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Predictive analytics with imperfect data using sensor data

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Build predictive models using sensor data to determine when a machine is likely to fail. Using data mining techniques you can get ahead of machine failures to schedule preventative maintenance during off peak hours.

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Predictive analytics with imperfect data using sensor data

  1. 1. Performing Predictive Analytics With Imperfect Data dwilson@cdoadvisors.com 832-819-5744 CDO Advisors LLC© - 2017
  2. 2. Overview • Sensor Data Example • Value can be gained from imperfect data • How a model works • Identify key data points • Clean up over time
  3. 3. Sensor Data Examples • IOT • Smart Meter • SCADA • Machine Events Sample Data 25 Sensors on 136 machines
  4. 4. Predictive Analytics • Can we predict when a machine is going to fail? • Use historical data on failures • Determine most importance factors • Build a classification model to predict failure • Apply active machine sensor data to the model • Review predictions
  5. 5. Model Outcomes Sensor Importance Predicted Failures
  6. 6. Sensor Modeling • By using sensor readings and known events such as failures you can build predictive models to let you know when a possible failure will occur. • By knowing this information you can schedule preventative maintenance in off hours to minimize downtown

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