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This is Part 1 in our three-part webinar series, "Industrial IoT & Failure Prediction on Event Signals." This series will highlight how companies can seize the opportunity to monetize the trillion-dollar opportunity in IIoT by “connecting the unconnected” for cognitive insights. These cognitive insights from real time sensor/machine data can empower companies to achieve ground-breaking results in plant efficiency, improved productivity and digital readiness for smart factories.
Part 1 is “Feature Engineering for Failure Prediction on Time Series Data” with Adurthi Ashwin Swarup, Sr. Data Scientist, Progress DataRPM. He'll introduce the concept of how imprecise business problems are translated to precise data science solutions, how data is transformed and whitened for algorithms essential to predictive maintenance use cases.
By attending this webinar, you will:
- Understand the importance of feature engineering for predictive maintenance
- Learn how to think about and apply feature engineering to sensor data to make the leap from storage efficiency to analytical efficiency
- Learn how big data can be leveraged using cognitive data science to simultaneously compare large set of sensor features for model computation