Anomaly Detection for Preventive Maintenance

Detecting anomalies in sensor events is a requirement for a wide variety of use cases in the industrial IoT. Examples include predicting failures of HVAC systems and elevators for property management to identifying potential signals of malfunction in aircraft engines to schedule preventive maintenance.  When the number of sensors runs into the tens of thousands or more, as is common in large IoT installations, a scalable model for preventive maintenance is needed.

Unlike prediction models for customer churn, inventory forecasts, etc. that rely on multiple sources of data and a wide range of domain-specific parameters, it is possible to detect anomalies for many types of time-series data using statistical techniques alone.
In this session, we will discuss a step by step process for anomaly detection with examples that aid in quick insights for building models for preventive maintenance.

Shanti Subramanyam is the Founder and CEO of Orzota, Inc. Shanti has decades of experience in building distributed applications and scalable systems having worked in companies such as Box, Yahoo! and Sun Microsystems before founding Orzota. She is a technical leader and consultant, architecting Big Data solutions for large enterprises.