What Is a Lifetime Variability Curve (LVC)?

Company
What We Do
- Data-Driven Reliability
  - What is Data-Driven Reliability?
    Pinnacle’s unique framework for reliability performance improvement that allows you to connect data to business decisions.
    Learn More
  - Data Collection
  - Data Organization
  - Intelligence
  - Strategic Decisions
- Ensuring Success Across All Reliability Focus Areas
  - Ensuring Success Across All Reliability Focus Areas
    Learn how Pinnacle can help you manage your daily activities to ensure success across your reliability focus areas.
  - Data-Driven Greenfield
  - Data-Driven Mechanical Integrity
  - Data-Driven Operations & Maintenance
  - Data-Driven Asset Reliability
- Making Reliability Technology Work
  - Making Reliability Technology Work
    Learn how Pinnacle enables you with the technology that best fits your goals and needs.
  - Technology Partners
  - Newton™
- Quantitative Reliability Optimization (QRO)
  - What is Quantitative Reliability Optimization (QRO)?
    QRO is an evolution in reliability modeling that combines the best traditional methods, data science, and subject matter expertise to help you make confident business decisions.
  - Quantitative Reliability Optimization (QRO)
Industries
Learn
Contact

What is an LVC?
How Does an LVC Work?
Example of an LVC
Benefits of an LVC

What is an LVC?
How Does an LVC Work?
Example of an LVC
Benefits of an LVC
Back to Top

What Is a Lifetime Variability Curve (LVC)?

Facilities can work towards achieving system-wide reliability with Quantitative Reliability Optimization (QRO). QRO helps facilities maximize reliability performance by balancing metrics such as production targets, Health, Safety, and Environment (HSE) risks, and the costs required to manage those risks effectively.

QRO is comprised of four elements: Asset Risk Analysis (ARA), the Lifetime Variability Curve (LVC), system view, and task optimization. The second component of QRO is the LVC.

An LVC is a data-driven model that leverages a facility’s reliability data to estimate asset performance, predict failure, and quantify uncertainty. With QRO, the LVC can generate a probability of failure (POF) curve, helping facility leaders better forecast system availability.

How Does an LVC Work?

LVCs work similar to a hurricane tracker – as the model collects more real-time data, it becomes more refined and better predicts when assets will fail. LVCs can be used for both functional failures and asset degradation.

LVCs start by taking existing data points and combining them with subject matter expertise and process data to forecast how the asset will change over time. As the LVC collects data, the model continues to refine itself.

Blog: Getting More from Your RBI Program through Lifetime Variability Curves (LVC)

An LVC can help you better quantify POF and expected failure timeframes to optimize your inspection and maintenance activities. To learn how you can get more from your RBI program through LVCs, read our blog in Inspectioneering.

Example of an LVC

For example, an LVC can be applied to the vibration of a bearing in a centrifugal pump. The LVC helps predict when the vibration of the bearing will cause the pump to stop functioning.

In Figure 1, the LVC leverages existing vibration measurements to predict when the bearing will fail. Since there is not a particular trend with existing data points, the model predicts a wide band of uncertainty.

As the LVC is fed more data, the model continues to refine itself, quantifying a POF curve. As a result, the model is able to create a more accurate band of uncertainty as seen in Figure 2.

Benefits of an LVC

With the incorporation of an LVC, facilities are able to do the following:

Leverage a POF that is continually refined based on facility data instead of a static POF
The ability to collect more data at the correct time, helping them justify spending and better identify the optimal time to perform maintenance activities
Process information in real-time without having to go out into the field to take measurements
Blend process data and subject matter expertise to help facilities avoid over-collecting data in some areas and under collect in other areas.

Case Study Highlight

CML Optimization Pilot Project Helps Refinery Reduce Risk and Identify Minimum Reduced Inspection Spend of $384K

Pinnacle implemented a data science enhanced CML Optimization process. The methodology, known as CML Prioritization, uses predictive analytics to forecast future thinning based on past performance of a specific CML considering repairs and replacements. It prioritizes CMLs according to the risk they pose to safety and production before the next scheduled action. Unlike traditional methods, CML Prioritization takes into account the degradation rates and the uncertainty of the facility’s data, which can impact the accuracy of the predictions. This approach also establishes a dynamic model that can be regularly updated to optimize reliability and return on investment (ROI) based on the latest information available.

Read the full case study: CML Optimization Pilot Project Helps Refinery Reduce Risk and Identify Minimum Reduced Inspection Spend of $384K

To learn more about how LVCs and QRO can help your facility,
set up a discovery call.

What is Data-Driven Reliability?

Ensuring Success Across All Reliability Focus Areas

Making Reliability Technology Work

What is Quantitative Reliability Optimization (QRO)?

What Is a Lifetime Variability Curve (LVC)?