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US 12,461,087 B2Granted
Fluid inspection using machine learning
AssigneeNVIDIA Corporation
InventorsRyan Kelsey Albright, William Andrew Mecham, Tahir Cader, Michael Scott Thompson, Aaron Richard Carkin, William Ryan Weese, Benjamin Joseph Goska, Marc Davis
Life of this patent
Estimating the term…
Abstract
A method includes determining, using a processing device, a set of observations from coolant data, the coolant data being received from one or more sensors in an environment associated with a coolant. The method further includes determining, using a machine learning model and the set of observations, a contamination level of the coolant. The method also includes initiating an operation, using the processing device, responsive to determining the coolant contamination level.
Claims16
A method, comprising:
- determining, using a processing device, a set of observations from coolant data received from one or more sensors in an environment associated with a coolant in a datacenter cooling system, the set of observations comprising at least one of:
- a fluid turbidity measurement, a pressure measurement, a conductivity measurement, or a potential hydrogen (pH) level measurement;
- determining, using the processing device, performance data including at least one of power consumption measurements, temperature measurements, or clock frequency measurements of one or more computing devices;
- processing the set of observations with the performance data using a machine learning model that determines whether the set of observations matches a contaminated coolant profile or an uncontaminated coolant profile and outputs a contamination level of the coolant based on a result of the processing;
- and
- initiating predictive maintenance of the datacenter cooling system, using the processing device, responsive to determining the coolant contamination level and that the coolant data matches a contaminated coolant profile.
3 more elements
A system comprising:
- a processing device to:
- determine a set of observations from coolant data being received from one or more sensors in a computing environment associated with a coolant in a datacenter cooling system, the set of observations comprising at least one of:
- a fluid turbidity measurement, a pressure measurement, a conductivity measurement, or a potential hydrogen (pH) level measurement;
- determine performance data including at least one of power consumption measurements, temperature measurements, or clock frequency measurements of one or more computing devices;
- process the set of observations with the performance data using a machine learning model that determines whether the set of observations matches a contaminated coolant profile or an uncontaminated coolant profile and outputs a contamination level of the coolant based on a result of processing the set of observations;
- and
- initiate predictive maintenance of the datacenter cooling system, using the processing device, responsive to determining the coolant contamination level and that the coolant data matches a contaminated coolant profile.
4 more elements
A method, comprising:
- determining, using a processing device, a set of observations from coolant data received from one or more sensors associated with a first portion of a computing environment associated with a coolant in a datacenter cooling system, the set of observations comprising at least one of:
- a fluid turbidity measurement, a pressure measurement, a conductivity measurement, or a potential Hydrogen (pH) level measurement;
- determining, using the processing device, performance data including at least one of power consumption measurements, temperature measurements, or clock frequency measurements of one or more computing devices;
- training, using the set of observations and the performance data, a machine learning (ML) model to determine a cooling efficiency and power efficiency associated with the first portion;
- determining, using the ML model, the cooling efficiency and power efficiency associated with a second portion of the computing environment;
- and
- determining whether to initiate predictive maintenance of the datacenter cooling system at the first portion or the second portion responsive to determining the cooling efficiency and power efficiency associated with the second portion.
4 more elements
Dependencies are inferred from the claim text.