EP3904581A1 - Machine de traitement du linge et procédé de fonctionnement d'une machine de traitement du linge - Google Patents

Machine de traitement du linge et procédé de fonctionnement d'une machine de traitement du linge Download PDF

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Publication number
EP3904581A1
EP3904581A1 EP20172486.1A EP20172486A EP3904581A1 EP 3904581 A1 EP3904581 A1 EP 3904581A1 EP 20172486 A EP20172486 A EP 20172486A EP 3904581 A1 EP3904581 A1 EP 3904581A1
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EP
European Patent Office
Prior art keywords
load
laundry treatment
treatment machine
drum
neural network
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
EP20172486.1A
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German (de)
English (en)
Other versions
EP3904581B1 (fr
EP3904581C0 (fr
Inventor
Daniele Martinello
Viktor BOBEK
Vladimir Hudak
Pavol Petracek
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Haier Germany GmbH
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Haier Deutschland GmbH
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Publication date
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Priority to PL20172486.1T priority Critical patent/PL3904581T3/pl
Priority to EP20172486.1A priority patent/EP3904581B1/fr
Publication of EP3904581A1 publication Critical patent/EP3904581A1/fr
Application granted granted Critical
Publication of EP3904581B1 publication Critical patent/EP3904581B1/fr
Publication of EP3904581C0 publication Critical patent/EP3904581C0/fr
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    • D—TEXTILES; PAPER
    • D06—TREATMENT OF TEXTILES OR THE LIKE; LAUNDERING; FLEXIBLE MATERIALS NOT OTHERWISE PROVIDED FOR
    • D06F—LAUNDERING, DRYING, IRONING, PRESSING OR FOLDING TEXTILE ARTICLES
    • D06F34/00—Details of control systems for washing machines, washer-dryers or laundry dryers
    • D06F34/14—Arrangements for detecting or measuring specific parameters
    • D06F34/16—Imbalance
    • D—TEXTILES; PAPER
    • D06—TREATMENT OF TEXTILES OR THE LIKE; LAUNDERING; FLEXIBLE MATERIALS NOT OTHERWISE PROVIDED FOR
    • D06F—LAUNDERING, DRYING, IRONING, PRESSING OR FOLDING TEXTILE ARTICLES
    • D06F2103/00—Parameters monitored or detected for the control of domestic laundry washing machines, washer-dryers or laundry dryers
    • D06F2103/24—Spin speed; Drum movements
    • D—TEXTILES; PAPER
    • D06—TREATMENT OF TEXTILES OR THE LIKE; LAUNDERING; FLEXIBLE MATERIALS NOT OTHERWISE PROVIDED FOR
    • D06F—LAUNDERING, DRYING, IRONING, PRESSING OR FOLDING TEXTILE ARTICLES
    • D06F2103/00—Parameters monitored or detected for the control of domestic laundry washing machines, washer-dryers or laundry dryers
    • D06F2103/26—Imbalance; Noise level
    • D—TEXTILES; PAPER
    • D06—TREATMENT OF TEXTILES OR THE LIKE; LAUNDERING; FLEXIBLE MATERIALS NOT OTHERWISE PROVIDED FOR
    • D06F—LAUNDERING, DRYING, IRONING, PRESSING OR FOLDING TEXTILE ARTICLES
    • D06F33/00—Control of operations performed in washing machines or washer-dryers 
    • D06F33/30—Control of washing machines characterised by the purpose or target of the control 
    • D06F33/48—Preventing or reducing imbalance or noise
    • D—TEXTILES; PAPER
    • D06—TREATMENT OF TEXTILES OR THE LIKE; LAUNDERING; FLEXIBLE MATERIALS NOT OTHERWISE PROVIDED FOR
    • D06F—LAUNDERING, DRYING, IRONING, PRESSING OR FOLDING TEXTILE ARTICLES
    • D06F37/00—Details specific to washing machines covered by groups D06F21/00 - D06F25/00
    • D06F37/20—Mountings, e.g. resilient mountings, for the rotary receptacle, motor, tub or casing; Preventing or damping vibrations
    • D06F37/22—Mountings, e.g. resilient mountings, for the rotary receptacle, motor, tub or casing; Preventing or damping vibrations in machines with a receptacle rotating or oscillating about a horizontal axis

