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.

1. Performing Predictive Analytics
With Imperfect Data
dwilson@cdoadvisors.com
832-819-5744
CDO Advisors LLC© - 2017

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. Sensor Data
Examples
• IOT
• Smart Meter
• SCADA
• Machine
Events
Sample Data
25 Sensors on 136 machines

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. Model Outcomes
Sensor Importance Predicted Failures

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