Dynamic Bayesian Network for Aircraft Wing Health Monitoring Digital Twin

Current airframe health monitoring generally relies on deterministic physics models and ground inspections. This paper uses the concept of a dynamic Bayesian network to build a versatile probabilistic model for diagnosis and prognosis in order to realize the digital twin vision, and it illustrates t...

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Bibliographic Details
Published in:AIAA journal 2017-03, Vol.55 (3), p.930-941
Main Authors: Li, Chenzhao, Mahadevan, Sankaran, Ling, You, Choze, Sergio, Wang, Liping
Format: Article
Language:English
Subjects:
Aerodynamics
Aging aircraft
Aircraft
Aircraft components
Airframes
Bayesian analysis
Continuity (mathematics)
Crack propagation
Dependent variables
Diagnosis
Diagnostic systems
Digital twins
Dynamics
Evidence
Fatigue
Fatigue failure
Fracture mechanics
Independent variables
Mathematical models
Nodes
Nonlinear dynamics
Physics
Probabilistic models
Prognosis
State variable
Statistical inference
Structural health monitoring
Time dependence
Variables
Wings (aircraft)
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Summary:Current airframe health monitoring generally relies on deterministic physics models and ground inspections. This paper uses the concept of a dynamic Bayesian network to build a versatile probabilistic model for diagnosis and prognosis in order to realize the digital twin vision, and it illustrates the proposed method by an aircraft wing fatigue crack growth example. The dynamic Bayesian network integrates physics models and various aleatory (random) and epistemic (lack of knowledge) uncertainty sources in crack growth prediction. In diagnosis, the dynamic Bayesian network is used to track the evolution of the time-dependent variables and calibrate the time-independent variables; in prognosis, the dynamic Bayesian network is used for probabilistic prediction of crack growth in the future. This paper also proposes a modification to the dynamic Bayesian network structure, which does not affect the diagnosis results but reduces the time cost significantly by avoiding Bayesian updating with load data. By using a particle filter as the Bayesian inference algorithm for the dynamic Bayesian network, the proposed approach handles both discrete and continuous variables of various distribution types, as well as nonlinear relationships between nodes. Challenges in implementing the particle filter in the dynamic Bayesian network, where 1) both dynamic and static nodes exist and 2) a state variable may have parent nodes across two adjacent networks, are also resolved.
ISSN:0001-1452
1533-385X
DOI:10.2514/1.J055201