Definitions

  • the invention relates to a laundry treatment machine. Furthermore, the invention relates to a method to operate a laundry treatment machine.
  • the laundry treatment machine is a washing machine or a drying machine or a combined washing and drying machine.
  • the washing unit of a laundry treatment machine is suspended to the cabinet by a set of springs and dampers.
  • This mechanical system is adjusted to have a resonance frequency at a relatively low angular speed.
  • the angular speed of the drum has to cross this resonance region without interference between the tub and the cabinet.
  • Due to an increase of the drum size and a loading capacity of the laundry treatment machine the available space between the tub and the cabinet decreased.
  • a precise estimation of an unbalance of the load is required in order to avoid a mechanical impact between the tub and the cabinet when the resonance region is crossed.
  • the unbalance estimation has to be performed before the resonance region is crossed.
  • EP 1736 589 A2 discloses a washing machine with a measurement device for detecting an unbalance of the drum.
  • the washing machine comprises two acceleration sensors which are arranged in a twelve o'clock position at a front side and a rear side of the drum. Furthermore, the washing machine comprises an angular speed sensor with a detector which detects marks at a rotating body. A controller of the washing machine determines the position and the magnitude of the unbalance from the measurement values.
  • the at least one unbalance sensor is arranged at a position such that for the angle ⁇ applies: 0° ⁇ ⁇ ⁇ 90°, in particular 15° ⁇ ⁇ ⁇ 75°, and in particular 30° ⁇ ⁇ ⁇ 60°.
  • the angle ⁇ is defined in a projection plane which runs perpendicular to the axis of rotation.
  • the suspension system changes this angular position of high signal quality.
  • an angular position of ⁇ 90° results in a decreased drum size or in an increased risk of a mechanical impact during operation of the laundry treatment machine. Since 0° ⁇ ⁇ ⁇ 90° applies, the at least one unbalance sensor provides a good signal quality without the above described disadvantages.
  • the rotational axis defines a horizontal plane and the at least one unbalance sensor is arranged above and/or below this horizontal plane.
  • the at least one unbalance sensor is arranged between a 0 o'clock position and a 3 o'clock position and/or between a 0 o'clock position and a 9 o'clock position and/or between a 6 o'clock position and a 3 o'clock position and/or between a 6 o'clock position and a 9 o'clock position.
  • the at least one unbalance sensor is arranged at a tub of the laundry treatment machine.
  • the at least one unbalance sensor is at least one of an accelerometer sensor and an optical sensor.
  • the signal quality of the measurement signal of the at least one position sensor is increased, because the at least one part to be detected is arranged directly at the drum.
  • the laundry treatment machine has exactly one angular position sensor.
  • the at least one part to be detected is arranged at a rear side of the drum.
  • the at least one position sensor comprises a number of k parts to be detected wherein 1 ⁇ k ⁇ 32, in particular 2 ⁇ k ⁇ 16, and in particular 4 ⁇ k ⁇ 8.
  • the parts to be detected are attached at the drum at equal angular distances.
  • the detector is arranged at a tub and/or at a cabinet of the laundry treatment machine.
  • the attachment of the at least one part to be detected directly at the drum results in a decreased amount of mechanical disturbances and in an increased signal quality.
  • the at least one position sensor enables an easy assembly.
  • the at least one position sensor is a once per revolution sensor with a detector and exactly one part to be detected.
  • the exactly one part is mounted at a defined angular position at the drum such that the at least one position sensor provides an absolute angular position of the drum.
  • the detector is designed as Hall sensor or reed switch.
  • the at least one part to be detected is designed as a magnet.
  • the at least one unbalance sensor and the at least one position sensor can be used independently of each other or in combination and result both in an increase of signal quality such that the laundry treatment machine enables an estimation of the load behaviour in any easy, reliable and accurate manner.
  • the laundry treatment machine comprises a washing unit which is suspended to a cabinet by means of a suspension system.
  • the suspension system comprises springs and dampers.
  • a tub of the washing unit is suspended to the cabinet by means of the springs and the dampers.
  • the drum is arranged within the tub.
  • the drive motor may be connected to the drum directly or via a belt drive.
