Reliability Modelling and Safety Learning Algorithms in Complex Risk Multifunctional Systems
Modelling safety procedures of complex risk systems of multifunctional production systems such as floating production storage and offloading (FPSO) vessels is typically rigorous. Deterministic modelling and Learning algorithms are normally used to generate whole sets of hazard data based on data of intrinsic risk events and safety measures incorporated. The model developed use failure data systems obtained from operator of multifunctional production systems of FPSO to generate fuzzy class surrogates based on learning algorithms to rank safety index. Thus classifications of risk events in a fuzzy set of system is predicted used weighted like hood of failure of human, process, mechanical, electrical, operational, in composite risk system to set the safety thresholds. The model used a learning constraint function in probable risk outcomes to match retroactively weights index of actual scenarios in skewed hazard surrogates to specific risk and safety ratings criteria. The MTBR (Mean Time before Repair) to plan maintainability studies and safety programmes were simulated to an optimal repair range from almost 0.5 yrs for worst case; fuzzy class 1 with safety rating of 0.0 to almost 5 million years for best case when the fuzzy class 5 with safety index rating of 1.0 assume availability is 80%.
