LAUSR

Continuous emission monitoring system (CEMS) has been widely used in many engineering fields. Common cause failures (CCFs) have remarkable effects on the system reliability of CEMS, because of shared work conditions and dependent failures for different components. A method for reliability evaluation of CEMS with CCFs is proposed based on fuzzy Failure Mode Effects and Criticality Analysis (FMECA) as well as Bayesian network (BN). By introducing the system composition and function principles of CEMS, the CEMS failure mode is clearly defined and the weak components of the system are identified. According to the hazard ranking of the CEMS failure modes, the places where reliability improvement or preventive maintenance should be implemented are found out. Then, BN-based reliability model of the sampling system, which is the weakest subsystem of CEMS, is constructed according to the results of a fault tree analysis. The behavior of CCF is further incorporated, and the α -factor model is used to evaluate the probability of CCF. Lastly, a numerical example is used to illustrate the proposed method. A comparison between the proposed method and the one without considering CCF is carried out. The result demonstrates that the proposed method has better reliability assessment accuracy for the CEMS with CCF than the one without considering CCF.