Abstract
Electromagnetic wave of electrical spark is a potential cause to eletrical equipment failure. This research focused on identificating and comparative analyzing the different types of electromagnetic waveform generated by eletrical equipment failure based on BP neural networks. After analyzing and extracting the features the electromagnetic waveform, BP neural network was chosen to build and train model because of fewer actual samples. The collected standard electromagnetic waveforms were used as the input of the train model, so that the nonlinear mapping between the input electromagnetic wave characteristics and the output electromagnetic wave types of the network can be maximally simulated. The model accuracy was improved by adjusting training parameters after analyzing the results. Then adjusted models were used to identificate the types of electromagnetic waveforms. The result shows that the identification of electrical spark electromagnetic waveform based on BP neural network is effective and feasible.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