  • a laundry treatment machine ensures an easy, reliable and accurate estimation of the load behaviour.
  • a base of the laundry treatment machine defines a base plane.
  • the horizontal plane runs in parallel to the base plane.
  • the at least one unbalance sensor is arranged between a 0 o'clock position and a 3 o'clock position and/or between a 0 o'clock position and a 9 o'clock position.
  • a laundry treatment machine ensures an easy, reliable and accurate estimation of the load behaviour.
  • the laundry treatment machine has a centre of gravity.
  • the centre of gravity defines a vertical plane which runs perpendicular to the axis of rotation.
  • This vertical plane defines a front side and a rear side.
  • the first unbalance sensor and the second unbalance sensor are arranged at different sides of this vertical plane, namely at the front side and at the rear side.
  • the first unbalance sensor and the second unbalance sensor have a distance from each other in an axial direction.
  • the unbalance sensors enable to estimate the behaviour of load which is unequally distributed in a direction parallel to the axis of rotation.
  • the unbalance sensors may have equal or different measuring principles.
  • a laundry treatment machine ensures an easy, reliable and accurate estimation of the load behaviour.
  • the artificial neural network has an input layer, at least one hidden layer and an output layer.
  • the artificial neural network has a number M of hidden layers, wherein 1 ⁇ M ⁇ 5, in particular 2 ⁇ M ⁇ 4.
  • the artificial neural network is designed as feedforward neural network.
  • a laundry treatment machine ensures an easy, reliable and accurate estimation of the load behaviour.
  • An input layer of the artificial neural network provides at least one signal input.
  • the at least one signal input is connected to a signal output of the at least one unbalance sensor and/or a signal output of the at least one position sensor and/or a signal output to provide a torque signal.
  • the torque signal is provided by an estimation of the drive torque or by a desired drive torque of a speed controller of the drive motor.
  • the output signal of the speed controller characterizes the desired electromagnetic drive torque of the drive motor and can be used to estimate the drive torque.
  • a laundry treatment machine ensures an easy, reliable and accurate estimation of the load behaviour.
  • An output layer of the artificial neural network has at least one signal out.
  • the at least one signal output provides an estimation of at least one of a mass of the load, an angular position of the load, an axial position of the load, a force acting on the drum and a torque acting on the drum. The force and the torque are caused by the drive motor and the load.
  • the at least one signal output provides at least one output signal which can be used to estimate the dry load at the beginning of the washing cycle in order to set the amount of resources and energy, the wet load at the end of the washing cycle and/or the position and magnitude of the unbalanced load.
  • a laundry treatment machine ensures an easy, reliable and accurate estimate of the load behaviour.
  • the drive motor is directly connected via a drive shaft with the drum.
  • the drive shaft provides a stiff connection such that the drive torque is acting directly on the drum. Consequently, the torque signal accurately corresponds to the drive torque. This results in an accurate and reliable estimation of the load behaviour, in particular by means of the artificial neural network.
  • a laundry treatment machine ensures an easy, reliable and accurate estimation of the load behaviour.
  • the compensation unit is used to compensate an unbalanced load depending on the estimated behaviour. Additionally, the compensation unit can be used to perform a training of the artificial neural network.
  • the compensation unit comprises several balancers which can be filled individually with water. By filling an amount of water in at least one selected balancer in a defined manner the compensation unit can simulate an unbalanced load. This load is known such that the artificial neural network can be trained.
  • each balancer is divided into at least two subchambers which can be filled individually with water.
  • the compensation unit comprises at least one injection valve, preferably at least two injection valves, to fill water into the at least one balancer, in particular into at least one subchamber at a front side and/or rear side.
  • Each balancer is connected with at least one supply channel, preferably with two supply channels to supply the water from the at least one injection valve to the at least one balancer.
  • a method according to claim 10 ensures an easy, reliable and accurate estimation of the load behaviour.
