tinyAI Forum on PdM & Anomaly Detection: TinyML Based Failure Prediction in Appliances With No…

TinyML Based Failure Prediction in Appliances With No Added Component Cost
Ali O. ORS, Global Director, AI ML Strategy and Technologies, NXP Semiconductors

Many motor driven household and commercial appliances play a vital role in our daily lives. Unexpected motor failures in these appliances can result in inconvenience and costly repairs. This presentation explores the application of tiny Machine Learning (tinyML) techniques in predicting and preventing appliance motor failures, without the need for additional microprocessors or sensors in the system and without having to redesign all the motor control electronics already deployed. We will showcase the research from NXP where motor control data is leveraged to determine the health of the motor and detect potential anomalies.

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