US20230220608A1

US20230220608A1 - Dryer airflow calibration and alerts

Info

Publication number: US20230220608A1
Application number: US18/067,253
Authority: US
Inventors: Ryan R. Bellinger; Dwayne S. Bontrager; Mario A. GONCALVES; Gobi Krishnan Mallikarjunan; K. David McCALLISTER
Original assignee: Whirlpool Corp
Current assignee: Whirlpool Corp
Priority date: 2022-01-11
Filing date: 2022-12-16
Publication date: 2023-07-13

Abstract

Detection of airflow conditions, such as blockage issues, in a dryer laundry appliance is provided. A current or instant calibration is performed utilizing an airflow model to infer, based on current or instant sensor data from sensors of the dryer laundry appliance, an estimated airflow for an exhaust air conduit of the dryer laundry appliance. The estimated airflow is compared to a baseline airflow previously inferred by the airflow model during a baseline calibration using previous sensor data from the sensors of the dryer laundry appliance. An alert is provided responsive to the estimated airflow being below a threshold level relative to the baseline airflow.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application Serial No. 63,298/267 filed Jan. 11, 2022, the disclosure of which is hereby incorporated in its entirety by reference herein.

TECHNICAL FIELD

Disclosed herein are approaches for airflow calibration and detection of airflow conditions and issues in dryer laundry appliances.

BACKGROUND

Laundry treating appliances, such as clothes washers, clothes dryers, and refreshers, for example, may have a configuration based on a rotating drum that defines a treating chamber in which laundry items are placed for treating according to a cycle of operation. The laundry treating appliance may have a controller that implements a number of pre-programmed cycles of operation having one or more operating parameters. The cycle of operation may be selected manually by the user or automatically based on one or more conditions determined by the controller.
In some laundry treating appliances, one or more operating parameters may be set based on a type, e.g., fabric type and/or fabric mix, of laundry placed inside of the treating chamber. The type of laundry may be provided by a user or automatically detected by the laundry treating appliance. In other laundry treating appliances, one or more operating parameters may be set based on the moisture content of the load of laundry. Commonly used sensors known as moisture strips are located in the treating chamber and detect the conductivity, and therefore the moisture, of the laundry during a cycle of operation.

