Intelligent PREdictive Maintenance for Aquaculture Systems


Mr Piet Haerens, Haedes, Belgium



Project Abstract

Intelligent PREdictive Maintenance for Aquaculture Systems is a research project aiming to improve the performance of aquaculture farms by introducing a novel platform and service for intelligent predictive maintenance. The platform is based on innovative monitoring systems and smart infrastructure, relying on machine learning (ML) and artificial intelligence (AI) techniques.

The platform measures key parameters in real time introducing innovative multi-sensor gauges which feed a chain of ML models for Time Series Forecasting (TSF), Anomaly Detection (AD), Fault Classification (FC) and Remaining Useful Life (RUL) estimation. The measurements give the current health status of the farm site while the forecasts provide a glimpse of future status. The analysis of the predictions allow to identify the potential need of preventive/corrective maintenance.

A cloud-based integration of the different components of the platform allows to improve connectivity and safety while optimizing the business process which lets the farmers benefit from a tailor made Software as a Service (SaaS) solution. The SaaS approach empowers the aquaculture farmers by providing a digital twin of their facilities ‘in their pocket’. Blockchain technology will be used to provide trust and traceability, such as securely managing the sensor data information as well as the identity of the stakeholders. Security and privacy compliance with GDPR will be ensured by implementing reliable, secure data transport and access. The new service gives farmers secure real time access to the current health status of the farm and facilitates them planning activities and measures based on the forecasted status. Ultimately, iPREMAS pursues cutting M&O costs for the farmer and providing additional tools for reducing negative effects to the environment in case of calamity.

Project Start

July 2022
Project Duration

30 Months
Project Budget

Total Cost:  0.5 M€
Funding:     0.3 M€
Project Website