Predictive Maintenance of VRLA Batteries in BTS Systems

Fri, 01 Aug 2025 00:00:00 +0000

This undergraduate project was developed in collaboration with Nian Electronic Company to implement predictive maintenance for Valve-Regulated Lead-Acid (VRLA) batteries in BTS systems. The project implements the methodology from the research paper “Predictive Maintenance of VRLA Batteries in UPS towards Reliable Data Centers”using a Gradient Boosted Decision Trees (GDBT) model.

The system predicts VRLA battery degradation in advance for the company’s BTS systems, helping prevent unexpected failures and optimize maintenance scheduling. Key features include:

Battery Health Monitoring: Real-time assessment of battery condition using performance metrics
Deterioration Prediction: Early warning system for battery replacement needs
Cost Optimization: Reduced downtime and extended battery lifespan through intelligent maintenance

The implementation processes battery voltage, internal resistance, temperature data, and historical maintenance records to provide accurate predictions for critical infrastructure applications.

Seizure Signal Classification

Thu, 01 Dec 2022 00:00:00 +0000

Developed a seizure detection system using EEG data and machine learning models including SVM, Random Forest, and CNNs.

Performed preprocessing, feature engineering, and K-fold validation to ensure accuracy and generalizability. Focused on biomedical signal analysis in practical ML settings.

You can view the project on GitHub: Seizure Signal Classification.

Machine Learning | mmrahneshin

Predictive Maintenance of VRLA Batteries in BTS Systems

Seizure Signal Classification