Disclosed are a method and apparatus for washing machine dehydration, and a washing machine. The method includes: step 1, detecting a current eccentricity value to serve as a first eccentricity value, and determining a first dehydration rotational speed value corresponding to the first eccentricity value; step 2, performing a corresponding dehydration operation; step 3, detecting another current eccentricity value to serve as a second eccentricity value, and determining a second dehydration rotational speed value corresponding to the second eccentricity value; step 4, if the second eccentricity value is smaller than the first eccentricity value, performing a corresponding dehydration operation, assigning the value of the second eccentricity value to the first eccentricity value and then performing the step 3; and step 5, if the second eccentricity value is greater than or equal to the first eccentricity value, performing a leveling operation, and then performing the step 3.

Disclosed are a method and apparatus for washing machine dehydration, and a washing machine. The method includes: step 1, detecting a current eccentricity value to serve as a first eccentricity value, and determining a first dehydration rotational speed value corresponding to the first eccentricity value; step 2, performing a corresponding dehydration operation; step 3, detecting another current eccentricity value to serve as a second eccentricity value, and determining a second dehydration rotational speed value corresponding to the second eccentricity value; step 4, if the second eccentricity value is smaller than the first eccentricity value, performing a corresponding dehydration operation, assigning the value of the second eccentricity value to the first eccentricity value and then performing the step 3; and step 5, if the second eccentricity value is greater than or equal to the first eccentricity value, performing a leveling operation, and then performing the step 3.

A laundry apparatus includes a tub, a drum, a control unit, a motor, and a dynamic balancing assembly. The drum is positioned within a fluid containment envelope of the tub and rotatable relative to the tub about a primary rotation axis. The motor is coupled to the tub and operatively coupled to the drum to cause rotation of the drum. The dynamic balancing assembly includes an orbital balancing passage arranged concentrically around the motor, a first counterweight device, and a second counterweight device. The first and second counterweight devices are positioned within the orbital balancing passage and are responsive to the control unit to move the first and second counterweight devices along the orbital balancing passage to adjust an angular position of the first and second counterweight devices. A cross-sectional plane passes through the dynamic balancing assembly, the motor, and the fluid containment envelope of the tub.

A laundry apparatus includes a tub, a drum, a control unit, a motor, and a dynamic balancing assembly. The drum is positioned within a fluid containment envelope of the tub and rotatable relative to the tub about a primary rotation axis. The motor is coupled to the tub and operatively coupled to the drum to cause rotation of the drum. The dynamic balancing assembly includes an orbital balancing passage arranged concentrically around the motor, a first counterweight device, and a second counterweight device. The first and second counterweight devices are positioned within the orbital balancing passage and are responsive to the control unit to move the first and second counterweight devices along the orbital balancing passage to adjust an angular position of the first and second counterweight devices. A cross-sectional plane passes through the dynamic balancing assembly, the motor, and the fluid containment envelope of the tub.

A washing machine includes a water storage tank, a vibration sensor provided to the water storage tank, a washing tub rotatably disposed in the water storage tank and configured to accommodate laundry therein, a motor configured to rotate the washing tub, and a controller. The controller is configured to: measure an unbalance (UB) value based on rotating the washing tub during a dewatering stroke; control the motor to stop rotating the washing tub based on the UB value exceeding an allowable UB value included in a preset UB table; measure a dewatering time of the laundry and a vibration value during the dewatering stroke using the vibration sensor; and change the allowable UB value included in the preset UB table based on the dewatering time and the vibration value.

A control method of a laundry treating apparatus includes rotating a drum at a reference rotational speed that is lower or higher than a resonance rotational speed of the laundry treating apparatus; measuring the maximum displacements of tub front and rear surfaces and a phase difference between the maximum displacements during the rotation of the drum at the reference rotational speed; determining a front unbalance (UB) mass located in a drum front area, a rear UB mass located in a drum rear area, and an angle between the UB masses based on the maximum displacements and the phase difference; and accelerating a drum rotational speed to a target rotational speed that is higher than the reference rotational speed and the resonance rotational speed, when the front and rear UB masses are in a preset allowable mass range and an angle between the UB masses is in an allowable angle range.

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.

Disclosed herein is a washing machine capable of appropriately identifying the presence or absence of waterproof clothing. The washing machine includes a cabinet, an outer tub elastically supported on the inside of the cabinet, an inner tub rotatably provided inside of the water tub, a motor configured to rotate the inner tub, a vibration sensor provided in the outer tub, a rotation sensor provided in the motor; and a at least one processor electrically connected to the vibration sensor, the rotation sensor and the motor and configured to control a rotation speed of the motor based on a vibration of the outer tub detected by the vibration sensor and a rotation of the motor detected by the rotation sensor.

Disclosed herein is a washing machine capable of appropriately identifying the presence or absence of waterproof clothing. The washing machine includes a cabinet, an outer tub elastically supported on the inside of the cabinet, an inner tub rotatably provided inside of the water tub, a motor configured to rotate the inner tub, a vibration sensor provided in the outer tub, a rotation sensor provided in the motor; and a at least one processor electrically connected to the vibration sensor, the rotation sensor and the motor and configured to control a rotation speed of the motor based on a vibration of the outer tub detected by the vibration sensor and a rotation of the motor detected by the rotation sensor.

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.