US 12,461,087 B2
Fluid inspection using machine learning
Ryan Kelsey Albright, Beaverton, OR (US); William Andrew Mecham, Elk Grove, CA (US); Tahir Cader, Spokane Valley, WA (US); Michael Scott Thompson, Wilsonville, OR (US); Aaron Richard Carkin, Hillsboro, OR (US); William Ryan Weese, Portland, OR (US); Benjamin Joseph Goska, Portland, OR (US); and Marc Davis, Hillsboro, OR (US)
Assigned to NVIDIA Corporation, Santa Clara, CA (US)
Filed by NVIDIA Corporation, Santa Clara, CA (US)
Filed on Aug. 23, 2022, as Appl. No. 17/893,834.
Prior Publication US 2024/0069005 A1, Feb. 29, 2024
Int. Cl. G01N 33/28 (2006.01); G01N 11/02 (2006.01); G01N 21/90 (2006.01); G06N 20/00 (2019.01)
CPC G01N 33/2888 (2013.01) [G01N 11/02 (2013.01); G01N 21/9072 (2013.01); G06N 20/00 (2019.01)] 16 Claims
OG exemplary drawing
 
1. 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.