Predictive Maintenance

Business Goals:

Anticipate equipment failure for commercial printers

Increase predictive maintenance

Reduce reactive service calls by 30%

Data Sets and Models:

Log data from equipment

Sensor readings

Error, warning, and status codes

Failure flag

Machine ID#'s and diagram of manufacturing process

Both random forests & neural network models trained on data

Output and Benefits:

Variables & patterns most related to future failure:

Provides engineering insight into failure points

Probability of failure within various time periods

Model statistics like precision, recall, false positive rates, etc

Anticipation & reduction of service calls:

Calls reduced by 30+%

Costs reduced by 20+%

Many on-demand calls transformed into anticipatory ones

Case Study:

Business Goals:

Anticipate equipment failure.

Increase predictive maintenance.

Reduce reactive service calls by 30%.

Outputs & Benefits:

Variables & patterns most related to future failure;

Provides engineering insight into failure points.

Probability of failure within various time periods.

Model statistics like precision, recall, false positive rates, etc.

Anticipation & reduction of service calls:

Calls reduced by 30+%.

Costs reduced by 20+%.

Many on-demand calls transformed into anticipatory ones.

Data Sets & Models:

Log data from equipment.

Sensor readings.

Error, warning, and status codes.

Failure flag.

Machine ID#'s and diagram of manufacturing process.

Both random forests & neural network models trained on data.