Many people talk about a CBM program in broad terms that many times include multiple aspects of a Reliability Centered Maintenance (RCM) program. The three most common aspects that are combined into one are Preventative Maintenance (PM), Condition Based
Maintenance (CBM) and Predictive Maintenance (PdM).
I would like to offer my perspective on how these three areas should be separate as they all contain different aspects of a maintenance program.
In a Preventative Maintenance program, the traditional method has been to schedule PM tasks based on calendar dates. This method has been the standard for many companies for many years. The problem with this method is that, unless you have equipment that is running 24x7, you may be over or under maintaining your assets.
When a manufacturer provides recommended maintenance intervals, they are most commonly provided in run hours. For example, a pump may have a set of maintenance tasks to be performed at 50 hours, a broader set at 100 hours, additional tasks at 500 and 1,000 hours and so on. By utilizing actual run hour data from PI, these PM task can be generated and scheduled based on the actual run hours rather than a date, increasing the accuracy of the PM’s being performed.
I am a big supporter and advocate of basing PM tasks on actual run hours’ vs a calendar based system. With that being said, there are still areas where configuring PMs based on a calendar are more practical than using run hours. An example would be maintenance activities that are seasonal, such as performing annual air conditioner systems in the spring.
The common element between these two methods is they both use measures of time (days vs hours) to schedule and perform the maintenance tasks. If an asset is scheduled for a three month PM according to manufactures recommendations and the asset has not
been utilized for two of the three, then an organization is incurring maintenance costs that could be avoided.
Condition Based Maintenance (CBM) utilizes PI data to base maintenance activities on current conditions of the asset. For example, if vibration is a data point that is monitored on a pump, a maintenance work order would be generated when the value reached a predetermined point. Another example would generate an emergency or high priority work order if the temperature of an asset exceeds a high level, but would only generate a maintenance work order if it exceeded a lower threshold.
In both examples, the maintenance tasks or repairs are based upon the actual condition of the asset and not a time value.
The third maintenance area that is commonly associated with CBM is Predictive Maintenance (PdM). There are several schools of thought if PdM is a form of CBM. PdM uses data with predictive tools and algorithms to predict when an asset will fail, and generate a work order. Since this method is predicting a future condition of an asset and not basing the task on a “current” state, I like to consider Predictive Maintenance a separate aspect of a maintenance program.
Another reason for splitting PM, CBM and PdM separately when discussing a RCM program is that they can all be implemented independently from one another or together as part of a broader more comprehensive Total Productive Maintenance (TPM) program.
There are additional aspects of a RCM or TPM program that PI data can be utilized, Root Cause Failure Analysis (RCFM), Metrics and KPI’s, Operational Equipment Effectiveness (OEE), Planning and Scheduling, for some examples. I would like to cover these in future articles.