  • the laundry treatment machine is operated below a resonance frequency and an angular speed of resonance during estimation of the load behaviour and/or training of an artificial neural network.
  • the angular speed ⁇ is lower than a critical resonance frequency, in particular lower than 300 rpm.
  • a method according to claim 11 ensures an easy, reliable and accurate estimation of the load behaviour.
  • an artificial neural network is used to estimate the load behaviour.
  • the artificial neural network is implemented into the control unit and has to be trained in advance to be able to estimate the load behaviour.
  • In order to train the artificial neural network at least one known unbalanced load is positioned in the drum.
  • the artificial neural network is trained by minimizing a quality function which comprises at least one error between the known behaviour of the provided load and a behaviour of the provided load which is estimated by means of the artificial neural network.
  • a method according to claim 12 ensures an easy, reliable and accurate estimation of the load behaviour.
  • the compensation unit is used to train the artificial neural network. At least one balancer of the compensation unit is filled with an amount of water in a defined manner such that a known unbalanced load is created. Afterwards the artificial neural network is trained to estimate the behaviour of the known unbalanced load. By using the compensation unit the training of the artificial neural network can be fully automated. This results in a considerable saving of time to train the artificial neural network.
  • a method according to claim 13 ensures an easy, reliable and accurate estimation of the load behaviour.
  • the accuracy of the estimation can be increased by increasing the number N of known unbalanced loads which are used to train the artificial neural network.
  • N Preferably, 2 ⁇ N ⁇ 500, in particular 10 ⁇ N ⁇ 400, in particular 20 ⁇ N ⁇ 300, and in particular 50 ⁇ N ⁇ 200.
  • the known unbalanced loads are different in their mass, in their angular position and/or in their axial position.
  • a method according to claim 14 ensures an easy, reliable and accurate estimation of the load behaviour.
  • the artificial neural network comprises weight parameters which have to be adapted during the training process.
  • the weight parameters are adapted by minimising a quality function.
  • the quality function comprises at least one error between a known behaviour of the provided load and an estimated behaviour of the provided load. For example, to estimate the mass of the load the corresponding quality function comprises an error between the known mass and an estimated mass.
  • a method according to claim 15 ensures an easy, reliable and accurate estimation of the load behaviour.
  • the trained artificial neural network is used to estimate the behaviour of a load caused by the laundry inside of the drum during normal operation of the laundry treatment machine.
  • the estimated behaviour of the load can be used to compensate the unbalance caused by the load by means of the compensation unit.
  • At least one balancer is filled with water such that the unbalance of the load is decreased. By compensating the unbalance of the load the mechanical stress on the drum and the bearings is reduced and a mechanical damage is prevented.
  • Fig. 1 shows a laundry treatment machine, namely a washing machine 1 with a cabinet 2 and a washing unit 3.
  • the washing unit 3 comprises a tub 4 and a drum 5.
  • the tub 4 is mounted to the cabinet 2 via dampers 6 and springs 7.
  • the drum 5 is mounted in a rotatable manner to the tub 4.
  • the tub 4 comprises a front wall F, a rear wall R and a circumferential wall W which is connected to the front wall F and the rear wall R.
  • the drum 5 is connected via a drive shaft 8 with a drive motor 9.
  • the drive motor 9 is mounted at the rear wall R of the tub 4.
  • the drive motor 9 rotates the drum 5 around a horizontal rotational axis 10 by exerting a drive torque T em .
  • the cabinet 2 comprises a base 11, four side walls 12 and a top cover 13.
  • the base 11 defines a base plane E B which runs in parallel to a horizontal x-direction and a horizontal y-direction.
  • the rotational axis 10 and the base plane E B define a vertical plane E V1 .
  • the vertical plane E V1 runs perpendicular to the base plane E B .
  • the vertical plane E V1 runs in parallel to the horizontal x-direction and a vertical z-direction.
  • the x-direction, the y-direction and the z-direction run in pairs perpendicular to each other and form a Cartesian coordinate system.
  • the washing machine 1 comprises a first unbalance sensor 14, a second unbalance sensor 15 and a position sensor 16.
