A Method for Recognizing Switchgear Faults Based on Parallel Long-Short-Term Memory Neural Network
A long-short-term memory and recognition switch technology, which is applied to pattern recognition in signals, character and pattern recognition, instruments, etc., can solve the problems of single signal acquisition, unsatisfactory diagnosis results, and low efficiency of recognition models, and achieve high recognition ability , broaden the channels of information collection, and comprehensively and accurately reflect the effect of
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[0051] The present invention will be further described below in conjunction with the accompanying drawings.
[0052] The invention extracts new characteristic ZMSF parameters (1-10) order from the audible sound signal, combines with other characteristic types and calculates the corresponding statistical function to ensure that the obtained data is highly consistent with the collected signal.
[0053] In the invention, a parallel mutual-feeding long-short-term memory (LSTM) neural network model is used as the core algorithm for training and recognition. The long-short-term memory neural network model of the parallel mutual-feedback structure can recognize or monitor audible sound signals and ultrasonic signals at the same time, and the calculation results are fed back to each other, and the calculation results of one side are terminated or strengthened by the way of mutual feedback control. In this way, computing resources are saved and the recognition effect is improved.
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