Method, device, and system for detecting dynamic imbalance of washing machine
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Solution Overview
Problem
Existing washing machine imbalance detection methods focus on static conditions and fail to address dynamic imbalance, leading to noise and vibration issues during operation.
Innovation Solution
A method and system using a vibration sensor attached to the washing machine cabinet to detect dynamic imbalance, processing vibration data with a controller and server to determine the dynamically balanced position, and employing machine learning algorithms to estimate gap sizes between the floor and the washing machine legs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If a washing machine is leveled under static conditions using tilt sensors, then the washing machine is balanced when stationary, but the washing machine experiences dynamic imbalance and vibration during operation
Solution Approach 1:
The patent transitions from static tilt sensing to dynamic vibration sensing. The vibration sensor detects cabinet vibrations during operation, and the system dynamically adjusts leg heights in real-time based on detected imbalance, resolving the contradiction between static leveling and dynamic stability.
Solution Approach 2:
The patent replaces the mechanical tilt sensing system with a vibration-based detection system. Instead of measuring static inclination angles, the system uses vibration sensors to detect dynamic cabinet movements and derives imbalance information from vibration patterns.
2Reliability
If vibration sensors are used to detect dynamic imbalance, then real-time balance detection during operation is achieved, but the complexity of the detection system increases
Solution Approach 1:
The vibration sensor serves multiple functions: detecting cabinet vibrations, determining dynamic imbalance, and triggering correction mechanisms. This multi-functionality reduces the need for separate detection systems while maintaining high reliability.
Solution Approach 2:
The system uses the washing machine's own vibration characteristics to detect imbalance without requiring external reference systems. The vibration sensor mounted on the cabinet detects the machine's self-generated vibrations during operation, enabling autonomous detection.
3Ease of operation
If the washing machine operates on three legs with one leg floating, then the washing machine can start operation, but noise is generated by repeated leg-to-floor contact
Solution Approach 1:
The vibration sensor provides real-time feedback on cabinet vibrations caused by leg-to-floor contact. The controller monitors this feedback and activates the height adjusting mechanism to lift the floating leg, eliminating the repeated contact and noise generation.
Solution Approach 2:
The system detects the developing imbalance condition through vibration patterns and proactively adjusts leg heights before significant noise and vibration occur. This preliminary correction prevents the harmful effects of severe dynamic imbalance.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Effectively detects and corrects dynamic imbalance, reducing noise and vibration, and improving the stability of the washing machine during operation.
Implementation Method 1
a vibration sensor attached to a washing machine cabinet to detect dynamic imbalance
Data Source
AI summary
A dynamic imbalance detection system capable of whether a washing machine is in a dynamically imbalanced position by using big data and executing an AI algorithm or a machine learning algorithm in a 5G communications network environment built for IoT. The system includes a washing machine and a server communicating with the washing machine. The washing machine includes a vibration sensor attached to a washing machine cabinet and a controller receiving a vibration signal detected by the vibration sensor during the operation of the washing machine and processing the vibration signal into vibration data. The server receives the vibration data from the controller and trains a machine learning algorithm on a training dataset that is obtained by processing one or more features among a displacement magnitude, a displacement ratio, and a displacement phase, and determines result values of dynamic balance and dynamic imbalance labeled with the one or more features.