Dr. Karthick Thiyagarajan received the B.E. degree in Electronics and Instrumentation Engineering from the Anna University, India, in 2011, the M.Sc. degree in Mechatronics from the University of Newcastle Upon Tyne, United Kingdom, in 2013 and the Ph.D. degree specializing in Sensor Technologies from the University of Technology Sydney, Australia, in 2018.
He is currently a Research Fellow within the Centre for Autonomous Systems of the University of Technology Sydney working on an Australian Government and industry funded AU$24M project, “Smart Linings for Pipe and Infrastructure”, in collaboration with water utilities, manufacturers, researchers and applicators from Australia, USA and UK. Also, he is involved in the Sydney Water Corporation funded AU$809K project, "Intelligent Sensing and Robotics for Sewer Condition Assessment”.
His Ph.D. research was part of an industry-led collaborative project, “Data Analytics on Sewers”, which was funded by four government-owned Australian water utilities. He is the recipient of the following awards:
- Student Water Prize 2018 from the New South Wales – Australian Water Association.
- People’s Choice Award (Poster Presentation) in the UTS FEIT Research Showcase 2017.
- People’s Choice Award (Oral Presentation) in the UTS FEIT Research Showcase 2016.
- Member, Institute of Electrical and Electronic Engineers (IEEE).
- Member, Australian Robotics and Automation Association (ARAA).
- IEEE Transactions on Instrumentation and Measurement.
- IEEE Access
Dr. Karthick has a good experience in working on Blue Sky/Industry/Government funded research projects with national and international partners. His present research focuses to solve fundamental/applied research problems in the following topics:
- Sensor technologies for infrastructure condition assessment
- Sensor-driven predictive analytics for system/environmental perception.
- Robotic localization based on non-conventional sensor features inside pipes.
Applications of his research topics are mainly in Smart Linings, Smart Pipes, Infrastructure Robotics and Smart Buildings.
Thiyagarajan, K, Kodagoda, S, Nguyen, LV & Ranasinghe, R 2018, 'Sensor Failure Detection and Faulty Data Accommodation Approach for Instrumented Wastewater Infrastructures', IEEE Access, vol. 6, no. 1, pp. 56562-56562.View/Download from: Publisher's site
Thiyagarajan, K, Kodagoda, S, Nguyen, LV & Wickramanayake, S 2018, 'Gaussian Markov Random Fields for Localizing Reinforcing Bars in Concrete Infrastructure', International Symposium on Automation and Robotics in Construction, Germany, pp. 1052-1052.View/Download from: UTS OPUS or Publisher's site
Thiyagarajan, K, Kodagoda, S & Nguyen, LV 2017, 'Predictive Analytics for Detecting Sensor Failure Using Autoregressive Integrated Moving Average Model', Proceedings of the 12th IEEE Conference on Industrial Electronics and Applications, IEEE Conference on Industrial Electronics and Applications, IEEE, Siem Reap, Cambodia, pp. 1926-1931.View/Download from: UTS OPUS
Sensors play a vital role in monitoring the important parameters of critical infrastructure. Failure of such sensors causes destabilization to the entire system. In this regard, this paper proposes a predictive analytics solution for detecting the failure of a sensor that measures surface temperature from an urban sewer. The proposed approach incorporates a forecasting technique based on the past time series of sparse data using an autoregressive integrated moving average (ARIMA) model. Based on the 95% forecast interval and continuity of faulty data, a criterion was set to detect anomalies and to issue a warning for sensor failure. The forecasted and faulty data were assumed Gaussian distributed. By using the probability density of the distribution, the mean and variance were computed for faulty data to examine the abnormality in the variance value of each day to detect the sensor failure. The experimental results on
the sewer temperature data are appealing.
Thiyagarajan, K, Kodagoda, S & Ulapane, N 2016, 'Data-driven Machine Learning Approach for Predicting Volumetric Moisture Content of Concrete Using Resistance Sensor Measurements', Proceedings of the 11th IEEE Conference on Industrial Electronics and Applications (ICIEA 2016), IEEE Conference on Industrial Electronics and Applications, IEEE, Hefei, China, pp. 1288-1293.View/Download from: UTS OPUS or Publisher's site
In sewerage industry, hydrogen sulphide induced corrosion of reinforced concretes is a global problem. To achieve a comprehensive knowledge of the propagation of concrete corrosion, it is vital to monitor the critical factors such as moisture. In this context, this paper investigates the use of resistance measuring and processing for estimating the concrete moisture content. The behavior of concrete moisture with resistance and surface temperature are studied and the effects of pH concentration on concrete are analyzed. Gaussian Process regression modeling is carried out to predict volumetric moisture content of concrete, where the results from experimental data are used to train the prediction model.
Thiyagarajan, K, Kodagoda, S & Alvarez, JK 2016, 'An Instrumentation System for Smart Monitoring of Surface Temperature', 2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016, International Conference on Control, Automation, Robotics and Vision, IEEE, Phuket, Thailand.View/Download from: UTS OPUS or Publisher's site
- Industries: Sydney Water, Melbourne Water, South Australian Water, Water Corporation and Data61-CSIRO.
- Industry Bodies: Water Services Association of Australia (WSAA)
- Universities: University of Sydney, Monash University and Newcastle University.