scholarly journals Identifying Critical Failure-Propagation in Function Models of Complex Systems

Author(s):  
Yann Guillouët ◽  
Frank Sill Torres
2021 ◽  
Author(s):  
Yann Guillouet ◽  
Oliver Keszocze ◽  
Frank Sill Torres

2014 ◽  
Vol 631-632 ◽  
pp. 265-270
Author(s):  
Qiang Sun ◽  
Jian Jiao ◽  
Shan Shan Zhou

To identify the component failure propagation path in complex systems, a component failure logical model based on DEVS was established by combining the failure logic principle and DEVS formal specification at the next granularity level of component hierarchy. A series of components failure states define a potential hazard. DEVS has modeling advantages of standardized, hierarchical and modular. Basing on its own characteristics,it can be used to study failure logic modeling techniques of complex systems, model and analyse the scene of the accident process. Therefore, the paper describes syntax and semantics of component failure logic and atomic DEVS, focusing on the implementation mechanism of failure logic using DEVS, the establishment of component state space, the trigger mechanism and the corresponding output in order to establish a component failure logical model based on DEVS. At last, a Wheel Brake System is used to verify the applicability and validity of the model.


2019 ◽  
Vol 69 (5) ◽  
pp. 481-488
Author(s):  
Changchang Che ◽  
Huawei Wang ◽  
Xiaomei Ni

Failure propagation is a critical factor for the reliability and safety of complex systems. To recognise and identify failure propagation of systems, a deep fusion model based on deep belief network (DBN) and Bayesian structural equation model (BSEM) is proposed. The deep belief network is applied to extract features between status monitoring data and the performance degradation in different failure components. To calculate the path weight of failure propagation, the Bayesian structural equation model is proposed to study the relationship among different fault modes. After getting the performance degradation of each fault through DBN and calculating the path weight of fault propagation by BSEM, it is available to get the overall reliability of the system. The aircraft landing gear system with 19 fault patterns is selected to evaluate the feasibility of the proposed deep fusion model. The results demonstrate that the overall reliability of the system can be obtained by analysing the fault propagation of multiple fault patterns, and the proposed model has a lower deviation than traditional back propagation neural network.


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