Anomaly Detection Using Vibration data and auto encoder

In this session Dr. Ibrahim El-Fayoumi will share the work with a Factory in China, using Autoencoder to detect anomaly in their motor (cracks in shaft or ball bearing) that ruin production and using vibration data.

About the Speaker:
Dr. Ibrahim El-Fayoumi is a seasoned AI researcher and software engineer with over 15 years of experience in developing cutting-edge machine learning solutions. He is an active leader in the Australian technology community, serving as a long-term speaker and organizer for the Perth Machine Learning Group since 2017.

With a strong academic background including a PhD from Flinders University, research associate at Columbia University, Ibrahim has a distinguished track record in complex signal processing and physics-informed neural networks. His expertise spans across deep learning, time-series forecasting, and anomaly detection, with notable projects involving Variational Autoencoders (VAEs), Graph Neural Networks, and LSTM-based predictive models.

Ibrahim's work often bridges the gap between theoretical research and industrial application. His upcoming talk at the Perth Machine Learning Group focuses on Anomaly Detection using Vibration Analysis, adaptive Fourier transform and Autoencoders, leveraging his extensive experience in unsupervised learning and high-dimensional data analysis to solve real-world monitoring challenges.

Agenda:

5pm - arrival and mingle
5.30pm - presentation start
7pm - wrap up