Calvin  Stephen

Calvin Stephen

Ph.D. Student

Research Assistant

Mr. Calvin Stephen is an Engineer and obtained his BSc (Hons) in Aircraft Maintenance Engineering from the University of South Wales, United Kingdom (UK). He received his MSc in Reliability Engineering & Asset Management from the University of Manchester, UK. He worked as a Project Quality Engineer at Siemens Industrial Turbomachinery Ltd and as a Quality Manager at Kalahari Air Services. Currently, he is working as a Research Assistant while pursuing his Ph.D in the Department of Civil, Structural and Environmental Engineering, Trinity College Dublin.

Working with: Professor Aonghus Mc Nabola

PhD Student

Development of a Low Cost Sensor Technology for Predictive Maintenance of Hydropower Turbines

The water industry is the 4th most energy intensive sector in the EU, and is responsible for considerable contributions to CO2 emissions and climate change. This is coupled with the challenges that climate change is placing on water and energy resources. The water industry is also a critical part of the water-energy nexus, where water supply requires energy, and energy production requires water.

Pumping of water is the most energy intensive activity within water supply. In addition many opportunities exist for the installation of hydropower turbines in water pipelines to generate electricity and reduce net demand. Significant efforts have been made in recent years to improve the efficiency of these hydraulic machines (pumps and turbines) to reduce energy consumption or improve production. However hydraulic machines require regular maintenance, and maintenance problems are known to greatly reduce efficiency, with consequent environmental impacts.

The global market for hydraulic machinery corresponds to €10.7 billion for pumps and €2.4 billion for turbines, illustrating the scale of these activities. This project aims to develop and test an innovative suite of sensor technology for the predictive maintenance of hydropower turbines, using an Internet Of Things (IOT) approach.

The data collected from the sensor suite will be transmitted through low-power wide-area networks and analysed in real-time to detect the onset of machinery faults. This will enable early detection of faults to allow the required repairs to be complete in advance of catastrophic failures, reduction in power output (turbines), or increases in power consumption (pumps). This approach will avoid expensive emergency repairs, lower the downtime of installed machinery, and maintain high operating efficiency in pumps and turbines, minimising environmental impacts. 

The expected results of this work will include significant financial savings for water companies, which will reduce the cost of water for consumers. The results will also increase in the efficiency of installed hydraulic machinery, with subsequent reductions in CO2 emissions. This project will build on previous TCD projects ( and

Project Supervisor(s): Prof. Aonghus McNabola & Prof. Biswajit Basu