Washing machines are an integral part of modern life and the cornerstone of automated laundry care. While they have made significant strides in energy efficiency over the past two decades, challenges remain. Users often struggle with confusing interfaces that lead to inefficient cycle selection, and motor reliability continues to be a concern due to wear, unbalanced loads, and inefficient control. Further advancements are needed to improve reliability, sustainability, and energy performance—ideally through software-driven innovations that do not require additional sensors. Renesas has introduced a software-based smart washing machine concept to enhance performance in two critical areas:
- User Interaction: Upgrade the Human-Machine Interface (HMI) for a more intuitive and streamlined user experience.
- Motor Reliability: A software-based condition monitoring layer increases the durability and efficiency by detecting wear, imbalance, and control inefficiencies in real time—extending product lifespan and lowering maintenance costs.
Both enhancements are powered by embedded AI models, implemented as software components on Renesas hardware. This approach enables smarter, more efficient appliances without increasing hardware complexity.
Motor Condition Monitoring
As part of Renesas' software-driven approach to enhancing washing machine reliability, motor condition monitoring plays a critical role. By integrating AI models directly into the appliance, the system enables real-time detection of key motor anomalies— without requiring additional sensors. This includes identifying startup irregularities, detecting varying load conditions, recognizing unbalanced loads, and predicting potential bearing failures. These insights ultimately enhance machine quality and elevate the user's experience by optimizing overall performance.
Startup Anomaly Detection
Startup anomalies can occur due to electrical imbalances or heavy loads, disrupting motor behavior before the wash cycle even begins. Renesas' AI algorithm analyzes system parameters from BLDC motor control to detect these irregularities during the machine's startup phase. This early detection helps prevent damage to the motor, drum, or electronics. Furthermore, the system also provides clearer error messaging and proactive support notifications—leading to faster fixes, fewer technician visits, and less downtime for users.
Bearing Failure
Bearings are essential components in all rotating systems, but they gradually degrade. It often starts with subtle high-frequency anomalies. Bearing issues typically go unnoticed until they cause audible noise or mechanical disruption, by which point significant damage has already occurred.
Renesas' sensorless AI model monitors motor system parameters to detect bearing wear and tear early. By identifying changes in mechanical load and friction, the model enables predictive maintenance—all through a software update, with no added hardware. This approach boosts machine reliability and simplifies integration for manufacturers.
Cycle Performance
Load Detection
Accurately detecting laundry weight is essential for optimizing water, detergent, and cycle duration. Renesas' AI model enables sensorless load detection by analyzing motor system parameters and predicting the weight of the laundry in the drum. This leads to:
- More efficient and eco-friendly wash cycles
- Reduced water and detergent usage
- Improved laundry quality
- Shorter cycle times
Unbalanced Load
When laundry items of varying weights are unevenly distributed, they create an unbalanced load that affects the mechanical and operational stability. This can result in:
- Increased Vibration: The uneven weight distribution causes the drum to rotate off-center, generating excessive vibration. The vibration transfers to the motor and other mechanical components, increasing wear and tear that can damage the drum bearing. If this vibration reaches the system's resonance frequency, it can cause structural damage to the machine.
- Higher Energy Consumption: The motor compensates for imbalance, leading to increased power usage and reduced efficiency.
To address this issue, we have developed a sensorless AI model that detects unbalanced load and shaft misalignment. This model operates similarly to our existing load detection and startup anomaly detection systems, requiring no additional hardware components.
- Software-Only Implementation: The model leverages existing system data and does not require extra sensors or BoM cost.
- Enhanced Reliability: Early detection of unbalanced loads allows the system to take corrective actions (e.g., redistributing the load or adjusting spin cycles), improving machine longevity and user satisfaction.
Vision-Based Cycle Optimization
Taking AI a step further, Renesas enables vision-based cycle optimization by placing a vision sensor, such as a camera, inside the drum. This allows us to:
- Identify different types of textiles (e.g., cotton, wool, synthetic, etc.)
- Optimize the washing cycle with laundry color detection
- Adjust the amount of consumed water, temperature, detergent, and drum speed accordingly
This solution concept is powered by an object detection model embedded within the washing machine, optimized to identify laundry. This could further optimize industrial use cases where textiles are pre-filtered before being placed in the drum.
Human Machine Interface Enhancement
Redefining the human-machine interface (HMI) in washing machines is a complex task that requires embedding intelligent, user-centric features to elevate both convenience and safety. Renesas enables this transformation through embedded AI models that support advanced natural language understanding (NLU), making operation more intuitive and accessible. Person ID detection allows the system to recognize individual users and apply personalized settings, while speaker identification combined with voice anti-spoofing ensures that only authorized users can issue voice commands, especially in shared or industrial environments. In the following sections, we will explore how these features are feasible in a cloud-free environment:
Speaker Identification and Natural Language Understanding
Setting up washing machines can sometimes be challenging—especially when it's unclear which program best suits your laundry, which parameters need adjusting, or when the interface is in a different language while traveling. One innovative solution is enabling voice commands, powered by a cascade of embedded AI models.
First, a voice anti-spoofing model identifies the authorized operator. Then, a voice command recognition model allows the operator to simply state the laundry type and desired washing parameters.
The system extracts and validates this information with the operator, then automatically selects the optimal settings. Recent advances in natural language understanding have made this possible to run effectively at the edge—with minimal RAM and ROM requirements—making it easy to integrate the software directly into existing appliance hardware.
Improve User Interaction Using Face ID Detection
Face recognition technology offers a powerful way to personalize and secure the user experience in modern washing machines. By identifying users through embedded face detection models, the system can automatically load customized profiles—such as preferred washing programs and cycle settings—without manual input.
Beyond personalization, this approach enhances safety and access control:
- Child Safety: Prevents unauthorized changes to machine settings.
- Industrial Use: Limits access to authorized personnel for loading and unloading laundry.
To ensure privacy and data protection, the AI model is fully embedded within the washing machine, with no cloud connectivity. All user data is stored and processed locally, maintaining a secure and seamless experience.
This AI-based smart washing machine solution concept highlights the transformative potential of edge AI in the home appliances market, making these machines more reliable, efficient, and eco-friendly.
Renesas AI/ML empowers next-generation AI technologies with scalable, edge-optimized AI/ML solutions that accelerate intelligent decision-making across industries.
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