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tinyML Asia 2021 Airborne sound maintenance in remote sites using low power federated learning Anton Kroger, Senior Director Natural Resources, SAP In this presentation, we’ll detail the business and technical reasons in selecting TinyML for Contextualize Airborne Sound for Predictive Maintenance. The objective of this solution is to: - Minimize planned & unplanned operational downtime by maximizing asset efficiency and availability. - Minimizing working capital for expensive spare parts holding following planned & unplanned operational downtime. - Minimize retrofitting expenses for upgrading existing machine infrastructure to be monitored and included in existing predictive maintenance models. Due to the remote nature of these operations, using low power sensors and a Federated Learning approach, we can provide a solution that continuously learns and only shares scores associated to the sound data to adhere to GDPR regulation.