SUMMARY

In one or more illustrative examples, a method for detection of airflow conditions in a dryer laundry appliance is provided, such as detecting airflow reduction or blockage issues, for example. A current calibration (i.e. present or instant calibration) is performed utilizing an airflow model to infer, based on current sensor data (i.e. present or instant sensor data) from sensors of the dryer laundry appliance, an estimated airflow for an exhaust air conduit of the dryer laundry appliance. The estimated airflow is compared to a baseline airflow previously inferred by the airflow model during a baseline calibration using previous sensor data from the sensors of the dryer laundry appliance. An alert is provided responsive to the estimated airflow being below a threshold level relative to the baseline airflow.
In one or more illustrative examples, the method includes, responsive to installation of the dryer laundry appliance, performing the baseline calibration utilizing the airflow model to infer, based on baseline sensor data from the sensors of the dryer laundry appliance, the baseline airflow for the exhaust air conduit of the dryer laundry appliance.
In one or more illustrative examples, the exhaust air conduit of the dryer laundry appliance is connected to a vent, and the alert indicates that the vent is blocked.
In one or more illustrative examples, the method includes determining the estimated airflow responsive to initiation of a drying cycle.
In one or more illustrative examples, the method includes determining the estimated airflow responsive to a predefined period of time having passed since a previous estimated airflow or the baseline airflow was computed.
In one or more illustrative examples, the method includes determining the estimated airflow responsive to selection of a calibration mode from a user interface of the dryer laundry appliance.
In one or more illustrative examples, the method includes determining the estimated airflow responsive to selection of a calibration mode from a user interface of a mobile device in wireless communication with the dryer laundry appliance.
In one or more illustrative examples, the method includes determining the estimated airflow responsive to occurrence of a fault code or other self-diagnostic of the dryer laundry appliance.
In one or more illustrative examples, the baseline airflow is determined using calibration parameters defining one or more of ambient temperature, connection or disconnection of the exhaust air conduit to a vent, or whether the dryer laundry appliance has recently been run and is in a hot state or has cooled and is in a cooled state.
In one or more illustrative examples, the current sensor data is indicative of one or more of load mass in a drum of the dryer laundry appliance, temperature of air in the exhaust air conduit, or voltage used to operate electrical components of the dryer laundry appliance.
In one or more illustrative examples, the current sensor data includes machine age data indicative of age or wear level of the dryer laundry appliance, and the machine age data is provided as an input to the airflow model to account for the age or wear level of the dryer laundry appliance.
In one or more illustrative examples, the current sensor data includes machine age data indicative of age or wear level of the dryer laundry appliance, and the method includes utilizing the airflow model to compute an unaged airflow using the current sensor data; and utilizing an aging model adjust the unaged airflow into the estimated airflow based on the machine age data to account for the age or wear level of the dryer laundry appliance.
In one or more illustrative examples, the method includes computing a rolling average of the estimated airflow over time; and provide the alert responsive to the rolling average of the estimated airflow being below the threshold level indicated by the baseline airflow.
In one or more illustrative examples, the method includes performing a first calibration with the exhaust air conduit of the dryer laundry appliance connected to a vent; performing a second calibration with the dryer laundry appliance disconnected from the vent; comparing the estimated airflow to the first calibration and to the second calibration; and providing, in the alert, that the dryer laundry appliance has disconnected from the vent responsive to the estimated airflow matching the second calibration and not the first calibration.
In one or more illustrative examples, the method includes performing a first calibration as a cold run with the dryer laundry appliance at ambient temperature; performing a second calibration as a hot run with the dryer laundry appliance warm from a previous cycle; utilizing the first calibration for the comparing of the estimated airflow to the baseline airflow responsive to the current sensor data indicative the estimated airflow as being computed for a subsequent cold run; and utilizing the second calibration for the comparing of the estimated airflow to the baseline airflow responsive to the current sensor data indicative the estimated airflow as being computed for a subsequent hot run.
In one or more illustrative examples, the method includes performing the baseline calibration responsive to completion of a temperature-based pre-cooldown mode; and performing the current calibration also responsive to completion of the temperature-based pre-cooldown mode, thereby ensuring a consistent temperature for performance of the baseline calibration and the current calibration.
In one or more illustrative examples, the current sensor data includes historical information to allow the model to estimate changes in the estimated airflow over time.
In one or more illustrative examples, the airflow model is a recurrent neural network trained to analyze sequential data; and inputs to the airflow model include the historical information in addition to the current sensor data.
In one or more illustrative examples, the current sensor data includes data indicative of voltage powering the dryer laundry appliance, ambient temperature surrounding the dryer laundry appliance, initial conditions of the dryer laundry appliance, and/or machine age of the dryer laundry appliance.
In one or more illustrative examples, the current sensor data includes data indicative of load size in a drum of the dryer laundry appliance, tumble pattern used by the dryer laundry appliance to perform a selected cycle, gas pressure of gas powering the dryer laundry appliance, gas type powering the dryer laundry appliance, and/or status of a gas heating value connecting the gas to the dryer laundry appliance.
A dryer laundry appliance includes a controller programmed to perform any of the aforementioned methods. A non-transitory computer-readable medium includes instructions that, when executed by a controller of the dryer laundry appliance, causes the dryer laundry appliance to perform any of the aforementioned methods.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the present disclosure are pointed out with particularity in the appended claims. However, other features of the various embodiments will become more apparent and will be best understood by referring to the following detailed description in conjunction with the accompanying drawings in which:

FIG. 1 is a front perspective view of a clothes dryer, wherein the clothes dryer may be controlled according to aspects of the present disclosure;

FIG. 2 is a front schematic view of the clothes dryer of FIG. 1 ;

FIG. 3 is a schematic representation of a controller for controlling the operation of one or more components of the clothes dryer of FIG. 1 ;

FIG. 4A illustrates an example of an airflow model for use in determining an estimated airflow for the clothes dryer of FIG. 1 ;

FIG. 4B illustrates an alternate example of an airflow model for use in determining an estimated airflow for the clothes dryer of FIG. 1 ; and

FIG. 5A illustrates an example process for operation of a calibration mode for the clothes dryer for the clothes dryer of FIG. 1 ; and

FIG. 5B illustrates an alternate example process for operation of a calibration mode for the clothes dryer utilizing a rolling average of estimated airflow for the clothes dryer of FIG. 1 .

