14. A system for predicting failures in an artificial lift system, the system comprising: a processor; a memory communicatively connected to the processor and storing computer-executable instructions that, when executed by the processor, cause the system to: receive a dataset of time-sampled data from each of a plurality of artificial lift systems disposed across a plurality of different oil fields, the dataset including data from failed and normally operating artificial lift systems; receive data labels from a user, the data labels defining one or more types of failures of artificial lift systems; identify pre-failure signatures based at least in part on a moving window of operational data in the extracted features preceding a known failure included in the one or more types of failures; generate a learning model by a multi-class support vector machine based on the labeled data, the learning model including one or more identified pre-failure signatures and predict a failure of an artificial lift system based on observation of one of the identified pre-failure signatures in operational data received from the artificial lift system.
20. The system of claim 14 , wherein the dataset includes measurements of attributes, and wherein the attributes are selected from a group of attributes consisting of: card area; peak surface load; minimum surface load; strokes per minute; surface stroke length; flow line pressure; pump fillage; prior day cycles; and daily run time.