New Product

Renesas Electronics Corporation (TSE: 6723), a foremost provider of innovative semiconductor solutions, declared the launch of its Failure Detection e-AI Option for motor-equipped house appliances, including the 32-bit microcontroller, Renesas RX66T. This method with embedded AI (e-AI) enables abnormality determination of home appliances -- such as air conditioners, refrigerators, and washing machines -- because of motor failure.

Property data showing the current or spinning rate status of the motor may be utilized directly for abnormality detection, which makes it feasible to execute e-AI-based abnormality detection and motor control using a single MCU un. Using the RX66T removes the need for extra sensors, thereby decreasing a customer's bill of materials (BOM) cost.


air conditioner motors

When a house appliance malfunctions, the engine performance typically appears abnormal when operating and being monitored for error detection in real time. By implementing e-AI supported motor control-oriented detection, the fault detection results can be applied not just to activate alarms when a fault happens, but also for precautionary maintenance.

e-AI can determine the time when maintenance and repairs should be performed, and the fault locations can be identified by it. This capability offers manufacturers of home appliance the ability to improve maintenance operations efficiency and enhance product security by adding a feature that estimates faults before they happen in their products.

The Renesas Fault Detection e-AI Option for motor-equipped house appliances can regulate up to four motors since it uses the highly efficient RX66T MCU. Today's washing machines incorporate three engines: one to drive the drying fan, one to rotate the washing tub, and one to operate the water circulation pump. The Renesas Failure Detection e-AI Option can be used to regulate and monitor these motors including an RX66T chip at the same time.

The new option utilizes an RX66T CPU card and the Renesas Motor Control Evaluation System. This hardware is coupled with a GUI tool which enables assessing and collecting property data in addition to a set of sample program files which run on the RX66T MCU.

For faults detection, it's imperative to learn the features of the normal state. System engineers can begin developing optimized error detection and AI learning using the GUI tool. When the AI versions are generated, the e-AI development environment (composed of an e-AI Checker, e-AI Translator, and e-AI Importer) can be readily utilized to import the learned AI models to the RX66T.

 At Renesas Industrial Solutions Business Unit, Vice President of Home Business Division, Toru Moriya, said: "Leveraging its great expertise in producing solutions for use in home machines, Renesas has now formed a solution intelligent of identifying malfunctions that influence the system based on determining abnormal motor performance." Moreover, in situations where a fault occurs in the engine, it can be hard to localize the source to conclude whether there is an anomaly in the motor or the inverter circuit.  The new resolution makes it feasible to recognize the fault location promptly, which has the potential to limit the maintenance burden for customers dramatically."

Moving ahead, Renesas will continue working toward the realization of a smart society by supporting the development of smart home appliances through improved endpoint intelligence in the operating technology (OT) field.


The Failure Detection e-AI Option for Motor-Equipped Home Appliances is available today.

E-AI Failure Prediction Becomes Standard Operation


This demonstration indicates that one Renesas MCU recognizes motor control and error detection for an application concurrently. e-AI finds an application fault during the motor's abnormal state. The abnormal state of the engine is identified by the acceleration sensor, even by torque, current and rotation speed, which will be the data stored on the MCU (Motor Control Unit). E-AI predicts the maintenance program, makes motor controller intelligent, identifies the location of the collapse, and comprehends the endpoint in real time.