- Workflow to develop predictive maintenance and condition monitoring algorithms
- Extracting condition indicators and building Remaining-Useful-Life (RUL) models
- Automating preparation and labeling of image/video data
- Interoperability with opensource deep learning frameworks
- Training deep neural networks, hyper-parameters to accelerate training time & increase accuracy
About the Presenters
Jayanth Balaji Avanashilingam
Jayanth Balaji Avanashilingam works as an Application Engineer at MathWorks in the area of Language of Technical Computing. He primarily focuses on areas of Data Analytics for the application involving with Time-Series data. Jayanth has around 6 years of research and industrial experience, where he was working developing AI/ML/DL solutions for various application areas, such as retail optimization, computer vision and Natural Language Processing. Prior to joining MathWorks Jayanth was working as Senior AI Engineer at Impact Analytics, Bangalore.
Jayanth holds Bachelor’s degree in Electronics & Communication Engineering, Master’s degree in Very Large Scale Integration Design and currently he is pursuing his doctoral research with the thesis titled “Investigations into Faster Training of Deep Learning Algorithms for Modelling Time-Series”.
Peeyush Pankaj is a senior application engineer at MathWorks, where he has been promoting MATLAB products for data science. He works closely with customers in the areas of predictive maintenance, digital twin, enterprise integration and big data. Peeyush has over 9 years of industry experience with a strong background in Aviation. Prior to joining MathWorks, he has extensively worked on aircraft engine designs, testing and certification. He has filed 25 patents on Advanced Jet Engine technologies and Prognostic Health Monitoring of aircraft engines. Peeyush holds a master’s degree in advanced mechanical engineering from the University of Sussex, UK.