Time Series Anomaly Detection Techniques for Predictive Maintenance
Overview
Fault data is critical when designing predictive maintenance algorithms – but it is often difficult to obtain and organize. Many organizations are faced with a growing sea of time series sensor data, most of which represents normal operation. How can engineers analyze this data and design anomaly detection algorithms to identify potential problems in industrial equipment?
Using real-world examples, this webinar will introduce you to a variety of statistical and AI-based anomaly detection techniques for time series data.
Highlights
- Organizing, analyzing, and preprocessing time series sensor data
- Feature engineering using Diagnostic Feature Designer
- Distance-based approaches for exploring anomalies in historical data
- One-class machine learning and deep learning approaches for algorithm development
- Comparing and testing algorithm performance
- Deploying anomaly detection algorithms in a streaming environment
Who Should Attend
- MATLAB users that work with time series data and would like to detect anomalies in the data.
- Professionals interested in using data to forecast the health of engineering equipment, such as engines.
Product Focus
This event is part of a series of related topics. View the full list of events in this series.
