1. A system comprising: a welding horn; an anvil positioned adjacent to the welding horn, wherein the welding horn is configured to clamp against the anvil and form a weld on a work piece during a repeatable vibration welding process; a plurality of sensors positioned with respect to the welding horn; a host machine and a learning machine each having a respective processor in electrical communication with the plurality of sensors; and tangible, non-transitory memory on which is recorded instructions for predicting a binary quality status of the welds formed via the vibration welding process, wherein the binary quality status includes only a passing binary class and a failing binary class, and wherein the learning machine is configured to execute the instructions via the processor to thereby: receive signals from the plurality of sensors; identify a set of candidate features as a function of the received signals; extract a plurality of features from the identified set of candidate features, each of which is more predictive of the binary quality status of the welds relative to all non-extracted features in the identified set of candidate features; map the extracted plurality of features to a dimensional space having a number of dimensions that is proportional to a number of the extracted features, wherein the dimensional space includes most of the welds from the passing binary class in the dimensional space and exclude at least 90% of the welds from the failing binary class; and record the dimensional space in the memory; wherein the host machine is configured to compare the received signals for a subsequent weld to boundaries of the recorded dimensional space during the repeatable vibration welding process to thereby predict, in real time, the binary quality status of the subsequent weld.