What Is a Reliability System Model?
As a whole, the industry typically has a difficult time quantitatively and practically forecasting system performance with confidence, which often results in wasted spending and unknown or unmanaged risks. Forecasting system availability using Quantitative Reliability Optimization (QRO) helps facilities maximize reliability performance by balancing production targets, HSE risks, and the costs of managing both effectively.
Knowing where to invest in order to manage risks and improve performance is challenging. Facilities often struggle with:
- Determining potential failure events at their facility
- The impact of those unplanned events
- The prescriptive maintenance tasks or actions that should be taken to prevent those events and improve overall performance in the most cost-effective manner and at the optimal point in time
Forecasting System Availability creates a dynamic cause and effect link between every data point and the facility, allowing operators to model how each asset, component, or data point impacts facility performance.
How Does Forecasting System Availability Differ from Conventional Models?
While some conventional reliability models such as . This means that the results are not as accurate as they could be, and they become quickly outdated over time as the facility is operated and maintained. Forecasting system availability is similar to RAM modeling, but it leverages live data and is updated on a consistent basis. This will provide a system of continuing insights rather than one based on a single point in time.
Forecasting system availability combines data extracted from the first two elements of QRO – Asset Risk Analysis (ARA) and Lifetime Variability Curves (LVC) – using first principles engineering analysis and data science to predict the health of each asset and its potential modes of failure. As a result, facilities can forecast the probability of failure for individual assets, as well as their expected availability over time, and are then able to incorporate these results into a system reliability model to show the impact that each individual asset has on the entire facility. Facility performance can then be forecasted, including a direct and dynamic correlation of how the quantitative data on each asset and activities performed in the field impact future performance. More importantly, this enables owner-operators to see when there are likely to be performance issues and where to focus their efforts to maintain facility reliability.
Benefits of Forecasting System Availability
By using QRO to forecast system availability, facilities can:
- Connect data in real-time from individual assets to the overall system’s availability
- Model system reliability configuration, including system redundancy, bypasses, slowdowns, and other operations parameters
- Prioritize tasks and activities that will have the greatest impact on facility performance