  • the first unbalance sensor 14 is mounted to the circumferential wall W adjacent to the front wall F
  • the second unbalance sensor 15 is mounted to the circumferential wall W adjacent to the rear wall R.
  • the first unbalance sensor 14 determines a movement of the washing unit 3 transverse to the rotational axis 10 at the front wall F and provides a first unbalance signal a 1
  • the second unbalance sensor 15 determines a movement of the washing unit 3 transverse to the rotational axis 10 at the rear wall R and provides a second unbalance signal a 2 .
  • Fig. 2 shows the washing unit 3 without the rear wall R of the tub 4.
  • the vertical plane E V1 and each of the unbalance sensors 14, 15 enclose an angle a, wherein 0° ⁇ ⁇ ⁇ 90°, in particular 15° ⁇ ⁇ ⁇ 75°, and in particular 30° ⁇ ⁇ ⁇ 75°.
  • the angle ⁇ is defined in a projection plane, wherein the rotational axis 10 runs perpendicular to each projection plane.
  • the unbalance sensors 14, 15 are arranged in the z-direction above a horizontal plane E H .
  • the horizontal plane E H includes the rotational axis 10 and runs in parallel to the base plane E B .
  • the first unbalance sensor 14 and the second unbalance sensor 15 have a distance from each other in the x-direction and are mounted at the tub 4 at different sides of a vertical plane E V2 .
  • the vertical plane E V2 and the rotational axis 10 run perpendicular to each other, whereas the vertical plane E V2 includes a centre of gravity G of the washing unit 3.
  • the first unbalance sensor 14 and the second unbalance sensor 15 are uniaxial accelerometer sensors.
  • the position sensor 16 is a once per revolution sensor.
  • the position sensor 16 comprises a detector 17 and a part 18 to be detected.
  • the detector 17 is attached to the rear wall R of the tub 4.
  • the drum 5 comprises a front wall f, a rear wall r and a circumferential wall w which is connected to the front wall f and the rear wall r.
  • the drum 5 further comprises a starlike stiffening member S which is attached to the rear wall r.
  • the part 18 to be detected is attached at the stiffening member S.
  • the position sensor 16 provides an absolute angular position signal ⁇ of the drum 5.
  • the washing machine 1 comprises a compensation unit 19.
  • the compensation unit 19 comprises three balancers A 1 , A 2 , A 3 which move the laundry and can be filled with water.
  • the balancers A 1 , A 2 , A 3 are mounted in equal angular distances ⁇ to an inner side of the drum 5.
  • Each balancer A 1 , A 2 , A 3 is divided by partition walls p into subchambers s. Adjacent subchambers s are connected to each other via a through hole t. The subchambers s can be successively filled with water from the front wall f and/or the rear wall r.
  • Each balancer A 1 , A 2 , A 3 is filled with water via supply channels Z 11 , Z 12 , Z 21 , Z 22 , Z 31 , Z 32 , wherein the supply channels Z 11 , Z 21 , Z 31 serve to fill the balancers A 1 , A 2 , A 3 from the rear wall r and the supply channels Z 12 , Z 22 , Z 32 serve to fill the balancers A 1 , A 2 , A 3 from the front wall f.
  • the water is injected into the supply channels Z 11 , Z 21 , Z 31 via a first nozzle N 1 and into the supply channels Z 12 , Z 22 , Z 32 via a second nozzle N 2 .
  • the injected water is forced into the balancers A 1 , A 2 , A 3 by a centrifugal force which is caused by a rotation of the drum 5.
  • the washing machine 1 comprises a control unit 20 to control the operation.
  • the control unit 20 comprises a speed controller 21 and a torque controller 22.
  • the torque controller 22 is part of an inner control loop or a torque control loop to control the drive torque T em of the drive motor 9.
  • the torque controller 22 is a PI controller.
  • the speed controller 21 is part of an outer control loop or a speed control loop to control the angular speed ⁇ of the drive motor 9.
  • the speed controller 21 is a PI controller.
  • the speed controller 21 is provided with a difference of a desired angular speed and measured or estimated angular speed ⁇ of the drive motor 9.
  • the output signal of the speed controller 21 is a desired drive torque T* em which is used as an input signal for the torque controller 22.
  • the control unit 20 comprises an artificial neural network NN which serves to estimate the behaviour of a load L.
  • the load L is caused by the laundry inside of the drum 5.
  • the artificial neural network NN has an input layer L I , two hidden layers L H1 , L H2 and an output layer L O .
  • the input layer L I has four neurons which provide four signal inputs to receive four input signals s 1 , s 2 , s 3 , s 4 .
  • the output layer L O has five neurons with five signal outputs.
  • the five signal outputs provide five output signals o 1 , o 2 , o 3 , o 4 , o 5 .
  • the hidden layers L H1 , L H2 each comprise five neurons.
