Research & Innovation Portfolio
Developing Artificial Intelligence, Industrial IoT, and Smart Energy solutions for predictive maintenance, real-time monitoring, and sustainable infrastructure.
Problem Statement
Traditional maintenance strategies are often reactive and rely on manual inspections.
This can result in:
- Unexpected equipment failures
- Increased maintenance costs
- Reduced operational reliability
- Energy production losses
Featured Project
AI-Driven Predictive Maintenance for Hydropower Plants
Overview
Hydropower plants often experience unexpected equipment failures that can lead to costly downtime and reduced energy production.
This project develops an AI-powered predictive maintenance framework that combines Industrial IoT, cloud computing, and machine learning to monitor hydraulic turbine health in real time and detect anomalies before failures occur.
Proposed Solution
An intelligent monitoring system integrating:
- 📡 IoT Sensors
- ☁ AWS Cloud Infrastructure
- 🧠 LSTM Deep Learning Models
- 📊 Real-Time Monitoring Dashboard
- 🚨 Automated Anomaly Detection
Realtime Supervision
Operator’s Dashboard at Kavumu plant
Real-time monitoring interface displaying:
- Turbine vibration
- Generator temperature
- Blade position
- Electrical parameters
- Anomaly alerts
350 kW Capacity production
Hydropower Real-Time Monitoring Dashboard
Open Realtime Anomaly DetectionTurbine’s Vibration
Cloud Infrastructure: Amazon Web Services (AWS EC2)
Real-Time Data Processing and Deployment Architecture
LSTM-Based Real-Time Anomaly Detection for Hydraulic Turbine Vibration
Research Contributions
✔ AI-based predictive maintenance framework
✔ Real-time anomaly detection
✔ Cloud deployment architecture
✔ Industrial IoT integration
✔ Time-series analytics
✔ Hydropower monitoring system