DETAILED DESCRIPTION

As required, detailed embodiments of the present disclosure are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimized to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present disclosure.
FIG. 1 illustrates one embodiment of a laundry treating appliance in the form of a clothes dryer 10 according to aspects of the present disclosure. While the laundry treating appliance is illustrated as a front-loading dryer, the laundry treating appliance according to aspects of the present disclosure may be another appliance which performs a cycle of operation on laundry, non-limiting examples of which include a top-loading dryer, a combination washing machine and dryer; a tumbling or stationary refreshing/revitalizing machine; an extractor; a non-aqueous washing apparatus; and a revitalizing machine.
As illustrated in FIG. 1 , the clothes dryer 10 may include a cabinet 12 in which is provided a controller 14 that may receive input from a user through a user interface 16 for selecting a cycle of operation and controlling the operation of the clothes dryer 10 to implement the selected cycle of operation. The clothes dryer 10 will offer the user a number of pre-programmed cycles of operation to choose from, and each pre-programmed cycle of operation may have any number of adjustable cycle modifiers. Examples of such modifiers include, but are not limited to chemistry dispensing, load size, a load color, and/or a load type. The controller may include a computer device, such as including a processor and software adapted to perform the operations described herein, including methods 400A, 400B, 500A, and 500B, described below and illustrated in corresponding FIGS. 4A-5B.
The cabinet 12 may be defined by a chassis or frame supporting a front wall 18, a rear wall 20, and a pair of side walls 22 supporting a top wall 24. A door 26 may be hingedly mounted to the front wall 18 and may be selectively moveable between opened and closed positions to close an opening in the front wall 18, which provides access to the interior of the cabinet 12.
A rotatable drum 28 may be disposed within the interior of the cabinet 12 between opposing front and rear bulkheads 30 and 32, which collectively define a treating chamber 34 having an open face that may be selectively closed by the door 26. The drum 28 may include at least one baffle or lifter 36. In most clothes dryers 10, there are multiple lifters 36. The lifters 36 may be located along the inner surface of the drum 28 defining an interior circumference of the drum 28. The lifters 36 may facilitate movement of laundry within the drum 28 as the drum 28 rotates.
Referring to FIG. 2 , an air flow system for the clothes dryer 10 is schematically illustrated and supplies air to the treating chamber 34 and then exhausts air from the treating chamber 34. The air flow system may have an air supply portion that may be formed in part by a supply air conduit 38, which has one end open to the ambient air and another end fluidly coupled to the treating chamber 34. Specifically, the supply air conduit 38 may couple with the treating chamber 34 through an inlet grill (not shown) formed in the rear bulkhead 32. A fan 40 and a heater 42 may lie within the supply air conduit 38 and may be operably coupled to and controlled by the controller 14. If the heater 42 is cycled on, the supplied air will be heated prior to entering the drum 28. The air supply system may further include an air exhaust portion that may be formed in part by an exhaust air conduit 44. Operation of the fan 40 draws air into the treating chamber 34 by the supply air conduit 38 and exhausts air from the treating chamber 34 through the exhaust air conduit 44. The exhaust air conduit 44 may be fluidly coupled with a household exhaust duct (not shown) for exhausting the air from the treating chamber 34 to the outside environment. This exhaust duct may be referred to herein as a vent. However, other air flow systems are possible as well as other arrangements of the fan 40 and heater 42. For example, the fan 40 may be located in the exhaust air conduit 44 instead of the supply air conduit 38.
The clothes dryer 10 may be provided with a temperature sensor 50 to determine the temperature of the air in the exhaust air conduit 44. One example of a temperature sensor 50 is a thermocouple. The temperature sensor 50 may be operably coupled to the controller 14 such that the controller 14 receives output from the temperature sensor 50. The temperature sensor 50 may be mounted at any location in or near the exhaust air conduit 44 of the clothes dryer 10 such that the temperature sensor 50 may be able to accurately sense the temperature of the exhaust air flow. For example, the temperature sensor 50 may be coupled the cabinet 12 in the area if the exhaust air conduit 44.
The drum 28 may be rotated by a suitable drive mechanism, which is illustrated as a motor 46 and a coupled belt 48. The motor 46 may be operably coupled to the controller 14 to control the rotation of the drum 28 to complete a cycle of operation. Other drive mechanisms, such as direct drive, may also be used.
