大型常压储罐底板的声发射在线检测

On-Line Acoustic Emission Inspection Method for Large Normal Pressure Storage Tank Bottom

基于大型常压立式金属储罐底板在线声发射检测及定位的原理,针对声发射检测过程中因声源性质不明确导致的罐底完整性评价结果不准确的问题,采用小波分析方法对罐底声发射信号进行了分解.通过提取声发射信号在不同小波分解频带上的特征频谱系数,与声发射波形参数共同作为BP神经网络学习样本集的特征向量,对神经网络的模式识别性能进行了优化.采用该神经网络对罐底裂纹、腐蚀、泄漏、机械噪声和电磁噪声等不同性质的声发射源进行判别时,其正确识别率均在90%以上,使基于声发射在线检测技术的储罐底板结构完整性评价技术更趋于完善和实用化.

The acoustic emission （AE） inspection and location principle of large vertical normal pressure storage tank bottom on-line inspection was studied. Aiming at solving the problem of inaccurate structure integrity evaluation resulted from ambiguous AE sources, wavelet analysis method was used to decompose acoustic emission signals derived from storage tank bottom. the characteristic frequency factors in different wavelet decomposition frequency bands were extracted as the BP neural network＇s characteristic input vector together with original acoustic emission waveform parameters. Thus, performance of the BP network is optimized and its recognition capability for AE sources is improved. The correct recognition rate of AE sources, such as crack, corrosion, leakage, mechanical noise and EMI noise are all increased to above 90% . The research makes the tank bottom structure integrity evaluation technology based on AE on-line inspection result to be more sophisticated and practical.