  • Each neuron of the input layer L I is connected with each neuron of the first hidden layer L H1 .
  • Each neuron of the first hidden layer L H1 is connected to each neuron of the second hidden layer L H2 .
  • each neuron of the second hidden layer L H2 is connected to each neuron of the output layer L O .
  • the artificial neural network NN is designed as a feedforward network.
  • the first unbalance sensor 14 provides the first unbalance signal a 1 which characterizes a movement of the washing unit 3 transverse to the rotational axis 10 near the front wall F.
  • the second unbalance sensor 15 provides the second unbalance signal a 2 which characterizes a movement of the washing unit 3 transverse to the rotational axis 10 near the rear wall R.
  • the first unbalance signal a 1 is transferred into a frequency domain by calculating a fourier transformation.
  • the first input signal s 1 of the artificial neural network NN is equal to the transferred first unbalance signal a 1 .
  • the second unbalance signal a 2 is transferred into a frequency domain by calculating a fourier transformation.
  • the second input signal s 2 of the artificial neural network NN is equal to the transferred second unbalance signal a 2 .
  • the position sensor 16 provides the position signal ⁇ .
  • the position sensor 16 is connected to a third signal input such that the third input signal s 3 is equal to the position signal ⁇ .
  • the speed controller 21 is connected to a fourth signal input such that the fourth input signal s 4 is equal to the desired drive torque T* em .
  • the artificial neural network NN is trained such that a first output signal o 1 estimates the mass m of the load L, a second output signal o 2 estimates an axial position X L of the load L, a third output signal o 3 estimates an angular position ⁇ L of the load L, a fourth output signal o 4 estimates a torque T acting on the drum 5 and a fifth output signal o 5 estimates a force F acting on the drum 5.
  • T L m ⁇ g ⁇ r ⁇ sin(( ⁇ L ), wherein g denotes the gravitational acceration.
  • a known unbalanced load L is generated by means of the compensation unit 19. At least one of the balancers A 1 , A 2 , A 3 is filled with a defined amount of water such that an unbalanced load L is simulated.
  • the load L has a known mass m, a known axial position X L , a known angular position ⁇ L such that the drive motor 9 and the load L create a known torque T and a known force F.
  • a second step S 2 the washing machine 1 is operated and the neural network NN is trained.
  • the training process is shown in fig. 6 , wherein s is a vector of all input signals s 1 to s 4 and o is a vector of all output signals o 1 to o 5 .
  • the known load L provides desired output signals of the artificial neural network NN which are summarized in a vector o L .
  • the weight parameters w 0 , w i of the artificial neural network NN are adapted such that the quality function is minimized.
  • the steps S 1 and S 2 are repeated for a number N of known unbalanced loads L, wherein 2 ⁇ N ⁇ 500.
  • the known unbalanced loads L are different of each other with respect to their mass m, their axial positions x L and their angular positions ⁇ L .
  • the training process takes place at an angular speed ⁇ of the drum 5 which is lower than an angular speed of resonance, wherein in particular 50 rpm ⁇ ⁇ ⁇ 250 rpm, in particular 75 rpm ⁇ ⁇ ⁇ 200 rpm, and in particular 100 rpm ⁇ ⁇ ⁇ 150 rpm.
  • the artificial neural network NN can be used in a step S 3 to estimate the behaviour of an unknown load L during normal operation of the washing machine 1.
  • step S 3 the adaption of the weight parameters w 0 , w i is finished and disabled such that the weight parameters w 0 , w i are constant. Due to the trained artificial neural network NN the behaviour of the unknown load L, namely the output signals o 1 to o 5 according to fig. 4 can be estimated.
  • a step S 4 the output signals o 1 to o 5 can be used to compensate the unbalance of the load L by means of the compensation unit 19.
  • the compensation unit 19 determines the at least one balancer A 1 , A 2 , A 3 , the amount of water and the nozzle N 1 , N 2 to compensate the unbalanced load L.
  • the unbalance signals a 1 , a 2 Due to the angular position of the first unbalance sensor 14 and the second unbalance sensor 15 the unbalance signals a 1 , a 2 have a good signal quality. Furthermore, due to their angular position the unbalance sensors 14, 15 do not reduce the available space between the tub 4 and the cabinet 2.
  • the potion sensor 16 provides an absolute angular position ⁇ of the drum 5 since exactly one part 18 to be detected is attached directly at the drum 5. Furthermore, the angular position ⁇ has a high quality since the measurement is not affected by mechanical disturbances.