The clothes dryer 10 may also have a dispensing system (not shown) for dispensing treating chemistries into the treating chamber 34. The dispensing system may introduce treating chemistry into the drum 28 in any suitable manner, such as by spraying, dripping, or providing a steady flow of the treating chemistry. The treating chemistry may be in a form of gas, liquid, solid or any combination thereof and may have any chemical composition enabling refreshment, disinfection, whitening, brightening, increased softness, reduced odor, reduced wrinkling, stain repellency or any other desired treatment of the laundry. Water is one example of a suitable treating chemistry. Other non-limiting examples of suitable treating chemistries are chromophore chemistry, softening chemistry, and stain-repellency chemistry. In all cases, the treating chemistries may be composed of a single chemical, a mixture of chemicals, or a solution of water and one or more chemicals.
As illustrated in FIG. 3 , the controller 14 may be provided with a memory 70 and a central processing unit (CPU) 72. The memory 70 may be used for storing the control software that may be executed by the CPU 72 in completing a cycle of operation using the clothes dryer 10 and any additional software. The memory 70 may also be used to store information, such as a database or table, and to store data received from the one or more components of the clothes dryer 10 that may be communicably coupled with the controller 14.
The controller 14 may be operably coupled with one or more components of the clothes dryer 10 for communicating with and/or controlling the operation of the component to complete a cycle of operation. For example, the controller 14 may be coupled with the fan 40 and the heater 42 for controlling the temperature and flow rate of the air flow through the treating chamber 34; the motor 46 for controlling the direction and speed of rotation of the drum 28; the temperature sensor 50 for receiving information about the temperature of the exhaust air flow; and the user interface 16 for receiving user selected inputs and communicating information to the user. The controller 14 may also receive input from various additional sensors 52, which are known in the art and not shown for simplicity. Non-limiting examples of additional sensors 52 that may be communicably coupled with the controller 14 include: a treating chamber 34, a temperature sensor 50, a supply air flow temperature sensor 50, a moisture sensor, an air flow rate sensor, a weight sensor, and a motor torque sensor.
Generally, in normal operation of the clothes dryer 10, a user first selects a cycle of operation via the user interface 16. The user may also select one or more cycle modifiers. In accordance with the user-selected cycle and cycle modifiers, the controller 14 may control the operation of the rotatable drum 28, the fan 40 and the heater 42, to implement the cycle of operation to dry the laundry. When instructed by the controller 14, the motor 46 rotates the drum 28 via the belt 48. The fan 40 draws air through the supply air conduit 38 and into the treating chamber 34, as illustrated by the flow vectors. The air may be heated by the heater 42. Air may be vented through the exhaust air conduit 44 to remove moisture from the treating chamber 34. During the cycle, treating chemistry may be dispensed into the treating chamber 34. Also during the cycle, output generated by the temperature sensor 50 and any additional sensors 52 may be utilized to generate digital data corresponding to sensed operational conditions inside the treating chamber 34. The output may be sent to the controller 14 for use in calculating operational conditions inside the treating chamber 34, or the output may be indicative of the operational condition. Once the output is received, the controller 14 processes the output for storage in the memory 70. The controller 14 may convert the output during processing such that it may be properly stored in the memory 70 as digital data. The stored digital data may be processed in a buffer memory, and used, along with pre-selected coefficients, in algorithms to electronically calculate various operational conditions, such as a degree of wetness or moisture content of the laundry. The controller 14 may use both the cycle modifiers specified by the user and the additional information obtained by the sensors 50, 52 to carry out the desired cycle of operation.
The previously described clothes dryer 10 provides the structure for the implementation of aspects of the present disclosure. Several embodiments of the method will now be described in terms of the operation of the clothes dryer 10. The embodiments of the method function to ensure proper drying of a load of laundry, as well as alerting to conditions such as a blocked dryer vent.
FIG. 4A illustrates an example 400A of an airflow model 402 for use in determining an estimated airflow 404. The airflow model 402 may be configured to receive inputs such as load mass 406, temperature 408. voltage 410. The airflow model 402 may also in some examples receive additional factors 412 and/or machine age 414 information. Based on the inputs, the airflow model 402 may be configured to infer an estimated airflow 404 in the exhaust air conduit 44. This estimated airflow 404 may be used to determine whether there is a venting issue with the clothes dryer 10.
