Challenge: Interpreting Anomalies

Explore how to interpret anomalies detected by the Isolation Forest model in H2O applied to industrial compressor sensor data. Learn to analyze multi-sensor readings and understand unusual operational patterns for effective anomaly detection.

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Problem statement
- Dataset details

Problem statement

This challenge revolves around a dataset derived from an industrial machine operating in a real-world environment. The dataset encompasses readings sourced from a compressor’s air production unit (APU), including measurements of pressure, temperature, motor current, and air intake valve status. These readings were meticulously collected at 10-second intervals.

To gain a deeper understanding of the unusual patterns observed during the machine’s operation, an isolation forest anomaly detection model has been employed using the H2OIsolationForestEstimator algorithm. The primary objective of ...

1.Introduction to Machine Learning

2.Supervised Learning: Regression Models with H2O

3.Supervised Learning: Classification Models with H2O

4.Unsupervised Learning: Clustering with H2O

5.Unsupervised Learning: Anomaly Detection with H2O

6.Closing Notes

7.Appendix

Challenge: Interpreting Anomalies

Problem statement