P-F curve is widely used in the manufacturing industry as a graph used to measure asset health deterioration over time. This curve was invented in the 1970s by the engineers Stanley Nowlan and Howard Heap in order to help the U.S. Defense Department and United Airlines in reducing downtimes for aircraft.
The fundamental design of the P-F curve acted as a guide for the subsequent versions. Each version was developed with the same purpose – helping maintenance and operations teams in increasing equipment reliability.
What is P-F Curve?
The P-F curve is a visual representation of an asset’s journey from potential failure points to functional failure. Asset failures are usually unpredictable and caused by multiple factors.
The primary elements of the P-F curve are the following:
P point: It indicates a potential failure where a probable asset failure is identified through physical condition monitoring.
F point: Functional failure where an asset failure is inevitable. It is identified as an inability to meet a specific performance standard.
P-F interval: This is the interval between the potential failure and functional failure. The key priority for the operations and reliability professionals is to prolong this interval, which can result in better asset reliability.
For increasing the P-F interval, condition-based monitoring methods are utilized where scheduled inspections are performed in various areas such as corrosion monitoring, vibration monitoring, thermography, oil analysis, motor testing, ultrasound, etc.
Utilities of P-F Curve
The P-F curve can be considered as a stepping stone in establishing a predictive maintenance program. Maintenance personnel usually depend on the P-F curve to track the asset deterioration. For the P-F curve to be useful, industrial companies need to possess historical data about asset conditions and failures.
The following functions of maintenance utilize the P-F curve:
Risk Assessment: Historical data of assets can uncover the risks and deterioration, leading to probable failures.
Maintenance Scheduling: The P-F curve enables maintenance teams in scheduling tasks to avoid asset failures as well as over-maintenance.
Asset life maximization: The P-F curve allows better decision-making for assets such as replacement, servicing, inspection which in turn, can remarkably increase asset life.
Resource Planning: After identifying the assets that need maintenance and servicing, adequate resources can be organized and prioritized. This also allows time for maintenance teams to source alternate spare parts instead of having to opt for last-minute replacements.
Issues with condition-based maintenance
Although, condition-based maintenance help in increasing the P-F intervals and are more reliable than shutdown-based inspections, it cannot be utilized realistically for most industrial plants. In companies with fairly established condition monitoring systems and frameworks, not 100% of assets can be maintained with it due to the varied resources, lifespan, and labor involved for different assets.
Finding the right balance between action and inaction varies with each asset and each plant. Scheduled and reactive inspections can inform about failure points but not the required time and resources to perform the fixes. Most maintenance and reliability programs include elements of preventive, reactive, and scheduled maintenance techniques.
The missing link – Failure Predictions are not enough
Despite the P-F curve’s usefulness in asset maintenance and implementation of predictive maintenance, a lot of companies were facing issues with asset reliability. As per Doug Plucknette, an industry expert, despite the considerable investments in the predictive maintenance efforts, many companies were facing repeated asset failures, as shown in the following visualization.
Companies used to issue replacement orders or maintenance jobs, based on the potential failure predictions, only to find out that the asset failure has occurred again in the future, making such a pattern of failures the saw-tooth effect.
The above P-F curves still prevent shutdowns up to an extent, they run the risk of misleading maintenance managers about the true value of a predictive maintenance system.
While it was undeniable that the companies could prevent probable costly damages, it was not known why each failure kept on occurring. This limitation led to an evolved version of the P-F curve with the addition of I (installation) and later, D (Design).
I-P interval and root cause analysis
The addition of point I before the potential failure point P formed the “I-P interval”. This interval represents the time taken from Installation to the detected potential failure. The best practices of maintenance and reliability should include measures to prolong the I-P interval. This can be achieved with a deep knowledge of the assets and a proactive maintenance schedule.
Doug Plucknette cites an example where a repeatedly failing blower was placed on an undersized foundation – leading to misalignment and subsequent failures. After conducting a thorough analysis of all the probable reasons leading to the failures, the root cause was discovered, resulting in a longer asset lifecycle.
Industry 4.0 and the D-I-P-F Curve: Amalgamation of the new with the old
In large-scale productions, asset condition is a critical factor in maintaining productivity and quality standards. With the increased digitization and automation, maintenance processes are more important than ever for ensuring safety as well as efficiency. The cutting-edge technology of the Industry 4.0 has contributed tremendously to increasing the operational reliability of the industrial assets. However, no industrial asset can be made failure-proof even with the advanced technological framework of predictive or preventive maintenance.
The desire for maximizing operational efficiency of critical industrial assets led to evolving of the I- P-F curve into the D-I-P-F Curve.
“D” indicates Design where the fundamentals of equipment selection, structure, and strength are considered for ensuring reliability and performance. Questions that should be asked in the design phase – Is the right equipment selected? Is the structure of the equipment aligned with the forces that it will be exposed to?
If a piece of equipment lacks precision and accuracy in design, the reliability and efficiency will lead to poor performance and lesser equipment longevity. The design phase can make or break the success of a reliability program.
AI-Driven root cause analysis with Eugenie
The introduction of the D and I points can lead to more reliable maintenance outcomes. Also, AI-driven systems like Eugenie can effectively provide root cause analysis and simulation for any operational anomalies, which can help in accurate visualization of the P-F curve.
Eugenie’s solutions not only provide alerts based on machine performance data, but also the detailed rot cause as well as remedial actions to enable operational staff in taking timely actions to avoid asset failures.
While the P-F curve has evolved over time, the end goal is the same – better uptime, lesser costs. Planned maintenance is always cheaper than downtime or a catastrophic failure. By combining the new and efficient data-driven technologies along with the traditional maintenance models like the P-F curve, maintenance companies can establish a solid PDM foundation.
To know more about Eugenie, get in touch with us.