The load mass 406 may include information or data indicative of the quantity of laundry in the drum 28. In an example, the load mass 406 information may include sensor data from a weight sensor configured to measure the weight of the contents of the drum 28. In another example, the load mass 406 may be inferred from the selected cycle of operation (e.g., towel dry, delicates, etc.). In yet another example, the load mass 406 may be inferred by torque required from the motor 46 to rotate the drum 28 (e.g., the greater the torque, the greater the load mass 406). The torque may be estimated from the electric current draw of the motor 46, as the torque may be directly proportional to the electric current.
The temperature 408 may include information or data indicative of the heat level in the exhaust air conduit 44. In an example, the temperature 408 data may be received from the temperature sensor 50 configured for receiving information about the heat level of the exhaust air flow. In some examples, additional or alternate temperature data may be available, such as sensor data indicative of the temperature of or in the drum 28, ambient temperature outside the clothes dryer 10,
The voltage 410 may include information or data indicative of the electric potential being provided to the clothes dryer 10 and/or to the motor 46. In some examples, electrical service may vary and the voltage 410 may be a useful metric in estimating performance, as a lower flow rate may be a result of a lower voltage 410 as opposed to a blockage in the vent. In an example, the voltage 410 may be used as a component of the inference of the motor torque along with the electric current in the power line driving the motor 46.
The additional factors 412 may include other sources of information or data that may be useful in inferring the estimated airflow 404. Examples of the additional factors 412 may include, as some possibilities: tumble pattern of the selected cycle, gas pressure, gas type (natural gas, propane, etc.), etc.
For instance, the additional factors 412 may include information or data that may allow the airflow model 402 to account for conditions specific to a particular installation or customer. In an example, the additional factors 412 may include information such as environmental noises or conditions that are encountered by the clothes dryer 10. These conditions may include customer-specific load sizes, power voltages, clothes dryer 10 ambient temperature, high limit stat trips, etc. As some other examples, these customer-specific conditions may include other factors discussed herein, such as ambient temperature, relative humidity, gas pressure, gas type, machine age 414, etc.
In another example, also to increase the accuracy of the estimated airflow 404, machine learning over time may be used to compensate for customer load size, cycle selection, and general habits as well as installation-specific environmental conditions such as temp, relative humidity, voltage, etc. For instance, the airflow model 402 may be tuned to a specific customer environment. In another example, a customer-specific model may be used in addition to the airflow model 402 to adjust the estimated airflow 404 to account for customer-specific conditions.
In another example, the additional factors 412 may include historical information or data that may allow the airflow model 402 to help estimate airflow and changes in the airflow over time. This historical information may include sensor data for runs or cycles of the clothes dryer 10 that is saved to the memory 70, for example, for retrieval in later cycles.
For instance, the airflow model 402 may be a recurrent neural network trained to analyze sequential data. In such an example, the airflow model 402 may receive inputs for the current run (i.e. present or in progress run) as well as additional historical data from prior runs, cycles, or other prior operation(s) of the clothes dryer 10 (e.g., retrieved from the memory 70). By accounting for temporal information, the airflow model 402 may be able to provide an estimated airflow 404 that accounts for changes in the clothes dryer 10 over time.
The machine age 414 may include information or data indicative of the age or wear level of the clothes dryer 10. In an example, the machine age 414 may be indicated as an amount of time since manufacture or install of the clothes dryer 10. In another example, the machine age 414 may be indicated as a quantity of machine cycles performed by the clothes dryer 10. The machine age 414 may be used for compensating and adjusting the estimated airflow 404 to account for factors such as degradation of components of the clothes dryer 10, such as the seals, belts, and/or heater 42.
The airflow model 402 may be any of various types of machine-learning models that are trained on data having ground truth information. For instance, the airflow model 402 may be trained on a dataset of load mass 406, temperature 408, voltage 410, additional factors 412, and/or machine age 414 with actual measured airflows. The airflow model 402 may utilize various techniques, such as linear regression, polynomial regression, decision trees, random forests, and neural networks, as some non-limiting examples. The airflow model 402 may then be used to infer results based on inputs at runtime. For example, the airflow model 402 may receive the load mass 406, temperature 408, voltage 410, additional factors 412, and/or machine age 414 inputs at runtime to infer the estimated airflow 404.
