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.
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. 15. The system of claim 14 , wherein the plurality of artificial lift systems disposed across a plurality of different oil fields include one or more artificial lift systems selected from a group of systems consisting of: a gas lift; a hydraulic pumping unit; an electric submersible pump; a progressive cavity pump; and a rod pump.