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  • Engineering & Computer Science (AREA)
  • Textile Engineering (AREA)
  • Control Of Washing Machine And Dryer (AREA)
EP20172486.1A 2020-04-30 2020-04-30 Machine de traitement du linge et procédé de fonctionnement d'une machine de traitement du linge Active EP3904581B1 (fr)

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PL20172486.1T PL3904581T3 (pl) 2020-04-30 2020-04-30 Maszyna pralnicza i sposób działania maszyny pralniczej
EP20172486.1A EP3904581B1 (fr) 2020-04-30 2020-04-30 Machine de traitement du linge et procédé de fonctionnement d'une machine de traitement du linge

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EP20172486.1A EP3904581B1 (fr) 2020-04-30 2020-04-30 Machine de traitement du linge et procédé de fonctionnement d'une machine de traitement du linge

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EP3904581A1 true EP3904581A1 (fr) 2021-11-03
EP3904581B1 EP3904581B1 (fr) 2025-06-18
EP3904581C0 EP3904581C0 (fr) 2025-06-18

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CN117248356A (zh) * 2022-06-10 2023-12-19 江南大学 一种主被动结合实现动平衡的滚筒洗衣机
EP4481352A1 (fr) * 2023-06-21 2024-12-25 BSH Hausgeräte GmbH Détermination d'un balourd d'un composant tournant autour d'un axe de rotation

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US20200010999A1 (en) * 2019-08-20 2020-01-09 Lg Electronics Inc. Method for inspecting unbalance error of washing machine and washing machine

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EP1736589A2 (fr) 2005-06-24 2006-12-27 V-Zug AG Machine à laver avec détecteur du balourd
US20120073062A1 (en) * 2010-09-28 2012-03-29 Whirlpool Corporation Method for controlling a laundry treating appliance based on a floor parameter
US20190390389A1 (en) * 2019-06-21 2019-12-26 Lg Electronics Inc. Method, device, and system for detecting dynamic imbalance of washing machine
US20190382935A1 (en) * 2019-08-05 2019-12-19 Lg Electronics Inc. Washing machine and a control method of the same
US20200010999A1 (en) * 2019-08-20 2020-01-09 Lg Electronics Inc. Method for inspecting unbalance error of washing machine and washing machine

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CN117248356A (zh) * 2022-06-10 2023-12-19 江南大学 一种主被动结合实现动平衡的滚筒洗衣机
EP4481352A1 (fr) * 2023-06-21 2024-12-25 BSH Hausgeräte GmbH Détermination d'un balourd d'un composant tournant autour d'un axe de rotation

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