Krish Kumar Raj is a dedicated Electrical and Electronics Engineer with a robust academic background and diverse hands-on experience. He holds an Honours Bachelor’s Degree in Electrical and Electronics Engineering from the University of the South Pacific (USP) and is currently pursuing a Post Graduate Certificate in Tertiary Teaching and Master’s Degree in Engineering Science at the same institution. His research focuses on using deep learning strategies for bearing fault diagnosis and estimation of remaining useful lifetime, a crucial area in predictive maintenance for rotating machinery.

Krish’s professional journey includes significant tutoring and lab demonstrator roles at USP, where he has supported students across various levels in subjects such as Digital Logic Design and Digital Signal Processing. His internships have provided him with practical experience in domestic wiring, industrial maintenance, and the development of low-cost ventilators for COVID-19 response, showcasing his ability to apply theoretical knowledge to real-world challenges.

With a strong foundation in power systems, digital control systems, and neural networks, Krish has also demonstrated his proficiency in programming languages like Python and C++, as well as platforms like MATLAB and Simulink. He has contributed to several academic conferences, presenting papers on fault detection in induction motors and photovoltaic inverters, underscoring his commitment to advancing engineering knowledge.

Krish’s technical skills are complemented by his leadership abilities and enthusiasm for problem-solving. His diverse interests, including swimming, futsal, soccer, and hiking, reflect his well-rounded personality. As a member of professional organizations like IEEE and Engineers Australia, he is poised to contribute significantly to the field of electrical and electronics engineering.

For more details on Krish’s academic and professional accomplishments, please visit his LinkedIn profile.

Community Service: Duke of Edinburgh International Club

Awards and Prizes: Silver and Bronze medals in Duke of Edinburgh International Award

Members of an organizatio: Institute of Electrical and Electronics Engineers

Interest: Areas of Neural Networks, Artificial Intelligences, Condition Monitoring Study, Digital Control Systems, Power Systems

Qualification: Bachelor’s degree in Electrical and Electronics Engineering (with honors) program

Undergraduate Courses:
Bachelor of Electrical and Electronics Engineering (Courses from each of the four years of the degree)

Journals:
Enhanced Fault Detection in Bearings Using Machine Learning and Raw Accelerometer Data: A Case Study Using the Case Western Reserve University Dataset

Conferences:

1. A state-space model for induction machine stator inter-turn fault and its evaluation at low severities by PCA
2. ECG Multi Class Classification Using Machine Learning Techniques
3. Open Circuit (OC) and Short Circuit (SC) IGBT switch fault detection in three-phase standalone photovoltaic inverters using shallow neural networks
4. A LSTM-based Neural Strategy for Diagnosis of Stator Inter-turn Faults with Low Severity Level for Induction Motors
5. Bearing Fault Classification using Temporal Features for Wind Turbine Application: Harnessing Neural and Non-Neural Techniques
6. Comparative Analysis of Machine Learning Techniques for Bearing Fault Classification in Rotating Machinery

Projects:
Prototype Low Cost Ventilator System funded by Agency universitaire de la Francophonie (AUF)

Workshop/Training:
UDEMY – Machine Learning A-Z: Hands-On Python in Data Science,  Alison – Supervision and Management skills, •   UDEMY – Machine Learning A-Z: Hands-On Python in Data Science