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How to Optimize Physical Assets Through ML-Powered Predictive Maintenance

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AnacondaCon 2018. Sourav Dey & Rajendra Koppula. With decreasing sensor, communication, storage and compute costs, it is now possible to collect vast amounts of data from physical assets like mechanical equipment, heavy earth moving equipment, and factory assembly lines. With recent advances, companies can now apply Machine Learning to all that data to optimize physical assets with the goal of achieving zero downtime. Using a real world case study, Sourav will demonstrate: 1) how to sample a time series dataset to generate training and validation datasets; 2) how to deal with non-uniformly sampled time series and other data quality issues; 3) how to use Dask to parallelize compute intensive feature creation to handle the volume and velocity of sensor data; 4) how to apply classic ML techniques like Random Forest trees; and 5) results and outcomes.

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