Fault Tree Analysis for Higher System Reliability

Use a complete minimal cut analysis for precise failure detection.

Use all common failure models, such as constant failure probability, constant failure rate, and latent faults, to ensure robust and reliable basic analyses.

Expand your analytical capabilities with specialized models such as Poisson, binomial, or standby, and achieve more accurate results for complex systems.

Combine fault tree and event tree analyses to comprehensively identify system risks. Accurately assess the consequences of hazards and make informed decisions based on causes and possible consequences.

Easily link basic events to complex Markov models.

Utilize real failure data by linking fault tree failure models with Weibull analysis data sets.

Common-cause failures can be taken into account by using methods such as the beta factor, MGL, alpha factor, or beta-BFR.

Identify failures that can be traced back to a single root cause. This allows you to reliably capture simultaneous effects and reduce the risk of overlooking failures.

Model time-dependent events where one failure triggers another. Use initiator-enabler analysis, priority AND gates, and sequential failure models to accurately analyze complex sequences.

Automatically vary failure and repair data within defined limits.

Get intermediate values for time-dependent parameters in your system.

Automated verification checks help you avoid critical failures such as circular logic, the most frequent serious problem in fault tree analysis, before your analysis even begins.

Identify and prioritize critical components and assess risks reliably.

Monitor your results with uncertainty assessments and status indicators.