Research on Weighted Logical Inference for Uncertain Fault Diagnosis
Chunling Dong · Acta Automatica Sinica · 2014
To solve the problems of fault diagnosis of complex systems such as modeling complexity, computational overload, insufficient data, and incomplete knowledge and observations, this paper systematically investigates the algorithm and its mathematical foundation for weighted logical inference(WLI) by means of dynamic uncertain causality graph(DUCG). After introducing a novel mechanism of logic event inference that is accompanied by algebraic operation of weighting factor, WLI is characterized as self-relied chaining inference which guarantees the auto-normalization of variables state probabilities, providing solutions for compact and incomplete representation of multi-valued causalities.Since WLI is beyond the realm of classical mathematical logic in respects of incomplete information and high dimension of propositional truth value space, this paper presents its formal definitions, supplements the inference algorithms, and analyzes the operational features in order to ensure the theoretical rigorousness. Also, the theoretic self-containment and self-consistency are proven in detail. The results of algorithm analysis and fault diagnosis experiments indicate WLI s efficiency, accuracy, and less dependency on the preciseness of parameters and completeness of observations.