Method and device for detecting and avoiding resonance during cycle of washer

Abstract

A washer may obtain driving configuration information based on a driving profile of the washer, the driving configuration information including information related to revolutions per minute (RPM) of a driver or a drum based on a spin cycle of the washer, obtain RPM data and vibration data during a first spin cycle based on the driving configuration information, identify whether a resonance associated with a floor is generated via at least one artificial intelligence (AI) model based on the RPM data and the vibration data, and adjust one or more values of the RPM-related information based on an RPM value associated with the resonance based on identifying that generation of the resonance associated with the floor being identified. A second spin cycle may be performed after the first spin cycle based on the adjusted RPM-related information.

What is claimed is: 
     
         1 . A washer, comprising:
 a housing;   a tub inside the housing;   a drum inside the tub and configured to be rotated with respect to the tub;   a driver inside the housing and configured to rotate the drum;   one or more sensors including a first sensor;   one or more processors; and   a memory including one or more storage media storing instructions,   wherein the instructions, when executed by the one or more processors, cause the washer to:
 obtain driving configuration information based on a driving profile of the washer, the driving configuration information comprising information related to revolutions per minute (RPM) of the driver or the drum associated with a spin cycle of the washer; 
 obtain RPM data and vibration data during a first spin cycle based on the driving configuration information, the RPM data comprising RPM values of the driver or the drum obtained during the first spin cycle, the vibration data, obtained via the first sensor, comprising values of a vibration of the washer during the first spin cycle; 
 identify whether a resonance associated with a floor is generated via one or more artificial intelligence (AI) models based on the RPM data and the vibration data, the washer being on the floor; and 
 adjust one or more values of the information related to RPM based on an RPM value associated with the resonance in a state in which generation of the resonance associated with the floor is identified, and 
   wherein the instructions, when executed by the one or more processors, further cause the washer to perform a second spin cycle, after the first spin cycle, based on the adjusted information related to RPM.   
     
     
         2 . The washer of  claim 1 , wherein the instructions, when executed by the one or more processors, further cause the washer to:
 input first input data based on the RPM data and the vibration data to a first AI model and obtain information about a state of the floor as output data of the first AI model; and   identify whether the resonance associated with the state of the floor is generated, via an algorithm for filtering resonance associated with laundry received in the drum, based on washer data comprising the RPM data and the vibration data, and the information about the state of the floor,   wherein the instructions, when executed by the one or more processors, further cause the washer to set the information about the state of the floor to a first value indicating that the floor is a soft floor or a second value indicating that the floor is a hard floor.   
     
     
         3 . The washer of  claim 2 , wherein the algorithm comprises:
 a first algorithm configured to filter the resonance associated with the laundry using information about a pattern associated with the resonance by the laundry;   a second algorithm configured to filter the resonance associated with the laundry using the vibration data and additional vibration data obtained, via a second sensor adjacent to the tub, during the first spin cycle; and   a third algorithm configured to filter the resonance associated with the laundry using information about a frequency of resonance generation.   
     
     
         4 . The washer of  claim 1 , wherein the instructions, when executed by the one or more processors, further cause the washer to:
 input first input data based on the RPM data and the vibration data to a first AI model and obtain information about a state of the floor as output data of the first AI model; and   input second input data based on the information about the state of the floor and washer data comprising the RPM data and the vibration data to a second AI model and obtain information indicating whether the resonance associated with the state of the floor is generated as output data of the second AI model,   wherein the instructions, when executed by the one or more processors, further cause the washer to set the information indicating whether the resonance associated with the state of the floor is generated to a first value indicating that the resonance associated with the state of the floor is generated or a second value indicating that the resonance associated with the state of the floor is not generated.   
     
     
         5 . The washer of  claim 1 , wherein the instructions, when executed by the one or more processors, further cause the washer to input third input data based on washer data comprising the RPM data and the vibration data to a third AI model and obtain information indicating whether the resonance associated with a state of the floor is generated as output data of the third AI model. 
     
     
         6 . The washer of  claim 1 , wherein the information related to RPM comprises information indicating a setting value of a final spin RPM to be used in the first spin cycle, and
 wherein the instructions, when executed by the one or more processors, further cause the washer to:   identify a basis spin RPM causing the resonance based on identifying generation of the resonance associated with a state of the floor;   obtain a harmonic resonance RPM based on the basis spin RPM; and   adjust the final spin RPM based on the harmonic resonance RPM.   
     
     
         7 . The washer of  claim 1 , wherein the instructions, when executed by the one or more processors, further cause the washer to obtain, during the first spin cycle, the RPM data and the vibration data at the same sampling period. 
     
     
         8 . The washer of  claim 2 , wherein the instructions, when executed by the one or more processors, further cause the washer to obtain weight data related to a weight of the laundry, via a third sensor, during the first spin cycle, and
 wherein the washer data further comprises the weight data, the weight data being used to identify whether the resonance associated with the state of the floor is generated.   
     
     
         9 . The washer of  claim 1 , wherein the one or more AI models is trained by a supervised learning scheme using training data comprising the vibration data obtained through the first sensor, the RPM data related to the RPM of the driver or the drum, the information related to a state of the floor, and label data. 
     
