Diagnostics and prognostics of multi-mode failure scenarios in miter gates using multiple data sources and a dynamic Bayesian network

Current health monitoring approaches for large structures mostly rely on a combination of distributed sensor networks and in-situ inspection. This paper presents a novel online diagnostics and prognostics framework for structures subject to multiple failure modes and demonstrates the proposed method...

Full description

Saved in:
Bibliographic Details
Published inStructural and multidisciplinary optimization Vol. 65; no. 9
Main Authors Wu, Zihan, Fillmore, Travis B., Vega, Manuel A., Hu, Zhen, Todd, Michael D.
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2022
Springer Nature B.V
Subjects
Advanced Optimization Enabling Digital Twin Technology
Bayesian analysis
Computational Mathematics and Numerical Analysis
Damage
Data sources
Dependent variables
Diagnosis
Engineering
Engineering Design
Failure
Failure modes
Finite element method
Inspection
Mitre gates
Parameter estimation
Prognosis
Research Paper
Strain gauges
Theoretical and Applied Mechanics
Miter gates
Digital twins
Surrogate models
Bayesian network
Crack
Online AccessGet full text

Cover

More Information
Summary:Current health monitoring approaches for large structures mostly rely on a combination of distributed sensor networks and in-situ inspection. This paper presents a novel online diagnostics and prognostics framework for structures subject to multiple failure modes and demonstrates the proposed method with a high-fidelity finite element model using multiple data sources (i.e., strain gages and images). The approach aims at an accurate simulation of the interaction between different failure features, and subsequently at the effective estimation and prediction of the damage states based on the generated structural physics. A dynamic Bayesian network is used which incorporates different data sources to evaluate the structures under different kinds of deterioration mechanisms. In diagnosis, the dynamic Bayesian network is used to approximate the damage-related parameters and estimate the time-dependent variables. In prognosis, the dynamic Bayesian network gives a probabilistic prediction of the remaining useful life of the structure based on the evolution of the failures. It is found that the proposed framework is highly effective in performing online diagnosis and prognosis using combined data sources.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-022-03381-z