Research on Noise Classification of Industrial Furnace Burner Based on Improved Generative Adversarial Network
In the process of classifying industrial furnace burner noise,it is prone to interference from issues such as uneven samples,nonlin-earity,and working environment,making it difficult to separate noise signals,resulting in poor classification performance.In order to solve the above problems,a noise classification method of industrial furnace burner based on improved Generative adversarial network is proposed.A combination sensor is used to collect signals from industrial furnace burners,and noise signals are separated through the acoustic energy super-position algorithm.The Wavelet packet decomposition algorithm is used to extract the noise signal features,and the extracted features are input into the improved Generative adversarial network.The improved Generative adversarial network completes the noise classification of industrial furnace burner through the classification function.The experimental results show that the proposed method has good feature extraction perform-ance,high classification accuracy,short classification time,and reliable classification results for industrial furnace burner noise signals.
improving the generative adversarial networkindustrial furnace burnernoise classificationacoustic energy superposition algo-rithmwavelet packet decomposition algorithm
万方数据
维普
