Predictive Maintenance for Improved Reliability in the Vaccine Cold Chain (B Medical)

A partnership project in collaboration with B Medical Systems to develop data-driven analytics for predictive maintenance in the vaccine cold chain.

The project at a glance

Start date:

01 Jul 2024
Duration in months:

48
Funding:

B Medical Systems
Principal Investigator(s):

Tegawendé François d Assise BISSYANDE

Jacques KLEIN

About

Industrial equipment typically begins in good working condition but deteriorates over time until it eventually fails and requires maintenance. Predicting such failures involves identifying potential issues at an early stage and implementing proactive measures to prevent them. This not only improves equipment reliability but also reduces maintenance costs and increases operational efficiency. This project centres on predictive maintenance within the vaccine cold chain of B Medical Systems, a company specialising in the manufacture of vaccine storage devices. Leveraging their real-time monitoring system—which uses sensors to collect and transmit data on device status—the project aims to analyse this data and develop AI-driven analytics. Specifically, the project will introduce novel time series mining techniques to infer load curves for B Medical’s equipment and provide maintenance instructions for solar panels. It will also focus on detecting anomalies and creating predictive maintenance models capable of forecasting failures and automatically classifying their causes. A key objective is to ensure the explainability of these models through Explainable AI (XAI), offering transparency into how predictions are made. Furthermore, the project seeks to improve interoperability by collecting publicly available datasets and enhancing cross-dataset integration.

B Medical Systems