Assessing the risk of vibration-induced fatigue in process pipework using convolutional neural networks
<p>This study develops convolutional neural networks (CNNs) to classify pipework vibration states in process plants, aiming to assess the risk of vibration-induced fatigue (VIF). A major challenge in VIF assessment is the need for strain measurements which, while ideal for assessing the risk o...
Saved in:
| Main Author: | Ahmed Mohamed (628889) (author) |
|---|---|
| Other Authors: | Jamil Renno (14070771) (author), M. Shadi Mohamed (18810406) (author) |
| Published: |
2025
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Hybrid AI Approach for Fault Detection in Induction Motors Under Dynamic Speed and Load Operations
by: Muhammad Irfan Ishaq (22564652)
Published: (2025) -
PCOS-WaveConvNet: A Wavelet Convolutional Neural Network for Polycystic Ovary Syndrome Detection using Ultrasound images
by: Tiwari, Shamik
Published: (2023) -
A Wavelet-Based Analysis for Monitoring Controller Reliability in Active Magnetic Bearing With Rotor Eccentricities
by: Debarghya Dutta (22282726)
Published: (2024) -
Binarization of Degraded Document Images Using Convolutional Neural Networks and Wavelet-Based Multichannel Images
by: Younes Akbari (16303286)
Published: (2020) -
Transformer Differential Protection using Deep Convolutional Neural Networks
by: Lutfi, Abdulla Eyad
Published: (2019)