	US 12,333,222 B2
	Scalable system and engine for forecasting wind turbine failure
Yajuan Wang, White Plains, NY (US); Gabor Solymosi, Solymar (HU); Ede Szarka, Budapest (HU); and Younghun Kim, Pleasantville, NY (US)
Assigned to Utopus Insights, Inc., Valhalla, NY (US)
Filed by Utopus Insights, Inc., Valhalla, NY (US)
Filed on Sep. 11, 2023, as Appl. No. 18/465,096.
Application 18/465,096 is a continuation of application No. 17/209,695, filed on Mar. 23, 2021, granted, now 11,803,676.
Application 17/209,695 is a continuation of application No. 16/234,455, filed on Dec. 27, 2018, granted, now 10,956,632, issued on Mar. 23, 2021.
Prior Publication US 2023/0418998 A1, Dec. 28, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 30/20 (2020.01); F03D 17/00 (2016.01); G06N 20/00 (2019.01); H02J 3/38 (2006.01)

CPC G06F 30/20 (2020.01) [F03D 17/00 (2016.05); G06N 20/00 (2019.01); H02J 3/38 (2013.01); H02J 2203/20 (2020.01)]

19 Claims

10. A component failure prediction system, comprising:

at least one processor; and

memory containing instructions, the instructions being executable by the at least one processor to:

receive log data from one or more Supervisory Control and Control and Data Acquisition (SCADA) systems that monitor any number of wind turbines, the log data being generated during a first period of time;

receive historical wind turbine component failure data and wind turbine asset data from the one or more SCADA systems, the historical wind turbine component failure data and wine turbine asset data being generated during the first period of time;

create cohort instances based on the wind turbine failure data and wind turbine asset data, each cohort representing a subset of the wind turbines, the subset of the wind turbines including a same type of controller and a similar geographical location, the geographical location of the wind turbines of the subset of wind turbines being within the wind turbine asset data;

generate a feature matrix, the feature matrix including a unique feature identifier for each feature of the log data in the feature matrix;

extracting patterns of events from the feature matrix based on the cohort instances;

receive first historical sensor data of the first time period, the first historical sensor data including sensor data from one or more sensors of one or more components of renewable energy assets, the first historical sensor data indicating at least one first failure associated with the one or more components of the renewable energy asset during the first time period;

generate a first set of failure prediction models using the first historical sensor data and the patterns of events, each of the first set of failure prediction models being trained using different amounts of first historical sensor data based on different observation time windows and different lead time windows, each observation time window including a time period during which first historical data is generated, each lead time window including a period of time before a predicted failure;

select a first selected failure prediction model from the first set of failure prediction models based on the observation time windows and lead time windows, the first selected failure prediction model including the lead time window;

receive first current sensor data of a second time period, the first current sensor data including sensor data from the one or more sensors of the one or more components of the renewable energy asset;

apply the first selected failure prediction model to the first current sensor data to generate a first failure prediction on a failure of at least one component of the one or more components;

compare the first failure prediction to a trigger criteria; and

generate and transmit a first alert based on the comparison of the failure prediction to the trigger criteria, the alert indicating the at least one component of the one or more components and information regarding the failure prediction.