Centrifugal pumps represent 70% of all kinds of pumps and are ubiquitous in the industrial world especially in heating, air conditioning and sewage applications. Although modern pumps can last for many years, their sudden failure can lead to undesirable or catastrophic disruptions.

The purpose of this project is to develop a low-cost IoT based predictive maintenance solution to continuously monitor the pump health using motor current signature analysis (MCSA) and predict failures using a combination of signal processing and machine-learning algorithms.An MCSA monitoring system is deployed by attaching current clamps, used as transducers, to power supply wires without requiring direct physical access to the pump itself. The proposed system consists of custom hardware modules that stream the pump data to the cloud, and a back-end for storage, visualisation and intelligent analysis.

The project is a collaboration with Uptime Systems Ltd and was funded by Innovate UK (2019-2023).

More details on the Early Detection of Electric Pump Failures project page

This project was funded internally under the England Call fund scheme  to use innovative models and methodologies for participatory research. One of the project aims was to start a new research theme within the engineering cluster, around the filtration of industrial sludge. This is now one of the active themes of research activities within IRESE, and continues to expand collaboration with industrial partners through new investigation, bid writing and consultancy work.

This project aims to develop an optimal control strategy for wind turbines using a method called Model Predictive Control (MPC). Wind turbines are complex machines that convert wind energy into electricity, and their performance is influenced by factors such as wind speed, rotor speed, and turbine settings. The challenge is ensuring that these turbines operate efficiently, safely, and reliably, especially under varying conditions like changing wind speeds and gusts.

The core idea of this project is to use a predictive approach where the system continuously forecasts how the turbine will behave and adjusts its settings to achieve optimal performance. By doing so, we can maximize energy production while reducing mechanical wear and tear, which leads to longer-lasting turbines and more stable energy output.

However, a key challenge is that the model used to predict the turbine’s behavior might not always match real-world conditions, resulting in discrepancies and errors in control. This project will focus on developing a truly effective model predictive control strategy for wind turbines and explore novel approaches to address these discrepancies. The ultimate goal is to make wind energy generation more efficient, reliable, and sustainable, supporting the growing demand for clean energy with minimal environmental impact.

Contact: Imran Ghous at Imran.Ghous@beds.ac.uk

This project aims to develop a smarter control system for robotic arms, which are commonly used in industries like manufacturing, healthcare, and logistics. Robotic arms are complex machines that often face challenges like unexpected changes in their surroundings, carrying different weights, or handling unknown objects. These factors can affect how precisely and reliably they work.

The goal is to design a control system that adjusts itself in real-time to keep the robotic arm steady and precise, even in uncertain conditions. This system will use a method called fractional-order sliding mode control, which is better at handling sudden changes and complex movements compared to traditional methods. It also helps reduce unnecessary movements, improving accuracy and efficiency while reducing wear and tear on the robotic arm.

One of the main challenges is dealing with the unpredictable nature of the robotic arm’s environment, like unknown forces or unexpected changes. The project focuses on developing a control system that can automatically adapt to these changes, ensuring the robotic arm works reliably and enjoys a longer lifespan. Ultimately, this will make robotic arms more versatile and practical in real-world applications.

Contact: Imran Ghous at Imran.Ghous@beds.ac.uk