FIG. 4B illustrates an alternate example 400B of an airflow model 402 for use in determining an estimated airflow 404. It should be noted that, as compared to the example 400A in which the machine age 414 is an input to the airflow model 402, in the example 400B the machine age 414 may be used in a separate aspect to perform the correction of the estimated airflows 404 with respect to the machine age 414.
In the example 400B, a first stage may utilize the airflow model 402 to determine an unaged estimated airflow 416. This unaged estimated airflow 416 may then be an input to an aging model 418. This aging model 418 may be a trained machine learning model or a function defined to adjust the unaged estimated airflow 416 output of the airflow model 402 into the estimated airflow 404 based on the machine age 414. This aging model 418 may be trained based on data of different machine ages 414, where this training may be used to infer the amount of adjustment to make to the estimated airflow 404 to account for the machine age 414, e.g., as the clothes dryers 10 is used for more time and/or is used for more cycles.
FIG. 5A illustrates an example process 500A for operation of a calibration mode for the clothes dryer 10. In an example, the process 500A may be performed by the controller 14 of the clothes dryer 10 responsive to installation of the clothes dryer 10.
At operation 502, the clothes dryer 10 performs a baseline calibration. The baseline calibration may include running the clothes dryer 10 within defined conditions in order to establish baseline signals from various sensors 50, 52 of the clothes dryer 10. This baseline may be used to signify optimal airflow conditions. These sensors 50, 52 may include, for instance, sensors that measure the temperature 408 in the exhaust air conduits 44, the supply air flow temperature, the temperature in the drum 28, moisture in the drum 28, air flow rate in the supply air conduit 38, air flow rate in the exhaust air conduit 44, weight in the drum 28, torque of the motor 46, etc. Data from the sensors 50, 52 may be stored to the memory 70 of the clothes dryer 10 for use in later comparisons. As discussed with respect to FIGS. 4A and 4B, the sensor data from the baseline calibration (i.e. baseline sensor data) may be input to the airflow model 402 to compute a baseline estimated airflow 404 indicative of proper operation of the clothes dryer 10.
This calibration may be performed according to calibration parameters 504. For instance, the calibration parameters 504 may include aspects such as: no load being located within the drum 28, the vent screen being clean, the vent being clean, etc. In some examples, the calibration parameters 504 may indicate for the clothes dryers 10 to be connected to the vent. In other examples, the calibration parameters 504 may indicate for the clothes dryer 10 to be disconnected from the vent. In yet further examples, the calibration parameters 504 may specify for the clothes dryer 10 to perform a cold run with the clothes dryer 10 at ambient temperature. Or, the calibration parameters 504 may specify for the clothes dryer 10 to perform a hot run having just been used.
In yet further examples, the calibration parameters 504 may specify for multiple calibrations to be run. For instance, a first calibration may be run or performed with the clothes dryer 10 connected to the vent, and a second calibration may be run or performed with the clothes dryer 10 disconnected from the vent. As another possibility, a first calibration may be a cold run, while a second calibration may be a hot run.
At operation 506, the clothes dryer 10 determines whether a further calibration is triggered. In an example, the clothes dryer 10 may perform a calibration before each drying cycle. In another example, the clothes dryer 10 may determine to run a further calibration responsive to a predefined period of time having passed since the previous calibration (or the baseline calibration) was performed. This time period may be daily, weekly, or monthly, as some examples. In another possibility, a calibration may be triggered response to selection of a calibration mode from the user interface 16. As yet another possibility, a calibration may be triggered responsive to user selection of a calibration cycle from a mobile device in wireless communication with the clothes dryer 10. In an even further possibility, a calibration may be triggered responsive to occurrence of a fault code or other self-diagnostic of the clothes dryer 10.
At operation 508, the clothes dryer 10 performs a current calibration (i.e. present or instant calibration). This current calibration may be performed as discussed with respect to operation 502. The current calibration may also be performed in accordance with one or more aspects of the calibration parameters 504, as noted above. This may allow for the current calibration to be performed in controlled and consistent circumstances with respect to the baseline calibration, thereby making a comparison of the current calibration to the baseline calibration more accurate.
