A modeling method of hybrid fault early warning model and hybrid fault early warning model

Abstract

The embodiment of the invention provides a modeling method of an early warning model of mixed failures and a modeling system. The modeling method provided by the invention comprises the following steps of: generating a function analyzing module on the basis of HAZOP (Hazard and Operability Analysis) or FMEA (Failure Mode and Effects Analysis); generating a degeneration analyzing module on the basis of FMEA analyzing results and a theory of stochastic processes; generating an accident analyzing module according to state monitoring data and maintenance action information; generating an action analyzing module according to output results of the function analyzing module and the degeneration analyzing module through combining a DBN (Dynamic Bayesian Network) theory; taking the output of the accident analyzing module as an inference evidence and utilizing a DBN inference algorithm to process forward and backward inferences in the same time period to generate an evaluating module for outputting factors and consequences of system failures; taking the output results of the evaluating module and the accident analyzing module as the inference evidence and utilizing the DBN inference algorithm to process forward and backward inferences in the different time periods to generate a predicating module for outputting prospective degeneration tendencies of each member of the system. The model provided by the invention can be used for tracking the failure factors of the system and inferring possible failure consequences and probability.

Description

technical field [0001] The invention relates to the technical field of safety engineering, in particular to a modeling method of a hybrid fault early warning model and a hybrid fault early warning model. Background technique [0002] With the continuous development of condition monitoring technology, more and more experts and scholars are committed to the detection and classification of system faults, and have developed many mature condition monitoring and diagnosis software and hardware systems. But "all diseases are not as good as one prevention", in order to improve the intrinsic safety of the system, it is necessary to fundamentally avoid the conditions of failure through fault warning. Existing methods tend to focus on the study of the degradation mechanism and remaining life of a single component or an independent subsystem. However, most complex systems do not operate in a stable environment like a laboratory, but are affected by various internal and external random ...

AI Technical Summary

Problems solved by technology

The fault coupling effect of a complex system is actually a network based on the causal chain of faults. The faults of one or a few nodes (which may be random or caused by human factors) will be caused by the coupling between network nodes. Relationships cause other nodes to fail, eventually leading to the collapse of a considerable number of nodes or even the entire network

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