What is the P-F curve in maintenance applications?

The eventual failure of any industrial machine is inevitable. Think about it this way, if you use a pair of shoes for 500 miles of walking they eventually get worn out. The same happens which industrial machines (production machines, pumps, motors etc.) At some moment in time wear and tear naturally occurs due to daily usage, reaching the end of equipment life. In the P-F curve, we call this the functional failure point.

The good news is that the functional failure point takes time to occur and there are certain moments in time which can give us information about the wear and tear of our industrial machines. The P-F curve in maintenance helps to identify the behaviour of industrial machines over time. It’s used to estimate the maximum usage expected from industrial machines.

In this blog, we take a quick dive into the different aspects related to the P-F curve in maintenance and its use in predictive maintenance applications including:

• What is a P-F curve in maintenance?
• How to use the P-F curve for maintenance of industrial assets
• Types of measurements techniques
• Getting started with the P-F curve for maintenance of industrial assets

The P-F curve is a graph used in maintenance engineering to show the health of a machine over time. It helps to identify the interval between potential failure and functional failure.

The vertical axis determines the asset condition and the horizontal axis shows the time since machine installation. Point P represents the moment in time a particular failure can be detected. Over time the asset condition will further degrade until point F. This is the moment the asset no longer functions as expected, the functional failure. The time between point P and point F is called the P-F interval.

• Point P: Potential failure indicates the point at which we notice the machine is starting to deteriorate. 
• Point F: Functional failure is the point at which the machine has reached the end of equipment life and is no longer operational.

Please note that the P-F graph is valid for one failure mode. The P-F curve of other failure modes might look completely different.

When your industrial machines are operating 24/7, you can’t afford to figure out what’s wrong when something is broken. To reduce maintenance costs, you want to detect the machine failure as soon as possible instead of waiting for the functional failure to be detected. Defining the maintenance P-F curve for your industrial machine is a valuable tool to get ahead of your maintenance operations. Proactively fixing the problem is usually cheaper and maintenance can be better planned to optimize the uptime of your machine. 

Nowadays various measurement techniques are used to measure the potential failure of a machine. Think about monitoring 

• Ultrasound energy detection
• Vibration 
• Noise 
• Heat 
• Humidity 

We can plot the different kinds of measurement types in the P-F graph (asset condition vs time). Each type of measurement can indicate something different and some measurements are closer to functional failure. In the graph below, you can see an example of different measurement methods for potential failures.

Figure 1: An example of the P-F curve for bearing failure in a specific production system. The locations of the various technologies on the curve will be different for each piece of equipment, production environment and failure mode, so be sure to calculate if for the specific equipment and types of degradation you want to monitor.

Ultrasound energy detection 

Ultrasound energy detection can be used to detect a potential failure at an early stage. However, one of the drawbacks is that such a measurement technique is very sensitive to background noise. Therefore, it makes it harder to deliver accurate measurements of potential failure. 

Vibration analysis 

Vibrations analysis includes the back-and-forth movement or oscillation of components or moving parts. Such analysis type has various pros and cons. You can read more about it in our other blog: Choosing the right IoT vibration sensor 

Oil analysis 

With oil analysis, you can detect the condition of the oil and based on the condition you say something about the machine condition. However, not every machine requires oil or the particular failure mode can’t be detected with the oil quality. This type of analysis depends on the specific machine and failure mode.

Audible noise and thermography analysis 

When performing periodic maintenance, audible noises can be detected at particular locations of the machine. The moment a noise is stronger it could indicate that the potential failure mode is soon likely to occur. 

Thermography equipment is used to see the temperature spikes at particular locations of the asset. Also, during visual inspection mechanically looseness can be detected. The downside is that when this happens, the machine condition has already degraded much more, making repair more expensive and time-critical.

Smoke and heat analysis 

Finally, just before functional breakdown you could detect smoke or the asset has become so hot that you can’t touch certain parts anymore. Whenever this happens, most likely immediate maintenance or even replacement of the asset/part is required. This with increase the maintenance costs and also reduce the plannability to zero leading to longer unplanned downtime. 

The P-F curve in maintenance application is a really useful tool for maintenance professionals to analyze the potential failure of an asset and guide them on how they want to measure the potential failure modes. Especially with critical assets, it is wise to invest in IoT technology that can detect failure modes at an early stage (predictive monitoring). However, in less critical assets a preventive or even a reactive approach might be more cost-efficient. Learn more about the different types of maintenance strategies in our free guide: The essential guide to predictive maintenance for OEMs

By embracing accurate predictive monitoring through industrial IoT solutions, you improve your assets’ uptime and lifetime and your Overall Equipment Effectiveness (OOE). Moreover, it allows you to enhance the productivity of the maintenance department and release them from unnecessary manual labour. It’s a way of achieving more with less by preventing the failure before it happens. 

Do you want to start with ai in predictive maintenance and need advice on taking the first steps? Then, get in touch with us.