A Modeling and Probabilistic Reasoning Method of Dynamic Uncertain Causality Graph for Industrial Fault Diagnosis

Abstract

Abstract:Online automatic fault diagnosis in industrial systems is essential for guaranteeing safe, reliable and efficient operations. However, difficulties associated with computational overload, ubiquitous uncertainties and insufficient fault samples hamper the engineering application of intelligent fault diagnosis technology. Geared towards the settlement of these problems, this paper introduces the method of dynamic uncertain causality graph, which is a new attempt to model complex behaviors of real-world systems under uncertainties. The visual representation to causality pathways and self-relied "chaining" inference mechanisms are analyzed. In particular, some solutions are investigated for the diagnostic reasoning algorithm to aim at reducing its computational complexity and improving the robustness to potential losses and imprecisions in observations. To evaluate the effectiveness and performance of this method, experiments are conducted using both synthetic calculation cases and generator faults of a nuclear power plant. The results manifest the high diagnostic accuracy and efficiency, suggesting its practical significance in large-scale industrial applications.

AI-aided general clinical diagnoses verified by third-parties with dynamic uncertain causality graph extended to also include classification

“…DUCG developed in recent years is such a model (Zhang 2012 , 2015a , b ; Zhang et al 2014 , 2018 ; Zhang and Geng 2015 ; Zhang and Zhang 2016 ; Zhang and Yao 2018 ) and has achieved promising application results for fault diagnoses of large, complex industrial systems (Zhang and Yao 2018 ; Zhang et al 2018 ; Dong et al 2014a , 2018 ; Qu et al 2015 ; Zhao et al 2014 ; Geng and Zhang 2014 ) and general clinical diagnoses (Dong et al 2014b ; Hao et al 2017 ; Fan et al 2018 ; Jiao et al 2020 ; Ning et al 2020 ; Zhang et al 2021 ).…”

Intelligent diagnosis of jaundice with dynamic uncertain causality graph model

“…The greatest advantage of DUCG in clinical diagnosis is that it can display the reasoning process and results graphically, and make an inference with incomplete information and less accurate parameters than conventional methods such as Bayesian Networks. The DUCG model has been applied in the clinical diagnosis of vertigo (Dong et al, 2014a) and for troubleshooting in nuclear power station electric generators, spacecraft power systems, and chemical process systems (Dong et al, 2014b) with competitive results.…”