At operation 510, the clothes dryer 10 compares the current calibration performed at operation 508 with the baseline calibration performed at operation 502. In an example, the clothes dryer 10 may retrieve the baseline calibration from the memory 70 and may compare the sensor information captured at operation 508 with that captured at operation 502. For example, and again as discussed with respect to FIGS. 4A and 4B, the sensor data from the current calibration (i.e. present or instant sensor data) may be input to the airflow model 402 used to compute a current estimated airflow 404 (i.e. present or instant estimated airflow). The current estimated airflow 404 may then be compared to the baseline estimated airflow 404. For instance, the comparison may ensure that estimated airflow 404 meets at least a threshold airflow level relative to the baseline airflow. By using a threshold relative to the baseline (as opposed to a threshold for all clothes dryers 10), a more accurate determination of whether an issue has occurred relative to installation may be performed.
If changes are indicated in the sensor data, such as if the temperature of air in the drum 28 is significantly higher than that of the baseline and/or if the fan 40 is utilizing greater power to move the airflow, then that may indicate that there is an obstruction of the exhaust air conduit 44.
In another example, current sensor data (i.e. present or instant sensor data) may be compared to threshold values indicative of obstructions or other conditions. In some examples, the comparison may include an adjustment for starting temperature. Thus, the estimated airflow 404 may not only be compared to proper operation of the clothes dryers 10, but may also be compared to predefined error conditions that may be alerted to the customer.
In some examples, the current calibration may be performed after a temperature-based pre-cooldown mode to ensure a consistent temperature for performance of the calibration. For instance, the clothes dryer 10 may be run and then allowed to cool until the temperature sensors 50 read a predefined temperature to begin the calibration procedure. This could also be done for the baseline calibration to ensure consistency between the baseline and the later current calibrations. In some examples, the clothes dryers 10 may be configured to compensate for other factors as well, such as voltage, ambient temp, unit initial conditions, machine age 414, etc. In yet a further example, the airflow model 402 may be used to determine whether the current calibration sensor data (i.e. present or instant calibration sensor data) is indicative of an issue, such as an airflow reduction or blockage issue, for example.
At operation 512, the clothes dryer 10 determines if the comparison at operation 510 resulted in an alert. If not, the process 500 ends. If, however, an alert is indicated, control passes to operation 514.
At operation 514, the clothes dryer 10 provides the alert. In an example, the alert may be indicated in the user interface 16 of the clothes dryer 10. In another example, the alert may be sent to a mobile device of a user of the clothes dryer 10. After operation 514, the process 500 ends.
FIG. 5B illustrates an alternate example process 500B for operation of a calibration mode for the clothes dryer 10 utilizing a rolling average of estimated airflow 404. As with the process 500A, the process 500B may also be performed by the controller 14 of the clothes dryer 10 responsive to installation of the clothes dryer 10.
In the alternate example process 500B, the estimated airflow 404 may be a rolling average over multiple cycles. As shown more specifically, in the process 500B, after operation 508, control passes to operation 516 at which the estimated airflow 404 from operation 508 is averaged with previously computed estimated airflows 404 from previous cycles. In an example, an average may be taken of the previous N runs or cycles of the clothes dryer 10, where N may be a predefined number of runs such as 1, 2, 3, 4, 5, 7, 8, 9, 10, 15, 20, etc. In some examples, the average may weight the runs equally. In other examples, the average may weight more recent runs with greater weight than older runs, to allow the average to prefer more recent data. For instance, a forgetting factor may be implemented to provide exponentially lower weight to older runs. As another variation, the average may be taken of runs that have similar calibration parameters 504 to one another as well as to the baseline being used for comparison.
At operation 518, this rolling average is compared to the baseline calibration, instead of the estimated airflow 404 for a single cycle as shown in the process 500A.
This rolling average may be used because factors such as load size, tumble pattern, ambient temp, voltage, gas pressure, gas type, gas heating value, etc. may cause the airflow model 402 to produce noisy results when inferring the estimated airflow 404. By averaging the estimated airflow 404 over a plurality of cycles, the noise level of the estimation may be reduced. because the alert may be raised responsive to the average of the estimated airflow 404 over multiple cycles falling below the predefined low airflow threshold, not merely due to a single estimated airflow 404 being low.
While exemplary embodiments are described above, it is not intended that these embodiments describe all possible forms of the disclosure. Rather, the words used in the specification are words of description rather than limitation, and it is understood that various changes may be made without departing from the spirit and scope of the disclosure. Additionally, the features of various implementing embodiments may be combined to form further embodiments of the disclosure.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Claims

