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To solve the problem analyzing the effect of complex faults and the complex characteristics of the equipment data, to predict the fault in real time, this paper presents a scheme of fault prediction based on deep learning. On-line dynamic fault prediction is carried out on the basis of mass data obtained from equipment condition monitoring and test verification by methods of fault model training, fault feature recognition, fault evolution. Based on the attribute information of software quality, software static fault can be predicted. Fault prediction system can be developed based on TensorFlow to improve the fault prediction ability of equipment.