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 in the iPipes Lab, Centre for Autonomous Systems at the University of Technology Sydney. He has 3+ years of experience in teaching Mechatronics subject and experience in working on Blue Sky/Industry/Government funded research projects with partners from Australia, USA, UK and Canada.
At the iPipes Lab, he is a Sensing and Smart Materials Research Lead. He supervises/co-supervise research degree students, capstone students, research engineers and interns. To date, he has secured $149k research funding as Chief Investigator since completion of his Ph.D. in August 2018.
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
- Co-organising an Invited Session on "Infrastructure Robotics: Challenges and Developments" in the IEEE CIS-RAM 2019.
Dr. Karthick Thiyagarajan 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 areas of his interest:
- Sensing Technologies
- Sensor Analytics
- Machine Learning for Sensor Systems
- Robotics for Infrastructure Inspection and Condition Assessment
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: UTS OPUS or Publisher's site
In wastewater industry, real-time sensing of surface temperature variations on concrete sewer pipes is paramount in assessing the rate of microbial-induced corrosion. However, the sensing systems are prone to failures due to the aggressively corrosive environmental conditions inside sewer assets. Therefore, reliable sensing in such infrastructures is vital for water utilities to enact efficient wastewater management. In this context, this paper presents a sensor failure detection and faulty data accommodation (SFDFDA) approach that aids to digitally monitor the health conditions of the sewer monitoring sensors. The SFDFDA approach embraces seasonal autoregressive integrated moving average model with a statistical hypothesis testing technique for enabling temporal forecasting of sensor variable. Then, it identifies and isolates anomalies in a continuous stream of sensor data whilst detecting early sensor failure. Finally, the SFDFDA approach provides reliable estimates of sensor data in the event of sensor failure or during the scheduled maintenance period of sewer monitoring systems. The SFDFDA approach was evaluated by using the surface temperature data sourced from the instrumented wastewater infrastructure and the results have demonstrated the effectiveness of the SFDFDA approach and its applicability to surface temperature monitoring sensor suites.
Giovanangeli, N, Piyathilaka, L, Kodagoda, S, Thiyagarajan, K, Barclay, S & Vitanage, D 2019, 'Design and Development of Drill-Resistance Sensor Technology for Accurately Measuring Microbiologically Corroded Concrete Depths', 36 International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction, Canada, pp. 735-735.View/Download from: UTS OPUS
Wickramanayake, S, Thiyagarajan, K, Kodagoda, S & Piyathilaka, L 2019, 'Frequency Sweep Based Sensing Technology for Non-destructive Electrical Resistivity Measurement of Concrete', 36 International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction, Canada, pp. 1290-1290.View/Download from: UTS OPUS
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
Sensor technologies play a significant role in monitoring the health conditions of urban sewer assets. Currently, the concrete sewer systems are undergoing corrosion due to bacterial activities on the concrete surfaces. Therefore, water utilities use predictive models to estimate the corrosion by using observations such as relative humidity or surface moisture conditions. Surface moisture conditions can be estimated by electrical resistivity based moisture sensing. However, the measurements of such sensors are influenced by the proximal presence of reinforcing bars. To mitigate such effects, the moisture sensor needs to be optimally oriented on the concrete surface. This paper focuses on developing a machine learning model for localizing the reinforcing bars inside the concrete through non-invasive measurements. This work utilizes a resistivity meter that works based on the Wenner technique to obtain electrical measurements on the concrete sample by taking measurements at different angles. Then, the measured data is fed to a Gaussian Markov Random Fields based spatial prediction model. The spatial prediction outcome of the proposed model demonstrated the feasibility of localizing the reinforcing bars with reasonable accuracy for the measurements taken at different angles. This information is vital for decision-making while deploying the moisture sensors in sewer systems.
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.