Embedded AI solution detects motor failure on-board home appliances
Property data showing the motor’s current or rotation rate status can be used directly for abnormality detection, making it possible to implement both motor control and e-AI–based abnormality detection with a single MCU. Hence the company argues that using its RX66T could eliminate the need for additional sensors, reducing BOM cost.
When a home appliance malfunctions, motor operation typically appears abnormal when running and being monitored for fault detection in real-time. By implementing e-AI-based motor control-based detection, the failure detection results can be applied not only to trigger alarms when a fault occurs, but also for preventive maintenance. For example, e-AI can estimate when repairs and maintenance should be performed, and it can identify the fault locations. This capability provides home appliance manufacturers the means to boost maintenance operations efficiency and improve product safety by adding functionality that predicts faults before they occur in their products.
The Renesas Failure Detection e-AI Solution can control up to four motors (based on the RX66T MCU), in excess of today’s typically three motor count in washing machines. The new solution uses the Renesas Motor Control Evaluation System and an RX66T CPU card combined with a set of sample program files that run on the RX66T MCU as well as a GUI tool that enables the collection and analysis of property data indicating motor states. Using the GUI tool, system engineers can immediately begin developing AI learning and optimized fault detection functionality. Once the AI models are developed, the e-AI development environment (composed of an e-AI Translator, e-AI Checker, and e-AI Importer) can be easily used to import the learned AI models into the RX66T.
Renesas Electronics – www.renesas.com
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