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MC Engine Overview

The Monte Carlo (MC) Engine provides temporal analysis through discrete-event simulation, offering statistical insights into both availability (repairable systems) and reliability (mission systems) behavior over time.

What is the MC Engine?

The MC Engine uses discrete-event simulation to model actual failure and repair events over time. By running thousands of simulations, it provides statistical analysis of system behavior including timing patterns, failure distributions, and confidence intervals.

Key advantage

Provides temporal insights and statistical confidence measures that complement the exact mathematical results from the BN Engine.

When to use MC Engine

Best for

Use MC when you need

  • Understanding temporal patterns and timing
  • Statistical confidence intervals
  • Validating BN Engine results
  • Outage duration analysis
  • SLA breach probability assessment
  • Recovery time objective (RTO) analysis

Considerations

Keep in mind

  • Longer execution times than BN Engine
  • Results include statistical variation
  • Requires more computational resources
  • Statistical accuracy depends on run count

Core capabilities

Discrete-event simulation

Models actual failure and repair events occurring over time, providing realistic system behavior patterns and timing insights.

Statistical analysis

Provides comprehensive statistics including confidence intervals, percentiles, distributions, and variability measures for all reliability metrics.

Temporal insights

Reveals failure patterns, outage durations, repair times, and other time-based characteristics invisible to steady-state analysis.

Cross-validation

Designed to match BN Engine steady-state results, providing validation of both approaches and increased confidence in analysis.

Technical approach

  • Event-driven simulation: Uses priority queue processing to handle failure and repair events in chronological order
  • Component independence: Maintains component independence principle to ensure consistency with BN Engine results
  • Multiple distributions: Supports exponential, normal, and lognormal probability distributions for failure modeling
  • Statistical rigor: Provides confidence intervals, percentiles, and distribution analysis for all metrics
  • Safety limits: Built-in resource protection prevents system overload and enforces execution time caps

Key metrics provided

Availability metrics

  • Component and system availability percentages

  • Confidence intervals (95% and 99%)

  • Statistical percentiles (5th, 25th, 75th, 95th)

  • Standard deviation and variance

Timing analysis

  • Outage duration distributions

  • Mean time between failures (MTBF)

  • Mean time to repair (MTTR) validation

  • Recovery time patterns

Advanced statistics

  • SLA breach probability analysis

  • RTO compliance statistics

  • Failure rate distributions

  • Histogram visualizations

Validation metrics

  • Comparison with BN Engine results

  • Statistical significance testing

  • Convergence analysis

  • Simulation quality indicators

MC vs BN Engine comparison

Aspect BN Engine MC Engine
Analysis type Steady-state, mathematical Temporal, statistical simulation
Results Exact probabilistic values Statistical estimates with confidence intervals
Execution time Fast (seconds) Slower (minutes for complex models)
Temporal information None Rich timing and pattern analysis
Best use case Quick analysis, impact studies Detailed analysis, validation, statistics

Available actions

Run simulation

Performs comprehensive Monte Carlo simulation with configurable parameters and provides detailed statistical analysis with histograms and confidence intervals.

Learn more about MC Actions →

Configuration parameters

Simulation control

  • Number of runs: 1000 (default) - More runs = better accuracy
  • Random seed: Optional - For reproducible results
  • Simulation time: 8760 hours (default) - Analysis time period
  • Progress display: Real-time execution progress

Analysis options

  • Target availability: SLA threshold for breach analysis
  • Target RTO: Recovery time objective for analysis
  • Repair mode: Enable/disable component repair
  • Distribution types: Exponential, normal, lognormal

Tip

Most settings are shared with the BN Engine. See Simulation settings for detailed configuration guidance.

Interpreting results

Statistical confidence

MC results include confidence intervals that show the range of likely values. Narrower intervals indicate more precise estimates.

  • 95% CI: 95% chance the true value falls within this range
  • 99% CI: 99% chance the true value falls within this range
  • Percentiles: Distribution characteristics (P5, P25, P75, P95)

Validation indicators

Compare MC results with BN Engine to validate both analyses. Close agreement indicates reliable results.

  • Availability match: Should closely match BN steady-state results
  • Confidence intervals: BN result should fall within MC confidence intervals
  • Convergence: Results should stabilize as run count increases

Next steps

Ready to start using the MC Engine? Here's what to explore next:

For new users:

Advanced topics: