# Incremental Clustering for Predictive Maintenance in Cryogenics for Radio Astronomy

## TL;DR

This paper introduces a system using AI and incremental clustering to monitor and predict maintenance needs in cryogenic systems for radio astronomy.

## Contribution

A novel unsupervised predictive maintenance system using incremental clustering to adapt to new anomalies in cryogenic cooling systems.

## Key findings

- The system uses Hall effect sensors and a microcontroller to monitor cold head motor currents.
- Incremental clustering allows the model to adapt and improve over time by forming new categories for new anomalies.
- The approach ensures reliable and precise long-term monitoring of cryogenic system health.

## Abstract

In a cooling system for radio astronomy receivers, maintaining cold heads and compressors is essential for consistent performance. This project focuses on monitoring the power currents of the cold head’s motor to address potential mechanical deterioration, which could jeopardize the overall functionality of the system. Using Hall effect sensors, a microcontroller-based electronic board, and artificial intelligence, the system detects and predicts anomalies. The model operates using an unsupervised approach based on incremental clustering. Since potential fault scenarios can be multiple and often challenging to simulate or identify during training, the system is initially trained using known operational categories. Over time, the system adapts and evolves by incorporating new data, which can be assigned to existing categories or, in the case of new anomalies, form new categories. This incremental approach enables the system to enhance its performance over the years, adapting to new anomaly scenarios and ensuring precise and reliable monitoring of the cold head’s health.