Enhanced vibration monitoring using machine learning

A computer-implemented method comprising:

• accessing streams of data encoding measurements taken at one or more pieces of rotating equipment;
• accessing a database encoding past measurement records from the one or more pieces of rotating equipment;
• identifying, using a processor configured to operate a machine learning (ML) module, relevant ranges of measurement values for at least a portion of the measurements encoded by the streams of data, wherein the ML module is adapted to predict the relevant ranges of measurement values based on, at least in part, the past measurement records from the one or more pieces of rotating equipment;
• extracting, using the processor configured to operate the machine learning (ML) module, a subset of the at least a portion of the measurements, wherein the subset is identified by the ML module as more capable of distinguishing normal and abnormal operating conditions for the one or more pieces of rotating equipment than a remainder of the at least a portion of the measurements;
• and
• generating, on a display device, at least one plot based on the subset of the at least a portion of the measurements and within the relevant ranges of measurement values such that the one or more pieces of rotating equipment are monitored continuously for deviations from the normal operating conditions.