i4.0

Machine Health : Condition Monitoring & Predictive Maintenance

This project enables advanced maintenance for asset and equipment using digitalization, machine learning, web applications and real-time data.

Using the React framework, this solution offers real-time data visualization and machine learning-based anomaly detection. Real-world data from industrial settings validates the integrated approach, offering a robust solution for equipment health management, reducing downtime and cost.

SUPERVISORS:

Zhang Qi | Chung Ock Jin | Ong Hock San | Qua Pheng Thiam | Teo Chin Heng

TEAM MEMBERS:

Ahmad Khair Hadi Bin Ahmad Jamal | Au Yong Jun Yin | Ng Shi Qing

DIPLOMAS:

Diploma in Computer Engineering
Diploma in Electrical & Electronic Engineering

INDUSTRY PARTNERS:

SICK Pte. Ltd
Mitsubishi Electric Asia Pte Ltd

SP Sustainability Matters
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