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Online Orientation Session on
Anomaly Detection Using Machine Learning in Industrial IoT
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About the Webinar
Duration
60 mins
Day
March 14, 2018
Time
3:00 pm
Who is the Speaker?
Abirami R works as a Data Scientist in Flutura Decision Sciences and Analytics for over 2.5 years. Being fond of Mathematics since childhood made her pursue pure Science. She holds a Master’s Degree in Statistics from Bangalore University and a triple major Bachelor’s Degree in Computer Science, Mathematics and Statistics. Abirami’s interest and passion for finding meaningful patterns and insights from data grew strong during her Bachelor degree days and made her choose Statistics to follow her interest. She is glad as she could secure a gold medal in her Master’s Degree at the University level.
Abirami R.
Key Takeaways
- Basic understanding of Data Science in Industrial IoT
- Introduction to Anomaly and Anomaly Detection in Hydro Electric Generator
- Basic Exploratory Data Analysis in Anomaly Detection
- Usage of Machine Learning Algorithms in Anomaly Detection
Session Agenda
Early detection of anomalies is of immense importance to safeguard the health of the asset and in downtime prevention. Anomalies or irregularities in the asset indicates that the process is inherently faulty. Industrial IoT Analytics leverages the sensor data of the various assets used in the industries to identify these faults and to ensure smooth operation, prevent downtime and monitor asset health.
In this session, you will be able to understand the role of Data Science in the field of Industrial IoT. The main area of focus would be Machine Learning Algorithms in Anomaly Detection with a specific use case in Hydro Electric Power Generator.
Who Should Attend?
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