US 12,242,260 B2
Machine learning based equipment failure prediction
Gurunath Venkatarama Subrahmanya Gandikota, Bangalore (IN); Shashwat Verma, Bengaluru (IN); Geetha Gopakumar Nair, Katy, TX (US); Pradyumna Singh Rathore, Bangalore (IN); Janvi Nayan Acharya, Mumbai (IN); and Richa Choudhary, Samastipur (IN)
Assigned to Halliburton Energy Services, Inc., Houston, TX (US)
Filed by Halliburton Energy Services, Inc., Houston, TX (US)
Filed on Oct. 1, 2021, as Appl. No. 17/449,728.
Prior Publication US 2023/0107580 A1, Apr. 6, 2023
Int. Cl. G05B 23/02 (2006.01); G06N 5/04 (2023.01)
CPC G05B 23/0283 (2013.01) [G06N 5/04 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
receiving a time series of data values for a time window of each operational parameter of a number of operational parameters of equipment;
calculating a time derivative feature that comprises a change of the data values of a first operational parameter of the number of operational parameters over the time window;
encoding the time derivative feature of the first operational parameter to generate a time derivative encoded value based on a rate of change of the time derivative feature over some period of time during the time window relative to a set of threshold values;
training a machine learning model using the time derivative encoded value to learn failure prediction patterns for the equipment; and
classifying, using the machine learning model and following the training of the machine learning model, an operational mode of the equipment in real time based at least in part on the time derivative encoded value.