Spark Discharge Identification of Electrostatic Dust Removal Based on the Back-propagation Neural Network

Spark discharge can be observed in high-voltage electrostatic dust removal, reducing the dust removal efficiency and damaging the equipment. In this study, we propose a spark identification method based on the sound because of spark discharge and use a microelectromechanical system microphone to obtainasound signal. Further, we establish a back-propagation (BP) neural network recognition system and conduct experiments using different characteristic vectorsafter analyzing the short-time energy, short-time zero-crossing rate, linear predictive cepstral coefficient, and melfrequency cepstral coefficient (MFCC) of sound. The experimental results denote that the application of MFCC based on short-time energy and short-time zero-crossing rate can improve the recognition rate, which is as high as 96% for pure samples, and that the application of instant spark discharge's two frame data as spark training samples for the BP neural network can considerably improve the robustness of the recognition systems; the recognition rate is observed to be as high as 95% in case of impure samples.