What is claimed is:

1. A method for detection of airflow conditions in a dryer laundry appliance, comprising:

performing a current calibration utilizing an airflow model to infer, based on current sensor data from sensors of the dryer laundry appliance, an estimated airflow for an exhaust air conduit of the dryer laundry appliance;

comparing the estimated airflow to a baseline airflow previously inferred by the airflow model during a baseline calibration using previous sensor data from the sensors of the dryer laundry appliance; and

providing an alert responsive to the estimated airflow being below a threshold level relative to the baseline airflow.

2. The method of claim 1, further comprising:

responsive to installation of the dryer laundry appliance, performing the baseline calibration utilizing the airflow model to infer, based on baseline sensor data from the sensors of the dryer laundry appliance, the baseline airflow for the exhaust air conduit of the dryer laundry appliance.

3. The method of claim 1, wherein the exhaust air conduit of the dryer laundry appliance is connected to a vent, and the alert indicates that the vent is blocked.

4. The method of claim 1, further comprising determining the estimated airflow responsive to initiation of a drying cycle.

5. The method of claim 1, further comprising determining the estimated airflow responsive to a predefined period of time having passed since a previous estimated airflow or the baseline airflow was inferred.

6. The method of claim 1, further comprising determining the estimated airflow responsive to selection of a calibration mode from a user interface of the dryer laundry appliance.

7. The method of claim 1, further comprising determining the estimated airflow responsive to selection of a calibration mode from a user interface of a mobile device in wireless communication with the dryer laundry appliance.

8. The method of claim 1, further comprising determining the estimated airflow responsive to occurrence of a fault code and/or a self-diagnostic of the dryer laundry appliance.

9. The method of claim 1, wherein the baseline airflow is determined using calibration parameters defining one or more chosen from an ambient temperature, a connection or disconnection of the exhaust air conduit to a vent, or whether the dryer laundry appliance has recently been run and is in a hot state or has cooled and is in a cooled state.

10. The method of claim 1, wherein the current sensor data is indicative of one or more chosen from a load mass in a drum of the dryer laundry appliance, a temperature of air in the exhaust air conduit, or a voltage used to operate electrical components of the dryer laundry appliance.

11. The method of claim 1, wherein the current sensor data comprises machine age data indicative of age or wear level of the dryer laundry appliance, and the machine age data is provided as an input to the airflow model to account for the age or wear level of the dryer laundry appliance.

12. The method of claim 1, wherein the current sensor data comprises machine age data indicative of age or wear level of the dryer laundry appliance, and said method further comprising:

utilizing the airflow model to compute an unaged airflow using the current sensor data; and

utilizing an aging model to adjust the unaged airflow into the estimated airflow based on the machine age data to account for the age or wear level of the dryer laundry appliance.

13. The method of claim 1, further comprising:

computing a rolling average of the estimated airflow over time; and

providing the alert responsive to the rolling average of the estimated airflow being below the threshold level indicated by the baseline airflow.

14. The method of claim 1, further comprising:

performing a first calibration with the exhaust air conduit of the dryer laundry appliance connected to a vent;

performing a second calibration with the dryer laundry appliance disconnected from the vent;

comparing the estimated airflow to the first calibration and to the second calibration; and

providing, in the alert, that the dryer laundry appliance has disconnected from the vent responsive to the estimated airflow matching the second calibration and not the first calibration.

15. The method of claim 1, further comprising:

performing a first calibration as a cold run with the dryer laundry appliance at ambient temperature;

performing a second calibration as a hot run with the dryer laundry appliance warm from a previous cycle;

utilizing the first calibration for the comparing of the estimated airflow to the baseline airflow responsive to the current sensor data indicative of the estimated airflow as being computed for a subsequent cold run; and

utilizing the second calibration for the comparing of the estimated airflow to the baseline airflow responsive to the current sensor data indicative of the estimated airflow as being computed for a subsequent hot run.

16. The method of claim 1, further comprising:

performing the baseline calibration responsive to completion of a temperature-based pre-cooldown mode; and

performing the current calibration also responsive to completion of the temperature-based pre-cooldown mode, thereby ensuring a consistent temperature for performance of the baseline calibration and the current calibration.

17. The method of claim 1, wherein the current sensor data comprises historical information to enable the model to estimate changes in the estimated airflow over time.

18. The method of claim 17, wherein the airflow model is a recurrent neural network trained to analyze sequential data; and inputs to the airflow model comprise the historical information in addition to the current sensor data.

19. The method of claim 1, wherein the current sensor data comprises data indicative of a voltage powering the dryer laundry appliance, ambient temperature surrounding the dryer laundry appliance, initial conditions of the dryer laundry appliance, and/or a machine age of the dryer laundry appliance.

20. The method of claim 1, wherein the current sensor data comprises data indicative of a load size in a drum of the dryer laundry appliance, a tumble pattern used by the dryer laundry appliance to perform a selected cycle, a gas pressure of gas powering the dryer laundry appliance, a gas type powering the dryer laundry appliance, and/or a status of a gas heating value connecting the gas to the dryer laundry appliance.