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ENHAnCE

European training Network in intelligent prognostics and Health mAnagement in Composite structurEs
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Uncertainty quantification in Neural Networks by Approximate Bayesian Computation: Application to fatigue in composite materials.

Particle filter-based hybrid damage prognosis considering measurement bias.

Reduction of Petri net maintenance modeling complexity via Approximate Bayesian Computation.

Risk-based maintenance strategy selection for wind turbine composite blades.

Numerical simulation-aided particle filter-based damage prognosis using Lamb waves.

Structural digital twin framework: Formulation and technology integration.

Guided waves-based damage identification in plates through an inverse Bayesian process. 

Particle filter-based delamination shape prediction in composites subjected to fatigue loading

A cross-sectorial review of the current and potential maintenance strategies for composite structures

Asset management modelling approach integrating structural health monitoring data for composite components of wind turbine blades

Self-adaptative optimized maintenance of offshore wind turbines by intelligent Petri nets

An assessment of different reinforcement learning methods for creating a decision support system based on the Petri Net model

Probabilistic safety Assesment in Composite Materials using BNN by ABC-SS

Interpretable neural network with limited weights for constructing simple and explainable HI using SHM

Intelligent health indicator construction for prognostics of composite structures utilizing a semi-supervised deep neural network and SHM data.

Intelligent health inidicator based on semi-supervised learning utilizing acoustic emission data.

A wind turbine blade leading edge rain erosion computational framework.

Physics-guided Bayesian neural networks by ABC-SS: Application to reinforced concrete.

Damage identification and Identification in Structural Joints through Ultrasonic Guided Wave-Based Features and an Inverse Bayesian Scheme.

An Efficient Procedure for Bonding Piezoelectric Transducers to Thermoplastic Composite Structures for SHM Application and Its Durability in Aeronautical Environmental Conditions.

Acousto-ultrasonic composite transducers integration into thermoplastic composite structures via ultrasonic welding.

Novel Procedure of Integrating Transducers to Thermoplastic Composite Structures by Induction Heating for SHM. 

Developing health indicators for composite structures based on a two-stage semi-supervised machine learning model using acoustic emission data.

Particle filter-based damage prognosis by online feature fusion and selection.

An asset management framework for wind turbine blades considering reliability of monitoring system.

Intelligent and adaptive asset management model for railway sections using the IPN method.

Optimized Petri net model for condition-based maintenance of a turbine blade.

An optimized asset management Petri net model for railway sections.

Reduction of Petri net maintenance modeling complexity via approximate Bayesian computation.

Optimal computation of integrals in the Half-Space Matching method for modal simulations of SHM/NDE in 3D elastic plate

Prediction of shape distortions in thermosetting composite parts using neural network interfaced visco-elastic constitutive model

Particle filter-based prognostics for composite curing process

A Novel machine learning model to design historical-independent health indicators for composite structures

Multiple local particle filter for high-dimensional system identification

Particle filter-based fatigue damage prognosis by fusing multiple degradation models

Reliability-based leading edge erosion maintenance strategy selection framework

Damage Quantification and Identification in Structural Joints through Ultrasonic Guided Wave-Based Features and an Inverse Bayesian Scheme

A general approach to assessing SHM reliability considering sensor failures based on information theory

Physics-guided recurrent neural network trained with approximate Bayesian computation: A case study on structural response prognostics

Training of physics-informed Bayesian neural networks with ABC-SS for prognostic of Li-ion batteries

Reduction of Petri net maintenance modelling complexity via approximate Bayesian computation

Intelligent Health Indicators Based on Semi-supervised Learning Utilizing Acoustic Emission Data

Probabilistic safety assessment in composite materials using BNN by ABC-SS

 

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Final meeting of ENHAnCE

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Public Deliverables

Download here our public deliverables and demonstrators from ENHAnCE: D-8.9.-End of Year 3 Summary Report & ENHAnCE achievements-ENHAnCE D-8.11-Potential inputs for policy feedback  

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Contact: COORDINATOR@H2020-ENHANCEITN.EU & MANAGER@H2020-ENHANCEITN.EU
This project has received funding from the European Union’s. Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 859957.
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