METHOD AND SYSTEM FOR ACCELERATING CONVERGENCE OF RECURRENT NEURAL NETWORK FOR MACHINE FAILURE PREDICTION

摘要

Embodiments of the invention provide a method and system for accelerating convergence of Recurrent Neural Network (RNN) for machine failure prediction. The method comprises: setting initial parameters in RNN wherein the initial parameters include an initial learning rate which is determined based on a standard deviation of a plurality of basic memory depth values identified from a machine failure sequence; training RNN based on the initial parameters and at the end of each predetermined time period, calculating current pattern error based on a vector distance between the machine failure sequence and current predicted sequence; and if the current pattern error is less than or not greater than a predetermined error threshold value, determining, by the processor, an updated learning rate based on the current pattern error, and updating weight values between input and hidden units in RNN based on the updated learning rate.