PRIMAL is a predictive maintenance application that monitors electrical assets in buildings to predict failures and inform technicians when maintenance is required to reduce costs and safety issues. It permanently monitors assets, performs self-diagnosis, and uses predictive maintenance, training on raw and filtered historical data to build models and validate predictions on new data to determine when assets need repair or replacement.

1. PRIMAL: PRedictive MAintenance of
eLectrical assets
Predix IIOT Challenge 2017
Team Members:
Rogério Dionísio
Pedro Torres
Eva Masero
Ricardo Mendes

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The challenge
• Electrical system failures are commonly the cause of personal injury
and/or property damage that can be caused by fire or other
degradation of related systems, vehicles or structures. Predicting
electrical assets failures before they actually happens reduces
maintenance cost and avoid unexpected problems.

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The goals
• PRIMAL is an application that informs electrical technicians when and
which electrical assets of one or more buildings requires maintenance
or must be replaced.
• Properties of the PRIMALapplication:
• Permanent monitoring
• Self diagnosis
• Predictive maintenance
• Flexible process
• Human machine cooperation

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The proposed solution
Raw Data Filtered Data
Training set
Validation set
Model
New Data
Final Evaluation
Validate
Collect
Record and store
Analyse
Act

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