Repairable System Analysis via Discrete Event Simulation
BlockSim's simulation capability for reliability, availability, maintainability and supportability (RAMS) analysis of repairable systems is more flexible and realistic than ever. For a new system, you can use simulation results to optimize the design and make projections about how the system may perform in the field. For existing equipment, use the results for maintenance planning, throughput estimates, life cycle cost estimation and more.
Reliability and Availability Analysis via Simulation
When you utilize simulation, the analysis can consider:
- The task scheduling logistics (e.g., based on item age, system age, system down, start of maintenance phase, etc.). This includes a "Virtual Age" option for situations in which the scheduled maintenance task will be performed even if the item has failed.
- The restoration factor that captures the impact of repairs on the future reliability of the component (i.e., as good as new, as bad as old or partial restoration).
- The duty cycles for components that experience a different stress load than the rest of the system (e.g., a component that may run for only 10 minutes out of every hour that the system operates, or a component that works twice as hard during a particular phase of the overall mission). Now in Version 10, these can also be set at the subdiagram level.
- The expected downtime associated with corrective or scheduled maintenance (defined either as fixed durations or based on probabilistic distributions).
- The costs and logistical constraints associated with allocating the personnel (repair crews) and materials (spare parts) required to perform maintenance when needed.
- Components that will receive maintenance based on what happens to other components in a specified maintenance group. For example, if one component in the group fails, this might trigger preventive maintenance for other components while the system is already down. In the latest version, BlockSim can now model even more complex scenarios, such as maintenance that occurs when another component is restored to operation, or components that are affected by more than one maintenance group.
- BlockSim now offers the ability to create state change triggers that activate or deactivate a block under certain conditions during the simulation. This provides increased modeling flexibility for highly complex dependency scenarios, such as standby configurations and other situations when you may need to divert the simulation onto an alternate path when a particular event occurs. The models can trigger actions or respond to a state change (e.g., start, stop, perform maintenance, etc.) from any block and across different diagrams. You can chain and cascade triggers to model even the most complex scenarios.
BlockSim’s simulations generate a wide variety of results at the system and/or component level. This includes Uptime/Downtime, Mean Time to First Failure (MTTFF), Availability, Reliability, Number of Failures, Number of PMs/Inspections, Costs, etc. You can use these results for many different applications, including (but not limited to):
- Choosing the most effective maintenance strategy based on considerations of safety, cost and/or availability.
- Determining the optimum preventive maintenance (PM) interval. In Version 10, we've added the ability to calculate optimum inspection times as well!
- Managing the spare parts inventory based on considerations of cost, utilization rate, supply bottlenecks, etc.
- Identifying the components that have the biggest impact on availability (downtime).
The software’s Log of Simulations feature provides the information you need to evaluate the variability in specific simulation results of interest. You can export these results to Microsoft Excel if desired.
Life Cycle Cost Estimation
Every successful organization understands that it is critical to understand the life cycle costs (LCC) associated with their equipment. Whenever applicable, BlockSim allows you to specify both the direct and indirect costs associated with the maintenance strategies that you have defined, including costs related to downtime, maintenance crews, spares, etc. This yields a wide array of simulation results that are instrumental in performing realistic LCC assessments.
In the Synthesis version, we have added the option to enter any cost input as a probabilistic model, if desired. We've also increased the modeling flexibility by allowing you to:
- Specify what kinds of crew delays are included in cost calculations and what delays should be ignored.
- Specify costs associated with system failure, including cost per incident and downtime rate.
- Specify system uptime revenue and revenue due to throughput so the simulation is able to calculate opportunity costs.
- View new cost-related simulation results, including system-level costs, the contributions of different kinds of wait times to block costs and the contribution (criticality) of a block's cost to the total system costs.