Salīdzināt metodes

	Simulāciju atbalstīta cēloņu analīze	Simulācijām balstīta kļūdu koku analīze
Nozare	Eksperimentu plānošana	Eksperimentu plānošana
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1990s–2000s (widespread adoption in engineering reliability contexts)	1970s–1980s (widespread adoption in nuclear and aerospace industries)
Autors	Evolved from root cause analysis practice (Kepner & Tregoe, 1960s) integrated with simulation methods (1990s–2000s in reliability engineering)	Fault tree analysis: H. A. Watson (Bell Labs, 1961); Monte Carlo integration in reliability: Herman Kahn / Stanislaw Ulam (RAND, late 1940s); combination formalized in reliability engineering literature from the 1970s onward
Tips	Analytical / diagnostic engineering method	Quantitative reliability and risk analysis technique
Pirmavots	Latino, R. J., & Latino, K. C. (2006). Root Cause Analysis: Improving Performance for Bottom-Line Results (3rd ed.). CRC Press. ISBN: 978-0849338267	Vesely, W. E., Goldberg, F. F., Roberts, N. H., & Haasl, D. F. (1981). Fault Tree Handbook. US Nuclear Regulatory Commission, NUREG-0492.
Citi nosaukumi	Sim-RCA, simulation-based RCA, virtual root cause analysis, computational root cause analysis	SA-FTA, Monte Carlo FTA, simulation-based FTA, stochastic fault tree analysis
Saistītās	6	6
Kopsavilkums	Simulation-assisted root cause analysis (Sim-RCA) integrates computational simulation — such as discrete-event simulation, Monte Carlo methods, or finite-element analysis — into the structured root cause analysis process to diagnose the underlying causes of complex failures or defects. By running virtual experiments on a system model, investigators can test hypothetical causal pathways safely, rapidly, and at scale, without disrupting live operations or waiting for rare failure events to recur.	Simulation-assisted fault tree analysis (SA-FTA) combines the logical structure of classical fault tree analysis with Monte Carlo or discrete-event simulation to estimate the probability and timing of an undesired top event when component failures follow complex, non-exponential, or correlated probability distributions. The approach overcomes the analytical limitations of Boolean algebra-based FTA and is widely used in nuclear, aerospace, chemical process, and manufacturing reliability engineering.