Just as autonomous maintenance is the core pillar of most implementations of TPM, RCM is a central pillar of predictive maintenance. Its whole purpose is to collect data to make informed decisions.
RCM typically involves heavy statistics, but there are software packages to help you get the most out of it.
To implement an autonomous maintenance system, you will need to follow these steps:
Data needs to be collected and recorded for each individual piece of equipment.
Let’s say you have 3 similar machines, but they are not loaded the same way. The failure pattern of individual machines might look like this – where the crosses indicate a breakdown event:
When you consider each of the machines, you’ll see that merging the data together won’t make much sense because the patterns are so different. Each machine needs to be considered individually.
It often makes sense to order spare parts and do the replacement without waiting for a breakdown, however, can we assume that the risk of breakdown truly increases past a certain time, or a certain number of cycles?
Most components follow patterns like these:
If this applies to some of your equipment, is a “basic” preventive maintenance policy appropriate? Clearly not. Time in operation, number of cycles, or other time/age based measurements, do not help predict a breakdown.
The idea behind condition monitoring is getting an early warning so you can react to a deviation from a standard before it leads to a breakdown. Just as with data collection, not all your injection presses, or stamping machines, are in the same condition, even if you purchased them all together, so you will need to monitor them individually.
You don’t need to have a full history of each breakdown; even if you only have past failure incidents data on 5 or 6 of these 20 machines, you can still rank them and run a Weibull analysis.
Want to know how a preventive maintenance system helps cut costs and increase productivity? Click below.