Linganisha mbinu

	Uchanganuzi wa Hali ya Kushindwa na Athari zake kwa njia ya Bayesian	Uchambuzi wa Mti wa Hitilafu wa Kibayeshi
Nyanja	Muundo wa Majaribio	Muundo wa Majaribio
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1990s–2000s	2001 (BFTA mapping); Bayesian networks: 1988
Mwanzilishi	Extension of classical FMEA (MIL-STD-1629, 1974) with Bayesian inference formalised in reliability literature from the 1990s onward	Andrea Bobbio, Luca Portinale et al. (mapping FTA to Bayesian networks); Judea Pearl (Bayesian networks)
Aina	Probabilistic reliability and risk analysis	Probabilistic reliability / safety analysis
Chanzo asilia	Bowles, J. B., & Peláez, C. E. (1995). Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliability Engineering and System Safety, 50(2), 203–213.	Bobbio, A., Portinale, L., Minichino, M., & Ciancamerla, E. (2001). Improving the analysis of dependable systems by mapping fault trees into Bayesian networks. Reliability Engineering & System Safety, 71(3), 249–260.
Majina mbadala	Bayesian FMEA, probabilistic FMEA, B-FMEA, Bayesian risk priority analysis	BFTA, Bayesian FTA, Bayesian network fault tree, probabilistic fault tree analysis
Zinazohusiana	5	5
Muhtasari	Bayesian FMEA extends the classical Failure Mode and Effects Analysis framework by replacing fixed point-estimate risk scores with probability distributions, allowing prior engineering knowledge and observed failure data to be formally combined through Bayes' theorem. The result is a probabilistic Risk Priority Number (RPN) that reflects uncertainty in severity, occurrence, and detectability ratings rather than masking it with single consensus values.	Bayesian Fault Tree Analysis (BFTA) extends classical fault tree analysis by converting the fault tree structure into an equivalent Bayesian network, enabling probabilistic inference in both forward (prediction) and backward (diagnosis) directions. This integration allows analysts to update failure probability estimates with observed evidence, quantify uncertainty explicitly, and identify the most probable root causes of a top-level system failure.