Probabilistic Fault Recoverability(FR) property reveals the capability of a system to accommodate faults under admissible input energy constraints in the sense of satisfactory probability. Motivated by the idea of pro...Probabilistic Fault Recoverability(FR) property reveals the capability of a system to accommodate faults under admissible input energy constraints in the sense of satisfactory probability. Motivated by the idea of probabilistic control methods, a class of admissible probability density functions is designed for detailed description of fault parameters, under which several probabilistic FR conditions are established. This significantly enlarges the range of recoverable faults obtained from the deterministic FR analysis. The tradeoffs between the risk of performance degradation and this increased recoverability margin are exactly achieved by allowing a small risk of FR violation. This paper analyzes the probability FR of dynamic systems with switching and interconnection characteristics, and applies the new results to several aircraft models including single longitudinal aircraft dynamic, Highly Maneuverable Technology(HiMAT) vehicle and meta aircraft. Simulation results show the efficiency of the proposed methods based on the comparison between deterministic and probabilistic cases.展开更多
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 i...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.展开更多
基金supported by National Natural Science Foundation of China (Nos. 61773201, 62073165)the 111 Project,China (No. B20007)the Fundamental Research Funds for the Central Universities, China (No. NZ2020003)。
文摘Probabilistic Fault Recoverability(FR) property reveals the capability of a system to accommodate faults under admissible input energy constraints in the sense of satisfactory probability. Motivated by the idea of probabilistic control methods, a class of admissible probability density functions is designed for detailed description of fault parameters, under which several probabilistic FR conditions are established. This significantly enlarges the range of recoverable faults obtained from the deterministic FR analysis. The tradeoffs between the risk of performance degradation and this increased recoverability margin are exactly achieved by allowing a small risk of FR violation. This paper analyzes the probability FR of dynamic systems with switching and interconnection characteristics, and applies the new results to several aircraft models including single longitudinal aircraft dynamic, Highly Maneuverable Technology(HiMAT) vehicle and meta aircraft. Simulation results show the efficiency of the proposed methods based on the comparison between deterministic and probabilistic cases.
基金supported by the National Natural Science Foundation of China(Nos.61050005 and 61273330)Research Foundation for the Doctoral Program of China Ministry of Education(No.20120002110037)+1 种基金the 2014 Teaching Reform Project of Shandong Normal UniversityDevelopment Project of China Guangdong Nuclear Power Group(No.CNPRI-ST10P005)
文摘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.
