Abstract: A method for predicting a wind turbine oil filter wear level wherein a differential pressure exists between upstream and downstream sides of the filter. The method includes extracting features from wind turbine sensor data to provide extracted data and selecting features from the extracted data that correlate with a change in the differential pressure. The method also includes estimating a filter condition by learning a filter regressive linear model that uses filter direct environment operating conditions data obtained from the extracted data. In addition, the method includes forecasting at least one operating condition scenario represented by three features obtained from the extracted data. Further, the method includes forecasting a filter wear level wherein the filter model uses the at least one forecasted operating condition scenario represented by the three features.

Abstract: A system for predicting time-to-failure of a machine includes one or more processors and a non-transitory, computer-readable storage medium in operable communication with the processors. The computer-readable storage medium contains one or more programming instructions that, when executed, cause the processors to receive or retrieve multivariate time series data observed a plurality of times, and infer a plurality of state variables from the multivariate time series data, each state variable describing an operating condition of the machine at a particular time. The instructions further cause the processor to compute an average life consumption rate by applying a life consumption rate model to the plurality of state variables and time-to-failure for the machine based on the average life consumption rate. The time-to-failure for the machine may then be reported to one or more users.