The Hybrid Causal Logic Analyzer (HCLA) software is a cross-platform command-line tool to perform traditional probabilistic risk assessment (PRA) based on the Hybrid Causal Logic (HCL) methodology with advanced time-to-failure models, importance measures, and uncertainty quantification. The HCL methodology employs a model-based approach to system analysis. The framework contains a multi-layer structure that integrates event sequence diagrams (ESDs), fault trees (FTs), and bayesian belief networks (BBNs) without converting the entire system into a large BBN. The models can be created with the IRIS software or directly using XML files.
This allows the most appropriate modeling techniques to be applied in the different individual domains of the system. The scenario or safety context is modeled in the first layer using event sequence diagrams. In the next layer, fault trees are used to model the behavior of the physical system as possible causes or contributing factors to the incidents delineated by the ESDs. The BBNs in the third layer extend the causal chain of events to potential human and organizational roots. The connections between the BBNs and ESD/FT logic models are formed by binary variables in the BBN that correspond to basic events in the FTs, or initiating events and pivotal events in the ESDs. The probability of the connected events is thus determined by the BBN. In order to quantify the hybrid causal model it is necessary to convert the three types of diagrams into a set of models that can communicate mathematically. This is accomplished by converting the ESDs and FTs into Reduced Ordered Binary Decision Diagrams (ROBDD). BBNs are not converted into ROBDDs; instead, a hybrid ROBDD/BBN is created. In this hybrid structure, the probability of one or more of the ROBDD variables is provided by a linked node in the BBN.
HCLA Developmental History
HCLA is under development with support from NASA's Jet Propulsion Laboratory for system reliability analysis of commercial off-the-shelf (COTS) usage in space systems.
Advanced quantification models:
Discrete nonparametric time-to-failure (e.g., output from MATLAB simulations)
Parsing of general expressions for failure mechanism models of time-to-failure (with global parameters)
Parameter Distributions: uniform, triangular, normal, lognormal, gamma, noncentral chi-squared, Cauchy, student T
Sampling from user-defined expressions
Sampling methods: Monte Carlo, Latin Hypercube (center, random, improved distributed), Quasi-Monte Carlo (Faure, Halton, Hammersley, Niederreiter, Sobol)
Post-processing of results:
Failure CDF, reliability CDF, failure PDF, hazard function, mean time-to-failure, confidence interval
Ranking of basic events by time-dependent importance measures: conditional probability, marginal, improvement potential, criticality, diagnostic, risk achievement worth, risk reduction worth