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Understanding the P-F curve and its impact on reliability centered maintenance

The P-F curve has become an essential component to any reliability centered maintenance program, and being able to understand it can help extend the lifespan of your machines by more than you might think.

Machines are never built to run forever, but they can last a lot longer than one may expect. Machine maintenance is an essential component to any industrial operation, but all too often, these processes are neglected if not ignored entirely. While it may seem like a hassle or expensive, having a reliability centered maintenance program in place can do wonders for the lifespan of your machines and avoid massive costs.

However, in order to run an effective reliability program, you need to take the right approach when it comes to making sure that machines are running properly. There are a bevy of tools available today that can help you detect machine issues before they become real problems, such as ultrasound and infrared imaging. But in order for you to properly employ these tools, you need to have a thorough understanding of how machines break down in the first place.

The P-F Curve

The P-F Curve chart is one of the most important tools for a reliability centered maintenance plan. It demonstrates the relationship between machine breakdown, cost, and how it can be prevented. Despite its usefulness a lot of companies may overlook its value.

Simply because a machine is working now, does not mean that failures are not already beginning to occur within the system. In fact, most of the early signals in a machine cannot be detected without the aforementioned tools. By closely examining the P-F curve, we can gain a better understanding of just how essential and cost saving RCM truly is. Let’s examine the various elements of the chart and why they justify a thorough RCM program.

Equipment condition and time

Machines can still run after a failure has begun, but once this incident occurs, it is only a matter of time before the machine fails entirely.

Along the X-axis of the P-F curve is time. At the start of the axis is when failure starts to occur; at the end of the axis is when the machine actually fails. Along the X-axis there are a number of instances during which the faults can be detected before the point of failure, but unfortunately, the ones that are most notable without the assistance of high-tech tools, usually already signal costly repairs.

Running along the Y-axis is the machine’s condition. Just before and at the point of failure, the assumption is that the machine is already in top working condition. This would put our P-F curve at the top left of the graph. As time progresses from the point of failure, the equipment’s condition moves down the y-axis until it physically fails.

Detecting problems before they happen

The two axes create a plane on which our P-F curve lies, arcing downward on the Y-axis as it moves along the X-axis. It can be difficult to pinpoint when exactly a failure mode has begun, but fortunately, we can look for signs of failure to address this issue before it fails outright. Unfortunately, when signs of failure become most notable, such as audible noise and the machine being hot to touch, it may already be too late.

Early signals of failure are where we need to focus so that we can keep machines running at full capacity and minimize downtime. By using ultrasound technology, such as the Ultraprobe 15,000, we can detect issues in rotating assets and lubrication that may be causing failures and mechanical inefficiencies. Through these technologies, we can also detect electrical faults and other issues as well.

Understanding cost

Almost serving as a mirror image to the condition of our machine is the cost of repairing it. When failure starts to occur, the machine is still running relatively well. But as these detection signs begin to crop up, the cost of repair begins to increase as well. If caught early enough, the cost of repair may be a bit of machine downtime needed to lubricate the machine, but as it progresses along the time axis, it becomes more and more expensive, ultimately hitting its peak in cost when the machine fails.

This is why RCM is so important. Because the early detection signs are not noticeable without the aid of technology, we need to be ever-vigilant in making sure that we can detect these issues before they occur. This means conducting routine inspections along designated routes to make sure that no machine gets neglected. Also, tagging issues as they are detected can help provide a visual means as to the condition of a machine.

At the end of the day, RCM simply makes fiscal sense. But in order to make sure that these programs are operating effectively, and understood by all members of your team, having an understanding of the P-F Curve can help ensure everyone is on the same page.