In all production facilities, the success of most business operations is closely tied to the performance of their maintenance operations. On a busy plant floor, for example, all it takes is for a critical machine to breakdown mid-production and the ripple effects begin; from lost deadlines to stressed staff, wasted materials, and so on.
Identifying exactly when an asset will fail still remains a big priority and “tools” like the p-f curve are here to guide us in the right direction.
Below, we’ll discuss how a CMMS can help you to optimize the p-f curve, especially when combined with a proactive maintenance strategy (in this case, that will be condition-based maintenance).
The P-F curve
For those that might not be familiar with the term, the p-f curve is a visual representation of a machine’s behavior over time. It shows us the health status of a machine and thereby helps us to estimate how much time exists between potential and functional failure.
This can help maintenance managers plan the best course of action and time their intervention just right; not too early nor too late.
Optimizing the P-F curve
The general idea behind applying the P-F curve is that it is useful for tracking specific behaviors that indicate deteriorating machine conditions. So, to improve asset reliability, the goal here is to increase the interval between the “P” and “F” points as much as possible.
It is noteworthy to mention at this point that proper equipment installation from the onset will go a long way to avoid many of the problems that occur later between the p and f points. Focusing some efforts on equipment installation by following OEM recommendations and adhering to all standards as stipulated creates a proactive approach from the get-go.
That said, after installation, you will need a combination of tools to keep your systems running optimally. As mentioned before, CMMS and CBM are a pretty good combination.
The Role of Condition-Based Maintenance (CBM)
Having reliable equipment is fundamental for running an efficient organization. Therefore to avoid failure, maintenance personnel are typically saddled with the responsibility of inspecting machines at intervals to catch any warning signs. But, the major challenge with this approach is that failure is not a linear event. Additionally, some inspections require technicians to turn off the machine so they can do the inspection safely, which is often far from optimal.
This means that each asset requires a more thorough but less intrusive method of analysis to identify its inherent failure pattern better.
This challenge was one of the main premises behind the development of condition-based maintenance. CBM makes it possible to track the different conditions (heat, oil, vibration, noise, etc) that indicate potential failure.
For example, an asset can be overheating, but still function for a while. By using different condition monitoring techniques, maintenance technicians can better track the level and speed of deterioration of the asset without interrupting its operations.
By using condition monitoring, we are minimizing unplanned machine shutdown and extending the functional lifespan of assets as long as possible. In addition, if you do not run CBM, you are artificially making the p-f curve shorter. It is not that an asset will magically breakdown sooner if you don’t have installed sensors, but if the first sign of deterioration you can really notice is noise, take a look at the picture above again and see how much space on the curve is wasted up to that point that could have been used to spot the deterioration earlier.
The Role of CMMS
We’ve seen above that it’s possible to minimize the impact of asset failure on our operations by using CBM to manage the p-f curve. But what if we could make the process even more efficient? Depending on the nature of the machine and how steep the curve is, we may have weeks, days or just a few hours before functional failure. In other words, the straighter the curve, the more time the maintenance unit has to plan and respond.
Whatever the case, keeping your assets in the best condition possible supported by an efficient process for monitoring, reporting, and repairing will give you more time to react properly. In simpler terms, the healthier the asset is in general, the more “damage” it can take before it reaches functional failure.
That’s possible by combining a CMMS with CBM. There are several CMMS features that will help you to manage this process such as:
- Generating Condition-Based Alerts
Once the condition monitoring sensors are set up to interact with the CMMS, the system sends automatic alerts that provide real-time insights into the machine’s health. However, the maintenance unit must act accordingly on this information. If not, the entire exercise is wasted as one of the purposes of running CBM is having the ability to act early. Just because the sensor shows that a part can hold on for a couple of days more doesn’t mean that it is not beneficial for overall machine health to replace it right away.
- Preventive Maintenance Planning and Scheduling
CBM gives you the vital information that you need about your asset’s condition. The next step is your responsibility. You’ll need to plan and schedule each maintenance task – based on OEM recommendations and equipment history – in a manner that will keep the machines running in peak condition for as long as possible. A CMMS effectively automates and speeds up the entire process of maintenance planning across thousands of assets.
- Spare Parts Inventory Management
It is almost impossible to keep your maintenance department running efficiently if there’s no insight into your spare parts inventory. Running out of essential parts typically leads to problems like deferred maintenance and emergency repairs. Imagine a scenario where your p-f curve is already well advanced towards the f point and replacement parts are unavailable. If you are using CBM and a modern CMMS that helps you forecast of stock-out levels, you should have plenty of time to ensure that the right spare parts are in stock when your team needs them.
As long as equipment failure remains a costly challenge for businesses, tools like the P-F curve will remain valuable for important decision making in maintenance teams. Such decisions when timed properly, often make the key difference between uninterrupted operations and repeated incidents of devastating asset failures.
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