I will be giving a talk titled “Anomaly Detection for Predictive Maintenance” at the Global Artificial Intelligence Conference in Seattle on April 27th 2018. If you are going to the conference, please do reach out.
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.