Automated analysis of non-stationary machine performance

A system for monitoring at least one machine comprising:

• a first sensor operative to acquire at least a first non-stationary signal from a servo-motor driving a component of a robot in a non-stationary manner during at least one operational time frame, said at least first sensor being operative to provide at least a first non-stationary output;
• a second sensor operative to acquire at least a second non-stationary signal from said servo-motor during said operational time frame, said at least second sensor being operative to provide at least a second non-stationary output;
• a signal processor employing a machine-learning model operative to fuse said at least first non-stationary output with said at least second non-stationary output, based on modification of said at least first non-stationary output by said at least second non-stationary output, to produce a fused output, said machine-learning model being further operative to cross-correlate said first non-stationary signal with said second non-stationary signal, to ascertain cross-correlation levels between said first non-stationary signal and said second non-stationary signal, and to grade said cross-correlation levels, based on weighting on a correlation magnitude by a rotational speed of said servo-motor;
• a feature extractor operative to extract, using said machine-learning model, at least one feature of at least one of said first and second non-stationary signals based on said fused output;
• a feature tracker operative to track, using said machine-learning model, said at least one feature over time;
• and
• a machine control module operative to adjust operational parameters of said servo-motor in order to optimize performance of said robot,
• wherein said signal processor is further operative to identify, by said machine-learning model and based on said cross-correlation levels, a plurality of intervals within said non-stationary operating regime, and
• wherein said control module is further operative to adjust operational parameters of said servo-motor in order to optimize performance of said robot by adjusting at least one of a sensor sampling frequency and a sensor sampling duration of said first and second sensors, based on said plurality of intervals.

A method for monitoring at least one machine comprising:

• causing at least a first sensor to acquire at least a first non-stationary signal from at least one machine operating in accordance with a non-stationary operating regime during at least one operational time frame, said at least first sensor providing at least a first non-stationary output;
• causing at least a second sensor to acquire at least a second non-stationary signal from said at least one machine during said operational time frame, said at least second sensor providing at least a second non-stationary output;
• fusing, by a computer-implemented machine-learning model, said at least first non-stationary output with said at least second non-stationary output, wherein said fusing comprises modifying said at least first non-stationary output based on said at least second non-stationary output, to produce a fused output, said fusing further comprising:
• ascertaining, by said computer-implemented machine-learning model, cross-correlation levels between said first non-stationary signal and said second non-stationary signal, by cross-correlating said first non-stationary signal with said second non-stationary signal, and grading said cross-correlation levels, based on weighting a correlation magnitude by a rotational speed of said at least one machine;
• and
• identifying, by said computer-implemented machine-learning model and based on said cross-correlation levels, a plurality of intervals within said non-stationary operating regime,
• extracting, by said computer-implemented machine-learning model, at least one feature of at least one of said first and second non-stationary signals based on said fused output, for at least some intervals of said plurality of intervals;
• tracking, by feature tracking algorithms of said computer-implemented machine-learning model, said at least one feature over time, for said at least some intervals of said plurality of intervals;
• and
• adjusting operational parameters of said machine to optimize said machine performance, based on said tracking,
• wherein said at least one machine comprises a servo-motor driving a component of a robot in a non-stationary manner,
• wherein said adjusting operational parameters of said machine to optimize said machine performance also comprises adjusting at least one of a sensor sampling frequency and a sensor sampling duration of said first and second sensors, based on said plurality of intervals.