小波包能量谱和BP神经网络在波纹管压浆超声检测中的应用

针对小波分析在信号处理的局限性,将小波包分析和反向传播(Back Propagation,BP)神经网络相结合,提出一种基于小波包能量谱和BP神经网络的波纹管压浆超声检测方法。采用超声检测方法接收波纹管模型的回波信号,以小波包分解后各子频带的能量作为检测特征,当波纹管内部出现脱落时,检测特征会发生变化,最后将特征输入BP神经网络中进行分类识别。试验结果表明,该方法能够理想地实现波纹管内部缺陷的诊断,可为波纹管超声检测提供一定的技术支持。

Application of wavelet packet energy spectrum and BP neural network to ultrasonic detection of slurry in bellows

Considered the limitation of wavelet analysis in signal processing, a wavelet analysis method based on wavelet packet energy spectrum and Back Propagation (BP) neural network is proposed to detect the slurry quality in bellows. Ultrasonic detection method is adopted to receive the echo signal of the bellows model, and the energy in every sub-frequency band after the wavelet packet decomposition is taken as the detection feature. When the concrete slurry inside the bellows falls off, the detection features change. Finally, the features are input into the BP neural network for classification and identification. The experimental results show that this method can be used to diagnose the internal defects of bellows and provides a technical support for the non-destructive testing of bellows.