Application of machine learning to process high-frequency sensor signals of a turbine engine

A method, comprising:

• operating an engine having a compressor element and a control system, the control system comprising one or more computing devices having one or more memory devices and one or more processing devices, the one or more memory devices storing computer-readable instructions that can be executed by the one or more processing devices to perform the method;
• receiving, by the one or more computing devices, data indicative of an operating characteristic associated with the compressor element of the engine;
• splicing, by the one or more computing devices, the received data into data subsets indicative of the operating characteristic associated with the compressor element, each data subset of the data subsets includes a time series of data having a plurality of data points between an initial time and an end time, the plurality of data points of each data subset having a unique initial time;
• and
• determining, by a machine-learned model of the one or more computing devices, a stall margin remaining of the compressor element based at least in part on the data subsets.

A non-transitory computer-readable media comprising computer-executable instructions, which, when executed by one or more processing devices of a control system for an engine, cause the one or more processing devices to:

• receive data indicative of an operating characteristic associated with a compressor element of the engine;
• splice the received data into data subsets indicative of the operating characteristic of the compressor element, each data subset of the data subsets includes a time series of data having a plurality of data points between an initial time and an end time, the plurality of data points of each data subset having a unique initial time;
• and
• determine, by executing a machine-learned model, a stall margin remaining of the compressor element based at least in part on the data subsets.