Research Interests: Condition Monitoring, Rotating Machinery, Fault diagnosis, Fault prognosis.
Dr. Akhand Rai received his PhD from Indian Institute of Technology Roorkee, Roorkee. He has a working experience of more than 4 years in various reputed academic and industrial institutions across the country as well as outside. His areas of interest include rotating machinery dynamics, data-driven condition monitoring of mechanical components, diagnosis-prognosis of rolling element bearings and gears, application of signal processing and artificial intelligence methods for reducing maintenance costs and preventing failures. He has published several research papers in SCI indexed journals and national-international conferences. Some of his contributions are in well-recognized journals such as Mechanical Systems and Signal processing, Measurement, Tribology International, Applied Soft Computing and IEEE Transactions on Instrumentation and Measurement. He is a reviewer of various international journals such as IEEE transactions on Industrial Electronics, IEEE Transactions on Circuits and Systems, Mechanism and Machine Theory, IEEE Access, and Measurement.
He began his career with General Electric, Aviation, Bangalore. There, he worked on high and low cycle fatigue analyses of jet engine externals and fuel-supply systems. Subsequently, he joined Jaypee University of Engineering and Technology, Guna and worked there as an Assistant Professor for 2.5 years. Afterwards, he finished his PhD from IIT Roorkee and worked with Thapar Institute of Engineering and Technology as a Visiting Assistant Professor for approximately 1 year. He was employed as a Postdoctoral Professional Researcher at University of Ulsan, South Korea, prior to joining Ahmedabad University. There, he worked on fault detection in various fluid filled mechanical components such as pipelines and boiler tubes
My research interests are primarily in the area of condition monitoring of rotating machinery. The rotating machinery finds extensive application in a variety of industries such as aerospace industry, electric power industry, textile industry, automotive industry, mining industry and energy industry, etc. A few examples of the rotating machinery used in these industries are electrical motors, electrical generators, turbines, compressors, rotary machine tools, and gearboxes, etc. It’s just a matter of the past few decades, that the industries have realized the importance of maintenance of these rotating machines that are actually installed in huge numbers on the production shop floor. A failure of a single rotating machinery leads to huge economic losses because of higher production downtimes and increased maintenance activities. In addition, the chances of human casualties are tremendously increased. My research work therefore focuses on providing efficacious condition-monitoring tools and solutions with an aim to eradicate the catastrophic failures in roto-mechanical equipments before it occurs and thus prevent the related damaging consequences.
For the past few years, I have been working on developing new methodologies for prognosis of rolling element bearings that constitute a key element of rotating machinery. During my PhD, I have developed several prognostics approaches to monitor the condition of bearings. On one hand, I used classification-based-machine learning algorithms like self-organizing maps and k-medoids to construct prognostics models for classifying the performance degradation states in bearings; and on the other hand, I build prognostics models based on regression-based-machine learning algorithms like neural networks and support-vector regression to forecast the remaining useful life of bearings. Contrary to the conventional diagnostics procedures, the developed prognostics methods were validated to be more fruitful in the sense that they reflected the bearing health in real-time manner and were capable of thwarting sudden failures before they occur.
Attempts are being made to attract research projects from organizations, industries and other stakeholders playing a key role in condition monitoring of rotating machines.