1. A computer implemented method of monitoring operation-of equipment and predicting equipment failure in an industrial plant, the method comprising: in a computer system having one or more processors: receiving, through a user interface, user input identifying a first target set of equipment including a first plurality of units of equipment, wherein the first plurality of units of equipment is configured with one or more monitoring sensors, and wherein each equipment unit of the first plurality of units of equipment has a first plurality of matching target parameters; receiving a set of time series waveforms from sensors associated with the first plurality of units of equipment, the time series waveforms including sensor data values; processing the received time series waveforms to generate a plurality of derived inputs wherein the derived inputs and the sensor data values collectively compose sensor data; training a first machine learning agent to: (i) discriminate between first normal baseline data for the first target set of equipment and first failure signature information for the first target set of equipment; and (ii) detect operational conditions associated with onset of an equipment failure by identifying patterns or relationships within the sensor data; wherein the first normal baseline data of the first target set of equipment is derived from a first portion of the sensor data associated with operation of the first plurality of units of equipment in a first normal mode, and wherein the first failure signature information is derived from a second portion of the sensor data associated with operation of the first plurality of units of equipment in a first failure mode; receiving monitored sensor signals produced by the one or more monitoring sensors; analyzing, by the trained first machine learning agent, data included within the received monitored sensor signals, the analyzing detecting, from the patterns or relationships within the data, an operational condition identifying an equipment failure onset of one or more monitored units of equipment, such that the analyzing by the first machine learning agent results in predicting failure of the one or more monitored units of equipment; and transferring an indication of the predicted failure to a plant control system and, using the plant control system, controlling the one or more monitored units of equipment to avert the predicted failure based upon the transferred indication.