annualised cost (for monitoring, repairs and an occasional breakdown that is not prevented) of about $5,000. We have saved the business about $5,000 per year by doing the condition monitoring on the gearbox instead of letting it run-to-failure. If we were concerned that 28 weeks was too long and instead wanted to do it every 14 weeks, you would double the inspection cost, but still save the operation $4,400 annualised.
With the model we can do ‘what-if’ analysis of our options. What-if we could do condition monitoring coverage for 100% of failure modes by including wear particle analysis into the CM regime? Inspection costs and overhead costs rise to $1,000 and $400 respectively. The model now shows annualised savings of about $5,600 with the inspections still at 28 weeks. The model gives you confidence that improving the condition monitoring regime to include wear particle analysis is a good business decision.
‘What-if’ a situation arose where we could purchase a gearbox with half the rate of failure of the current unit (for example by getting an oversized gearbox) and we wanted to know the impact that would have on our maintenance costs? The large gearbox would cost more to repair and that is reflected in the model below. The unit of measure on the graphs has been changed to 2-weeks per division to show the longer 30-week optimal period between inspections.
The annualised operate-to-failure maintenance cost is $6,000 for a breakdown every 20 years; down from $10,000 and is an immediate $4,000 annualised saving on using the 10-year life gearbox. If we continue with the 100% failure mode detection condition monitoring regime on this long-lived gearbox we save about $2,400 per year compared to a run-to-failure strategy. Using a long-lived gearbox gives us a total annualised maintenance savings of $6,400, which when compared to the 10-year life gearbox savings of $5,600 is clearly a better outcome for the operation.
This CM-optimization condition based maintenance optimization modelling tool was developed by Howard Witt, a professional reliability engineer with over 25 years hands-on industry experience, including nuclear facilities and industrial process plants.
View a PDF document showing the use of the Condition Maintenance Optimization Tool to Model Predictive Maintenance Optimal Frequency:
CM Optimization Condition Based Maintenance Optimization Example
View a PDF document of the first four pages from the User Guide for the Condition Maintenance Optimization Model:
Extract of Condition Based Maintenance Interval Optimization User Guide
Because it is unknowable how applications will be used, their Developer and Lifetime Reliability Solutions take no responsibility for correctly modelling the situation, or for the outcomes of using an application.
It is recommended that you understand well the theory behind the application you use, so you can confidently judge whether it applies to the situation under investigation and if its output is sufficiently accurate in the circumstances. Remember the warning that applies to all modelling methods — ‘garbage in, garbage out’.