SESSION 4 – Structural Health Monitoring – Data Analytics – HBK Technology Days 2022 Virtual Seminar Series
HBK Technology Days 2022 – Virtual Seminar Series
Session 4: Structural Health Monitoring – Data Analytics
Overview
The value of structural health monitoring (SHM) is realised through analysis of structural response measurements. Such analyses can include structural characterisation, identification of trends and divergence from trends, calculation of cumulative usage indicators, predictive estimation of remaining structural life and more. These data analytics quantify the overall structural condition to inform decision making for continual safe operation, predictive maintenance planning, and replacement planning
Successful and efficient implementation of SHM data analytics requires many steps from measurement data acquisition, validation & cleaning, database ingress, calculating operational parameters from physical and statistical models, investigative and retrospective analysis, visualisation and reporting.
These presentations show applications of such models with machine learning for SHM of bridges and civil infrastructure, certification of machine learning for remaining useful life of aircraft landing gear, and SHM of railway infrastructure and rail vehicles.
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Machine learning for structural health monitoring and the role of white/grey/black box models
Prof. Elizabeth Cross, University of Sheffield | Dynamics Research Group
To be confirmed
Uncertainties and risk management for certification of machine learning & LG RUL Assessment
Haroun El Mir, PhD Researcher, Transport Systems, Cranfield University
Landing Gear Systems on Aircraft undergo a multitude of forces during their life cycle, leading to the eventual replacement of this system based on a ‘safe life’ approach that certain circumstances underestimate the component’s remaining useful life.
The efficacy of fatigue life approximation methodologies is studied and compared to the ongoing Structural Health Monitoring techniques being researched, which will forecast failures based on the system’s specific life and withstanding abilities, ranging from creating a digital twin to applying neural network technologies, in order to simulate and approximate locations and levels of failure along the structure.
Explainable Artificial Intelligence allows for the ease of integration of Deep Neural Network data into Predictive Maintenance, which is a procedure focused on the health of a system and its efficient upkeep via the use of sensor-based data. Test data from a flight includes a multitude of conditions and varying parameters such as the surface of the landing strip as well as the aircraft itself…
Structural Health Monitoring of Civil Engineering Structures, Railway Tracks and Rail Vehicles for Asset Health Monitoring and Predictive Maintenance
Dietmar Maicz, ARGOS Systems| Asset Health Monitoring, Predictive Maintenance, Infrastructure and Railway
To be confirmed
Free ONLINE Seminar - 90 minutes
November 15, 2022
10:00 am - 12:00 pm
905-839-9960