Merged reference fingerprint generation for machine-learning detection of degraded operation

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:

• generate a set of time series signals from sensor readings of a reference device while the reference device is operated through multiple individual iterations of an exercise profile, wherein the reference device operates in a known undegraded state;
• separate the set of time series signals into segments that correspond to the individual iterations of the exercise profile;
• align and merge the segments to generate a merged reference fingerprint;
• and
• train a machine learning model to detect anomalous departures from the known undegraded state based on the merged reference fingerprint.

A computing system, comprising:

• at least one processor;
• at least one memory connected to the at least one processor;
• a non-transitory computer readable medium including instructions stored thereon that when executed by at least the processor cause the computing system to:
• generate a set of time series signals from sensor readings of a reference device while the reference device is operated through multiple individual iterations of an exercise profile, wherein the reference device operates in a known undegraded state;
• separate the set of time series signals into segments that correspond to the individual iterations of the exercise profile;
• align and merge the segments to generate a merged reference fingerprint;
• and
• train a machine learning model to detect anomalous departures from the known undegraded state based on the merged reference fingerprint.