How to extend the P-F interval for critical assets

The objective of prolonging the P-F interval is to increase the capability to detect potential failures

By Bryan Christiansen January 13, 2022
Courtesy: Limble CMMS

Understanding the concept of P-F intervals is a cornerstone of implementing effective proactive maintenance planning. As the name implies, the P-F interval represents the time interval between two states of the same asset. The potential failure (P) is the point in time when failure symptoms first start to appear. The functional failure (F) is the later point in time for the same asset, the moment when potential failure converts into a functional failure.

The P-F interval should be maximized for critical assets to ensure uptime for the overall plant. While there are many ways to extend the P-F intervals — from collecting data to improving maintenance strategies and personnel training — they focus on one common objective, which is detecting the “P” point as soon as possible. This is because potential failures are often hidden and may not manifest themselves unless they are intervened upon during maintenance, monitoring or the inspection cycle.

Here are some ways used by industry to extend P-F intervals for many assets.

Incorporate condition-based maintenance

The right maintenance at the right time plays a crucial role in catching potential failures. Since the equipment’s health does not remain constant from the time it is installed until its disposal, maintaining equipment at regular intervals is not sufficient to track potential failures.

The level of maintenance for any equipment should evolve to correspond to the actual condition of the equipment. If the equipment is maintained timely, based on the actual condition, and inspected with the right diagnostic tests, the chances are the potential failures will be caught by the maintenance personnel. To improve the effectiveness, certain diagnostic tests can be added on top of basic inspection — ones that produce better insights into the actual condition of the equipment.

For example, consider the example of preventive maintenance (PM) of an internal combustion engine shown in Figure 1. Instead of replacing engine oil and filters at every given interval as part of regular maintenance, additional tests on dissolved gasses, lubricant analysis and vibration analysis can be performed, providing greater insight on the root causes of a wide variety of potential failure in the engines. Such advanced testing procedures can be employed to detect the presence of incipient failures that may turn into a catastrophe for the entire operation if left unattended.

Figure 1: P-F interval represents the time interval between two states of the same asset.

Figure 1: P-F interval represents the time interval between two states of the same asset. Courtesy: Limble CMMS

Installing condition monitoring sensors

One of the effective ways to track potential failures is to automate asset condition monitoring. The automation enables faster intervention compared to manual condition recording and thus further enhances the P-F interval.

The automation is performed by installing sensors that can record and monitor statistically significant condition parameters of the equipment. The information on the condition is derived and processed to trigger an alarm if any anomaly is observed in the condition rating: The value of the condition above or below the specified threshold value.

While the cost-effective solution for most equipment can be to install offline sensors, some of the advanced sensors driven by the Internet of Things (IoT) are still economically viable for critical equipment contributing the most to overall plant reliability. The data collected from such IoT sensors is uploaded to the cloud database, which does not look only at the real-time data stream but also at historical data for that asset. This enables advanced failure diagnostics algorithms to be employed, providing deep insights on the emerging trends and identifying any anomalies.

For example, the data on the current intake by an electric motor can be recorded and analyzed by a failure diagnostics and detection (FDD) algorithm to detect potential failures in bearing and/or winding failures. Typically, during the conventional inspection and maintenance testing, such motors would need to be decoupled to analyze the condition of their parts. The use of online monitoring — together with advanced predictive algorithms — is gaining significant attention for critical equipment, as it avoids unnecessary and repetitive shutdown of equipment to collect inspection data. This method not only increases the P-F interval but also improves the overall uptime of the plant.

Human training to handle equipment

Though it sounds obvious, not many industries focus on building the right skills for people operating and maintaining the equipment. Although advanced condition monitoring sensors and algorithms can be quite effective in predicting potential failures, a situational awareness on part of the plant operator can track failure modes that may go unnoticed by any sensory algorithms. The need for human training to track potential failures becomes more important because it is not practical to record every condition of the equipment.

For example, in the case of a car, an experienced driver detects an anomaly in the car by riding the car on smooth road. Though the objective is not for a human to locate the potential failures with pinpoint accuracy, a simple situational awareness on behalf of the operator and maintainer, together with support from monitoring sensors, can do wonders in prolonging P-F intervals.

Final thoughts on P-F interval

The objective of prolonging the P-F interval is to increase the capability to detect potential failures. While there are many techniques to enable advanced failure detection, no technique works for each asset class. The selection of the right technique requires careful examination of equipment and operational requirements. For some equipment, it might be cost-effective to install advanced IoT-driven sensors and processors that perform predictive diagnostics. For others, a sanity check on behalf of maintenance personnel, together with standalone sensors analyzing real-time parameters, would be sufficient to meet the requirement.


Bryan Christiansen
Author Bio: Founder and CEO at Limble CMMS.