A novel adaptive noise reduction method for field natural gas pipeline defect detection signals. (October 2022) - Wu, Linyu; Liang, Wei; Sha, Duolin
Abstract: To suppress noise interference and improve defect identification accuracy, we propose a transfer learning-based adaptive wavelet neural network (AWNN) method for pipeline defect detection signal denoising. Simulated noisy signals and field detection signals are used to determine the network's initial structure and fine-tune its parameters. The Meyer wavelet basis function is chosen as an excitation function based on the defect signal waveform. The pretrained structure is determined by a search algorithm. The network parameters are adaptively fine-tuned according to the sample entropy of the noise. The new genetic beetle antennae search strategy (GBA) optimization algorithm is proposed to improve the AWNN to avoid local optima. Compared with traditional noise reduction methods, the AWNN has excellent ability to recovery the pure denoised signal with a higher SNR and smaller RMSE . The AWNN can realize adaptive noise reduction of different field signals, providing reliable input for subsequent defect diagnosis. Highlights: A transfer learning-based adaptive wavelet neural network denoising method is proposed. Search algorithm is applied to network structure initialization. The sample entropy of noise is used to fine-tune the network. An optimization algorithm combining global and local optimization is proposed for network optimization. The proposed method has been successfully applied to the noise reduction of field signals.