Embodiments of the present invention disclose a method, a computer program product, and a computer system for predictive maintenance of refrigeration cases. A computer collects a temperature time series for a refrigeration case and, based on the temperature time series, learns a refrigeration case signature for both non-frost and defrost cycles. The computer generates features based on the refrigeration case signature and compares the refrigeration case signature to real time, or observed, temperatures and features using a rule-based and/or machine learning framework. Based on determining that the real time data varies beyond a threshold from the refrigeration case signature, the computer identifies a failure symptom of the refrigeration case and diagnoses a root cause of the symptom or failure. In addition, the computer may activate an alarm and open a work order corresponding to the root cause of the symptom or failure.

The subject matter of this specification can be embodied in, among other things, a method for time shifting when a cold storage facility is cooled that includes determining a thermal model of a cold storage facility, obtaining an energy cost model that describes a schedule of variable energy costs over a predetermined period of time in the future, determining an operational schedule for at least a portion of a refrigeration system based on the thermal model, the energy cost model, and a maximum allowed temperature, and powering on the portion the refrigeration system based on the operational schedule, cooling, by the powered portion of the refrigeration system to a temperature below the maximum allowed temperature, reducing power usage of the powered portion of the refrigeration system based on the operational schedule, and permitting the facility to be warmed by ambient temperatures toward the maximum allowed temperature.

The subject matter of this specification can be embodied in, among other things, a method for time shifting when a cold storage facility is cooled that includes determining a thermal model of a cold storage facility, obtaining an energy cost model that describes a schedule of variable energy costs over a predetermined period of time in the future, determining an operational schedule for at least a portion of a refrigeration system based on the thermal model, the energy cost model, and a maximum allowed temperature, and powering on the portion the refrigeration system based on the operational schedule, cooling, by the powered portion of the refrigeration system to a temperature below the maximum allowed temperature, reducing power usage of the powered portion of the refrigeration system based on the operational schedule, and permitting the facility to be warmed by ambient temperatures toward the maximum allowed temperature.

A meat aging chamber suitable for residential or portable use is provided. An aging apparatus comprises a refrigerated aging cabinet having a climate created by environmental apparatus. A server in a cloud resource communicates with a control circuit in the aging apparatus and with a portable interactive device. In a setup mode the portable interactive device communicates with a control circuit in the aging apparatus via a communications interface to identify the user to the control circuit. In an operating mode the communications interface enables communication between the cloud resource and the control circuit. A sensor module provides current values to the cloud resource for controlling operation in accordance with a software program embodying the aging protocol. The cloud resource can inform the smartphone of aging progress and alarm conditions. Physical features enable embodying the functions of a commercial aging chamber in a residential sized unit.

A method of measuring pressure includes the steps of (1) providing a vacuum cabinet with a storage compartment and an insulating space, and three temperature sensors; (2) sensing a first temperature level of an interior wall of the storage compartment; (3) sensing an ambient temperature level within the storage compartment; (4) sensing a second temperature level of an exterior wall of the storage compartment; (5) calculating an overall heat transfer coefficient (Q) using the ambient temperature level, the first temperature level, and a convective heat transfer coefficient for the interior wall of the storage compartment; (6) calculating a temperature differential between the second and first temperature levels; (7) determining a conductivity level (K) using the temperature differential, the overall heat transfer coefficient (Q) and a thickness of the insulating space; and (8) determining a pressure level (P) within the insulating space using the conductivity level (K).

The present disclosure provides a refrigerator operation control method capable of accurately recognizing whether an abnormal situation occurs as well as determining a defrosting time using an identified rotation speed of a blowing fan.

A refrigerator is provided having a shelf assembly and a controller. The shelf assembly may include a shelf and a drive mechanism configured to move the shelf in at least a vertical direction. The refrigerator also includes an object detection system configured to detect an article placed adjacent to the shelf. The controller may be configured to determine the proximity of an article and the shelf using the object detection system, detect a shelf moving event, and move the shelf in response to the shelf moving event. The shelf moving event may be based, at least in part, on the proximity of the article and the shelf. The controller may also be configured to move the shelf in response to detecting a flow restriction above or below the shelf.

According to the present disclosure, after a load operation is performed by satisfying a start condition of the load operation, the load operation is terminated when a compartment temperature of a storage compartment satisfies a termination condition when a door is opened or when a temperature change in the storage compartment satisfies the termination condition. Accordingly, excessive power consumption during the load operation may be prevented, and thus power consumption may be improved.

Embodiments of the present invention disclose a method, a computer program product, and a computer system for predictive maintenance of refrigeration cases. A computer collects a temperature time series for a refrigeration case and, based on the temperature time series, learns a refrigeration case signature for both non-frost and defrost cycles. The computer generates features based on the refrigeration case signature and compares the refrigeration case signature to real time, or observed, temperatures and features using a rule-based and/or machine learning framework. Based on determining that the real time data varies beyond a threshold from the refrigeration case signature, the computer identifies a failure symptom of the refrigeration case and diagnoses a root cause of the symptom or failure. In addition, the computer may activate an alarm and open a work order corresponding to the root cause of the symptom or failure.

The present invention discloses a refrigerator and a humidity control method for the same. The refrigerator comprises a humidity controlling device to controllably maintain moisture and/or perform humidification. The humidity controlling device is configured to: set a target humidity value, measure an actual relative humidity value of a target space, and calculate a target water replenishing mass W based on a difference between the two values and a current temperature of the target space; measure and calculate a water replenishing time T, and adjust the humidity controlling device so as to maintain its maximum humidifying rate V.sub.max within the time T to achieve the humidification purpose; and adjust the humidity controlling device so as to maintain a humidifying rate consistent with a water vapor loss rate in the target space to achieve the moisture maintaining purpose.