     
         10 . The washer of  claim 9 , wherein the one or more AI models is further trained based on a trigger signal received from a server, and
 wherein the trigger signal is generated by the server based on complaint data related to the resonance.   
     
     
         11 . The washer of  claim 1 , wherein the driving profile corresponds to an initial driving profile among a plurality of driving profiles and is selected based on information related to an installation position of the washer, and
 wherein the plurality of driving profiles are based on statistical analysis of a residential environment.   
     
     
         12 . A server, comprising,
 a communication device;   one or more processors; and   a memory including one or more storage media storing instructions,   wherein the instructions, when executed by the one or more processors cause the server to:
 receive, from a washer through the communication device, RPM data and vibration data obtained during a first spin cycle based on driving configuration information, the driving configuration information being obtained based on a driving profile of the washer and comprising information related to revolutions per minute (RPM) of a driver or a drum associated with a spin cycle of the washer, the RPM data comprising RPM values of the driver or the drum obtained during the first spin cycle, and the vibration data comprising values related to a vibration of the washer obtained through a first sensor during the first spin cycle; 
 identify whether a resonance associated with a state of a floor is generated via one or more artificial intelligence (AI) models based on the RPM data and the vibration data, the washer being on the floor; 
 adjust one or more values of the RPM-related information based on an RPM value associated with the resonance in a state in which generation of the resonance associated with the floor is identified; and 
 transmit the adjusted RPM-related information to the washer through the communication device, and 
   wherein the instructions, when executed by the one or more processors, further cause the server to cause the washer to perform a second spin cycle, after the first spin cycle, based on the adjusted information related to RPM.   
     
     
         13 . The server of  claim 12 , wherein the instructions, when executed by the one or more processors, further cause the server to:
 input first input data based on the RPM data and the vibration data to a first AI model and obtain information about a state of the floor as output data of the first AI model; and   identify whether the resonance associated with the state of the floor is generated, via an algorithm for filtering resonance associated with laundry received in the drum, based on washer data comprising the RPM data and the vibration data, and the information about the state of the floor,   wherein the instructions, when executed by the one or more processors, further cause the server to set the information about the state of the floor to a first value indicating that the floor is a soft floor or a second value indicating that the floor is a hard floor.   
     
     
         14 . The server of  claim 13 , wherein the algorithm comprises:
 a first algorithm configured to filter the resonance associated with the laundry using information about a pattern associated with the resonance by eccentricity of the laundry;   a second algorithm configured to filter the resonance associated with the laundry using the vibration data and additional vibration data obtained, via a second sensor adjacent to a tub, during the first spin cycle; and   a third algorithm configured to filter the resonance associated with the laundry using information about a frequency of resonance generation.   
     
     
         15 . The server of  claim 12 , wherein the instructions, when executed by the one or more processors, further cause the server to:
 input first input data based on the RPM data and the vibration data to a first AI model and obtain information about a state of the floor as output data of the first AI model; and   input second input data based on the information about the state of the floor and washer data comprising the RPM data and the vibration data to a second AI model and obtain information indicating whether the resonance associated with the state of the floor is generated as output data of the second AI model,   wherein the instructions, when executed by the one or more processors, further cause the server to set the information indicating whether the resonance associated with the state of the floor is generated to a first value indicating that the resonance associated with the state of the floor is generated or a second value indicating that the resonance associated with the state of the floor is not generated.   
     
     
         16 . The server of  claim 12 , wherein the instructions, when executed by the one or more processors, further cause the server to input third input data based on washer data comprising the RPM data and the vibration data to a third AI model and obtain information indicating whether the resonance associated with the state of the floor is generated as output data of the third AI model. 
     
     
         17 . The server of  claim 12 , wherein the information related to RPM comprises information indicating a setting value of a final spin RPM to be used in the first spin cycle, and
 wherein the instructions, when executed by the one or more processors, further cause the server to:
 identify a basis spin RPM causing the resonance based on identifying generation of the resonance associated with the state of the floor; 
 obtain a harmonic resonance RPM based on the basis spin RPM; and 
 adjust the final spin RPM based on the harmonic resonance RPM. 
   
     
     
         18 . The server of  claim 12 , wherein the RPM data and the vibration data are obtained at the same sampling period during the first spin cycle. 
     
     
         19 . The server of  claim 13 , wherein the instructions, when executed by the one or more processors, further cause the server to receive, from the washer through the communication device, weight data related to a weight of the laundry, via a third sensor, during the first spin cycle, and
 wherein the washer data further comprises the weight data, the weight data being used to identify whether the resonance associated with the state of the floor is generated.   
     
     
         20 . The server of  claim 12 , wherein the one or more AI models is trained by a supervised learning scheme using training data comprising the vibration data obtained through the first sensor, the RPM data related to the RPM of the driver or the drum, the information related to a state of the floor, and label data,
 wherein the one or more AI models is further trained based on a trigger signal received from an external server, and   wherein the trigger signal is generated by the external server based on complaint data related to the resonance.