Gasket leak detection in a refrigerator appliance
A refrigerator appliance is provided including a cabinet defining a chilled chamber, a door rotatably hinged to the cabinet to provide selective access to the chilled chamber, and a light sensing assembly positioned within the chilled chamber. A controller is configured to determine that the door is closed, identify light in the chilled chamber using the light sensing assembly, and provide a user notification in response to identifying the light in the chilled chamber when the door is closed.
1. A refrigerator appliance comprising:
a cabinet defining a chilled chamber;
a door being rotatably mounted to the cabinet to provide selective access to the chilled chamber, the door comprising a gasket;
a light sensing assembly positioned within the chilled chamber; and
a controller operably coupled to the light sensing assembly, the controller being configured to:
determine that the door is closed;
identify light coming into the chilled chamber through or around the gasket using the light sensing assembly; and
provide a user notification in response to identifying the light coming into the chilled chamber when the door is closed.
2. The refrigerator appliance of claim 1 , wherein the light sensing assembly is a light sensor or an optical sensor.
3. The refrigerator appliance of claim 1 , wherein the light sensing assembly is a camera assembly, and wherein identifying the light in the chilled chamber comprises:
obtaining one or more images using the camera assembly; and
identifying the light by analyzing the one or more images.
4. The refrigerator appliance of claim 3 , wherein identifying the light by analyzing the one or more images comprises identifying a pixel brightness of the one or more images.
5. The refrigerator appliance of claim 3 , wherein identifying the light by analyzing the one or more images comprises using a machine learning image recognition process.
6. The refrigerator appliance of claim 5 , wherein the machine learning image recognition process comprises at least one of a convolutional neural network (“CNN”), a region-based convolutional neural network (“R-CNN”), a deep belief network (“DBN”), or a deep neural network (“DNN”) image recognition process.
7. The refrigerator appliance of claim 1 , wherein the controller is further configured to:
provide the user notification including an image showing the location of the light.
8. The refrigerator appliance of claim 1 , wherein the controller is in operative communication with a remote device and is configured for transmitting the user notification when the light is identified and the door is closed.
9. The refrigerator appliance of claim 1 , wherein the camera assembly comprises:
a camera mounted at a top of the chilled chamber.
10. The refrigerator appliance of claim 1 , wherein the controller is further configured to:
sound an alarm when the light is detected and the door is closed.
11. The refrigerator appliance of claim 1 , further comprising:
a door sensor operably coupled to the door of the refrigerator appliance, wherein the controller is in operative communication with the door sensor and is configured to identify the light when the door is closed.
12. A method of operating a refrigerator appliance, the refrigerator appliance comprising a chilled chamber, a door providing selective access to the chilled chamber and comprising a gasket, and a light sensing assembly positioned within the chilled chamber, the method comprising:
determining that the door is closed;
identifying light coming into the chilled chamber through or around the gasket using the light sensing assembly; and
providing a user notification in response to identifying the light coming into the chilled chamber when the door is closed.
13. The method of claim 12 , wherein the light sensing assembly is a light sensor or an optical sensor.
14. The method of claim 12 , wherein the light sensing assembly is a camera assembly, and wherein identifying the light in the chilled chamber comprises:
obtaining one or more images using the camera assembly; and
identifying the light by analyzing the one or more images.
15. The method of claim 14 , wherein identifying the light by analyzing the one or more images comprises identifying a pixel brightness of the one or more images.
16. The method of claim 14 , wherein identifying the light by analyzing the one or more images comprises using a machine learning image recognition process.
17. The method of claim 16 , wherein the machine learning image recognition process comprises at least one of a convolutional neural network (“CNN”), a region-based convolutional neural network (“R-CNN”), a deep belief network (“DBN”), or a deep neural network (“DNN”) image recognition process.
18. The method of claim 12 , further comprising:
providing the user notification including an image showing the location of the light.
19. The method of claim 12 , wherein the controller is in operative communication with a remote device and is configured for transmitting the user notification when the light is identified and the door is closed.
20. The method of claim 12 , further comprising:
sounding an alarm when the light is detected and the door is closed.