Sökning: "skadedetektering"

Hittade 3 uppsatser innehållade ordet skadedetektering.

  1. 1. Application of Lamb waves using piezoelectric technique for structure health monitoring

    M1-uppsats, KTH/Hälsoinformatik och logistik

    Författare :Simon Mauritz; [2023]
    Nyckelord :Structural health monitoring SHM ; damage detection; Lamb waves; piezoelectric PZT ; printed circuit board PCB ; circuit simulation; Strukturell hälsoövervakning SHM ; skadedetektering; Lambvågor; piezoelektrisk PZT ; printed circuit board PCB ; kretssimulering;

    Sammanfattning : Structural health monitoring (SHM) is damage detection strategy for aerospace, civiland mechanical infrastructure. This project tries to show that Lamb waves, that are being generated and sensed with piezoelectric transducers, can be used for damage detection in a SHM system. LÄS MER

  3. 2. Analysis of design requirements for early failure detection in a gear test rig

    Master-uppsats, KTH/Skolan för industriell teknik och management (ITM)

    Författare :José Agustín Spaccesi; [2020]
    Nyckelord :Design requirements; Failure detection; FZG test rig; Gear; Konstruktionskrav; Skadedetektering; FZG-rigg; Kuggväxlar; Engranajes; Detección de fallo; Requisitos de diseño; Equipo de prueba FZG;

    Sammanfattning : Gears are the heart of many machines, being its function transform and transmit torque. This work is a study of adequate design requirements, in particular, the best methodology to early detect gear fatigue failure using a gear test rig, an FZG test machine. LÄS MER

  4. 3. Quantifying uncertainty in structural condition with Bayesian deep learning : A study on the Z-24 bridge benchmark

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :David Steinar Asgrimsson; [2019]
    Nyckelord :Bayesian Deep Learning; Autoencoders; Bridge Structural Health Monitoring; Bridge Damage Detection;

    Sammanfattning : A machine learning approach to damage detection is presented for a bridge structural health monitoring system, validated on the renowned Z-24 bridge benchmark dataset where a sensor instrumented, threespan bridge was realistically damaged in stages. A Bayesian autoencoder neural network is trained to reconstruct raw sensor data sequences, with uncertainty bounds in prediction. LÄS MER