Abstract: Predictive maintenance is the most recent technique in maintenance engineering. Machine operational parameters are used to assess the health of equipment and decide on the maintenance schedule. In Aviation, aircraft engine manufacturers continuously monitor their engine parameters in flight to evaluate performance and deviations from normal.
The application of AI in this field enables measurement of behavior that is not observable using traditional means. AI-based monitoring provides the edge required to operate in Industry 4.0 where connected machines do away with buffers in between processes and any unscheduled downtime of one machine effects the entire production chain.
This demonstration will walk you through the development of AI models using IoT data for one of the largest metal manufacturing company in India. It will help you master different types of AI models to answer questions like:
- When do I plan the maintenance of the given equipment?
- Will a component last till the next maintenance cycle or do I replace it during the current maintenance?
- How to identify faulty equipment in the long production line?
Bio: Dr. Sri Vallabha Deevi is a Data Scientist at Tiger Analytics and lead teams in building analytics solutions - from simple statistical models to AI/Deep Learning models. His expertise is in scientific computing, machine learning & reduced-order modeling of physical systems. He is interested in teaching and talk on data science and machine learning regularly at various colleges & conferences. He finished B.Tech from IIT Madras and Ph.D. from IISc Bangalore.
Sri Vallabha Deevi, PhD
Lead Data Scientist | Tiger Analytics