Research & Innovation Portfolio
Developing Artificial Intelligence, Industrial IoT, and Smart Energy solutions for predictive maintenance, real-time monitoring, and sustainable infrastructure.
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.
Project Information
- Role: Lead Researcher
- Domain: Renewable Energy
- Technologies: Python, AWS, IoT, LSTM, LabVIEW
- Case Study: Kavumu Hydropower Plant
- Focus: Predictive Maintenance
- Status: Ongoing Research
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
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