Haseeb Zaidi

Haseeb Zaidi

Email: b6044396@hallam.shu.ac.uk
Research Centre: NCEFE
PhD Thesis Title: Machine learning for fault and anomaly detection in food and drink manufacturing
Director of Studies: Professor Alex Shenfield
Supervisors: Dr Hongwei Zhang & Dr Augustine Ikpehai

SUMMARY

Originally from Pakistan, I completed a Bachelor’s in Electrical and Computer Systems Engineering before moving to the UK for postgraduate studies at Sheffield Hallam University in Computer and Network Engineering. I then earned an MSc in AI and Data Science from the University of Hull.

Currently, I am pursuing a PhD at Sheffield Hallam University on a GTA scheme. My research interests include AI, specifically machine learning and deep learning, with a focus on their applications in fault and anomaly detection in manufacturing.

I am passionate about leveraging AI to improve industrial processes and enhance operational efficiency.

RESEARCH

With the advent of Industry 4.0, the vast majority of data generated in food and drink manufacturing processes is now readily available. Leveraging this data, machine learning (ML) techniques can significantly enhance fault and anomaly detection, ensuring product quality and operational efficiency.

Advanced ML algorithms analyze extensive production data to identify deviations from normal patterns, pinpointing potential faults or anomalies in real time. This improves the accuracy of detecting issues such as equipment malfunctions, contamination, or packaging defects and enables predictive maintenance, reducing downtime and production costs.

Consequently, ML-driven fault detection systems enhance the industry’s operational reliability and efficiency by minimizing disruptions and maximizing uptime. This results in more consistent production quality and lower operational costs.

For consumers, these improvements ensure safer and higher-quality products, fostering greater trust and satisfaction.