AI Platform Simplifies Motorized Appliance Maintenance

(Renesas Electronics)

Renesas Electronics’ Failure Detection e-AI Solution for motor-equipped home appliances employs the company’s RX66T 32-bit microcontroller (MCU). This solution with embedded AI (e-AI) enables failure detection of home appliances, such as refrigerators, air conditioners, and washing machines, due to motor abnormality. 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. Using the RX66T eliminates the need for additional sensors, thereby reducing bill of materials (BOM) cost.

 

The Renesas solution can control up to four motors. Today’s washing machines typically incorporate three motors: one to rotate the washing tub, one to drive the water circulation pump, and one to drive the drying fan. The solution can therefore be used to control these three motors with a single RX66T chip while at the same time monitoring all three motors for faults.

Sponsored by Infosys

In Conversation with Antonio Neri, President & CEO – Hewlett Packard Enterprise & Salil Parekh, CEO – Infosys

Hear the CEOs of Infosys & HPE discuss the current crisis and how it has accelerated the need for digital transformation for their clients. Connectivity at the Edge, right mix of hybrid cloud, ability to extract data faster than ever before… these are just some of the contributions that HPE and Infosys make to our clients’ digital transformation journey.

 

Other integrated features include the Renesas Motor Control Evaluation System and an RX66T CPU card. This hardware is combined with a set of sample program files that run on the RX66T MCU as well as a GUI tool that enables collecting and analyzing property data indicating motor states.

 

To detect faults, it is necessary to learn the characteristics of the normal state. 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.

 

The Failure Detection e-AI Solution for Motor-Equipped Home Appliances is available now. For more information, watch some video demos.

Suggested Articles

Revenues overall hit $3.82 billion, up 1% from third quarter of 2019, as auto plants reopened and personal electronics revenues grew

MIT Sloan and Boston Consulting Group call for expanding organizational learning to gain better financial rewards of AI deployments

Originally a 1960s memory manufacturer, Intel wants out of NAND following the market decline in 2018