Method for inspecting unbalance error of washing machine and washing machine
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Solution Overview
Problem
Conventional washing machines struggle to accurately detect unbalance errors during the spin-drying process, particularly with bulky items like blankets, leading to inefficient operations and potential motor short-circuits.
Innovation Solution
A machine-learning-based washing machine that acquires data on driving current and RPM, uses a laundry movement identifying model to determine if laundry is moved, and adjusts the spin-drying profile or terminates operations if an unbalance error is predicted, reducing operating time and preventing motor short-circuits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the washing machine performs a laundry untangling operation before spin-drying, then the unbalance value is lowered, but the unbalance problem persists for bulky laundry like blankets that fill up the washing machine
Solution Approach 1:
The system performs preliminary actions (washing and rinsing processes) to prepare the laundry before spin-drying, ensuring that bulky items are properly saturated and settled. This preliminary preparation helps the laundry move more naturally during spin-drying, improving unbalance detection accuracy without requiring additional untangling operations during the spin cycle
Solution Approach 2:
The system continuously monitors motor current and RPM during spin-drying to detect laundry movement in real-time. When movement is detected, the system adjusts the spin-drying profile dynamically, creating a feedback loop that improves detection accuracy while maintaining operational efficiency by adapting to the actual laundry behavior
2Measurement precision
If the washing machine uses machine learning to identify laundry movement and predict unbalance errors, then detection accuracy is improved, but the operating time increases due to data processing
Solution Approach 1:
The system applies machine learning selectively rather than continuously - using it primarily during the spin-drying process when unbalance detection is most critical. The model processes only the essential features from motor current and RPM data, performing partial analysis that achieves sufficient detection accuracy without excessive processing time
Solution Approach 2:
The system changes operational parameters (spin-drying speed, acceleration profiles) based on machine learning predictions. When unbalance is predicted, the system adjusts RPM and acceleration rates, allowing the washing machine to operate more efficiently by adapting to predicted conditions rather than maintaining fixed conservative parameters throughout
3Productivity
If the washing machine continues spin-drying with unbalance error, then the operating time is maintained, but motor short-circuit may occur
Solution Approach 1:
The system takes preliminary anti-action by predicting unbalance errors before they cause motor damage. The machine learning model analyzes patterns in motor current and RPM to forecast potential unbalance conditions, allowing the system to adjust the spin-drying profile proactively and prevent harmful vibrations that could lead to motor short-circuits
Solution Approach 2:
The system dynamically adjusts spin-drying parameters based on real-time conditions and predictions. When unbalance is detected or predicted, the washing machine modifies RPM, acceleration, and deceleration profiles on-the-fly, creating a dynamic operation that maintains productivity while protecting the motor from damaging conditions
Data Source
AI summary
A method for detecting an unbalance error of a machine-learning-based washing machine and the washing machine are provided. The method identifies whether laundry is within an inner drum of the washing machine using a laundry movement identifying model provided in an AI device of a server or the washing machine, measures an unbalance value on the basis of data of unbalance when the laundry is not moving, and then detects an unbalance error. The unbalance error can be more precisely detected without using additional components. The AI device for detecting the unbalance error of the present disclosure can be associated with drones (unmanned aerial vehicles (UAVs)), robots, augmented reality (AR) devices, virtual reality (VR) devices, devices related to 5G service, and the like.