Sankararaman et al., 2013 - Google Patents
Bayesian methodology for diagnosis uncertainty quantification and health monitoringSankararaman et al., 2013
View PDF- Document ID
- 13512420050118626128
- Author
- Sankararaman S
- Mahadevan S
- Publication year
- Publication venue
- Structural Control and Health Monitoring
External Links
Snippet
This paper develops a Bayesian approach for the continuous quantification and updating of uncertainty in structural health monitoring. The uncertainty in each of the three steps of damage diagnosis—detection, localization, and quantification—is considered. Bayesian …
- 238000011002 quantification 0 title abstract description 69
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B17/00—Systems involving the use of models or simulators of said systems
- G05B17/02—Systems involving the use of models or simulators of said systems electric
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Sankararaman et al. | Bayesian methodology for diagnosis uncertainty quantification and health monitoring | |
Fassois et al. | Time-series methods for fault detection and identification in vibrating structures | |
Kang et al. | Concrete dam deformation prediction model for health monitoring based on extreme learning machine | |
Cross et al. | Cointegration: a novel approach for the removal of environmental trends in structural health monitoring data | |
Cheung et al. | Calculation of posterior probabilities for Bayesian model class assessment and averaging from posterior samples based on dynamic system data | |
Jha et al. | Particle filter based hybrid prognostics for health monitoring of uncertain systems in bond graph framework | |
Lam et al. | Markov chain Monte Carlo‐based Bayesian method for structural model updating and damage detection | |
Huang et al. | Sensor fault diagnosis for structural health monitoring based on statistical hypothesis test and missing variable approach | |
Sun et al. | Statistical regularization for identification of structural parameters and external loadings using state space models | |
Sankararaman et al. | Uncertainty quantification in structural damage diagnosis | |
Ye et al. | A Bayesian approach to condition monitoring with imperfect inspections | |
Orchard et al. | A particle filtering-based framework for real-time fault diagnosis and failure prognosis in a turbine engine | |
Das et al. | Structural health monitoring techniques implemented on IASC–ASCE benchmark problem: a review | |
Daneshvar et al. | Early damage detection under massive data via innovative hybrid methods: application to a large-scale cable-stayed bridge | |
Hou et al. | Sparse Bayesian learning for structural damage detection using expectation–maximization technique | |
Kaloop et al. | Multi input–single output models identification of tower bridge movements using GPS monitoring system | |
D'Amico et al. | Wind speed modeled as an indexed semi‐Markov process | |
Benmoussa et al. | Remaining useful life estimation without needing for prior knowledge of the degradation features | |
Bartram et al. | Integration of heterogeneous information in SHM models | |
Huang et al. | Damage identification of a steel frame based on integration of time series and neural network under varying temperatures | |
Zhou et al. | Structural identification of a concrete-filled steel tubular arch bridge via ambient vibration test data | |
Zheng et al. | Bayesian probabilistic framework for damage identification of steel truss bridges under joint uncertainties | |
Reda Taha | A Neural‐Wavelet Technique for Damage Identification in the ASCE Benchmark Structure Using Phase II Experimental Data | |
Qian et al. | Surrogate-assisted seismic performance assessment incorporating vine copula captured dependence | |
Prakash et al. | Bayesian two-phase gamma process model for damage detection and prognosis |