DEVELOP A FAMILY OF DATA-DRIVEN MACHINE LEARNING MODULES THAT PROVIDE REAL-TIME DETECTION, CLASSIFICATION, AND PREDICTION OF HETEROGENEOUS PV SYSTEM FAILURES AND THEREBY FACILITATE EFFICIENT CORRECTIVE AND PREVENTIVE MAINTENANCE OF PV SYSTEMS. THE PROJECT WILL OFFER PV SYSTEM FAILURE PREDICTION CAPABILITIES WITH GREATLY ENHANCED ACCURACIES. BY PROVIDING CONSTANTLY UPDATED INFORMATION ON UNDERLYING COMPONENT FAILURES & DAMAGES AND FAILURE RISK LEVELS OF THE PV SYSTEMS, THE SOLUTIONS WILL ENABLE PV ASSET MANAGERS TO A) BE AWARE OF HIDDEN PROBLEMS BY DETECTING INCIPIENT FAILURES SO THAT LOSS OF ENERGY ARE MINIMIZED, B) REDUCE REDUNDANT AND COSTLY MAINTENANCE ACTIONS DUE TO FALSE ALARMS OR OVER-CONSERVATIVENESS, AND C) BETTER OPTIMIZE PREVENTIVE MAINTENANCE SCHEDULING AND IMPROVE MAINTENANCE EFFICACY.