[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Parola et al., 2022 - Google Patents

Convolutional Neural Networks for Structural Damage Localization on Digital Twins

Parola et al., 2022

View PDF
Document ID
13444782249685954552
Author
Parola M
Galatolo F
Torzoni M
Cimino M
Publication year
Publication venue
International Conference on Deep Learning Theory and Applications

External Links

Snippet

Structural health monitoring (SHM) using IoT sensor devices plays a crucial role in the preservation of civil structures. SHM aims at performing an accurate damage diagnosis of a structure, that consists of identifying, localizing, and quantify the condition of any significant …
Continue reading at drive.google.com (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design
    • G06F17/5009Computer-aided design using simulation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/005Probabilistic networks
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B15/00Systems controlled by a computer
    • G05B15/02Systems controlled by a computer electric
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00Testing or monitoring of control systems or parts thereof
    • G05B23/02Electric testing or monitoring
    • G05B23/0205Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218Electric 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
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00Systems involving the use of models or simulators of said systems
    • G05B17/02Systems involving the use of models or simulators of said systems electric

Similar Documents

Publication Publication Date Title
Ritto et al. Digital twin, physics-based model, and machine learning applied to damage detection in structures
Azimi et al. Structural health monitoring using extremely compressed data through deep learning
Tibaduiza et al. A damage classification approach for structural health monitoring using machine learning
Cross et al. Physics-informed machine learning for structural health monitoring
Sony et al. Multiclass damage identification in a full-scale bridge using optimally tuned one-dimensional convolutional neural network
Guo et al. Structural health monitoring by using a sparse coding-based deep learning algorithm with wireless sensor networks
Bigoni et al. Systematic sensor placement for structural anomaly detection in the absence of damaged states
Vlassis et al. Component-based machine learning paradigm for discovering rate-dependent and pressure-sensitive level-set plasticity models
Chen et al. A novel Bayesian-optimization-based adversarial TCN for RUL prediction of bearings
Rosafalco et al. A self-adaptive hybrid model/data-driven approach to SHM based on model order reduction and deep learning
Rosafalco et al. Combined model order reduction techniques and artificial neural network for data assimilation and damage detection in structures
Zhao et al. Multiclass anomaly detection in imbalanced structural health monitoring data using convolutional neural network
Bai et al. Application of support vector machine-based classification extremum method in flexible mechanism
Iqbal et al. Efficient training of transfer mapping in physics-infused machine learning models of UAV acoustic field
Sisson et al. Digital twin for component health-and stress-aware rotorcraft flight control
Oh et al. A bayesian learning method for structural damage assessment of Phase I IASC-ASCE benchmark problem
Parola et al. Convolutional Neural Networks for Structural Damage Localization on Digital Twins
Liang et al. Civil infrastructure serviceability evaluation based on big data
Bigoni et al. Predictive monitoring of large-scale engineering assets using machine learning techniques and reduced-order modeling
Diaz et al. Fully automated model updating framework for damage detection based on the modified Constitutive Relation Error
Kunzer et al. The digital twin landscape at the crossroads of predictive maintenance, machine learning and physics based modeling
Singh et al. Hierarchical Neural Network and Simulation Based Structural Defect Identification and Classification
Kumar et al. Multiple layer radial basis neural network with remora regression tree optimum feature extraction for structural health monitoring
Huang et al. A hybrid FCN-BiGRU with transfer learning for low-velocity impact identification on aircraft structure
Parola et al. Structural Damage Localization via Deep Learning and IoT Enabled Digital Twin.