Off-duty-cycle-robust machine learning for anomaly detection in assets with random down times

A non-transitory computer-readable medium that includes stored thereon computer-executable instructions that when executed by at least a processor of a computer cause the computer to:

• receive a transmission of an electronic time series signal of readings by one or more sensors configured to sense one or more physical phenomena occurring in or around an asset;
• infer ranges of asset downtime from the time series signal for the asset;
• extract an asset downtime signal from the time series signal based on the inferred ranges of asset downtime;
• determine that the asset downtime signal carries telemetry based on a variance of the asset downtime signal;
• train a first machine learning model for the asset downtime signal using segments of the extracted asset downtime signal that were determined to carry telemetry, wherein the trained first machine learning model is configured to detect anomalous activity during asset downtime and configured to automatically replace surveillance of the time series signal performed by a second machine learning model during periods of asset downtime;
• detect an indication in the time series signal of a transition to asset downtime;
• and
• in response to the indication being detected, automatically switch from surveillance of the time series signal using the second machine learning model to surveillance of the time series signal for anomalous activity using the trained first machine learning model.

A computing system comprising:

• a processor;
• a memory operably connected to the processor;
• one or more sensors operably connected to the processor and memory and configured to sense one or more physical phenomena occurring in or around an asset;
• a non-transitory computer-readable medium operably connected to the processor and memory and storing computer-executable instructions that when executed by at least the processor of the computing system cause the computing system to:
• receive a transmission of an electronic time series signal of readings by the one or more sensors;
• infer ranges of asset downtime from the time series signal for the asset;
• extract an asset downtime signal from the time series signal based on the inferred ranges of asset downtime;
• determine that the asset downtime signal carries telemetry based on a variance of the asset downtime signal;
• train a first machine learning model for the asset downtime signal using segments of the extracted asset downtime signal that were determined to carry telemetry, wherein the trained first machine learning model is configured to detect anomalous activity during asset downtime and configured to automatically replace, during periods of asset downtime surveillance of the time series signal performed by a second machine learning model;
• detect an indication in the time series signal of a transition to asset downtime;
• and
• in response to the indication being detected, automatically switch from surveillance of the time series signal using the second machine learning model to surveillance of the time series signal for anomalous activity using the trained first machine learning model.