管道损伤磁梯度共振稀疏分解与BMSR辨识方法

Magnetic Gradient Resonance Sparse Decomposition and BMSR Identification Method for Pipeline Damage

为更好地提取埋地钢质管道地磁环境下由应力集中产生的磁力信号,克服现有磁力检测缺乏有效高灵敏度多元探头阵列和信号处理技术的问题,提出一种磁梯度张量共振稀疏分解结合偏置单稳随机共振(bias monostable stochastic resonance,简称BMSR)的辨识方法对管道损伤进行有效评估。首先,探头布置采用十字张量阵列形式;其次,针对管道缺陷和现场干扰信号特点,用不同品质因子对信号进行共振稀疏分解,剔除掉一部分干扰信号;最后,加入不同时域恢复的随机共振系统结合量子遗传算法进行参数寻优。将该辨识方法用于实际站场管道张量检测信号,比较传统低通滤波、不同随机共振系统结合共振稀疏分解的处理结果,验证了磁梯度张量共振稀疏分解加偏置单稳处理算法在提取管道损伤磁场和表征管道应力集中的有效性。

To extract the magnetic signal generated by a stress concentration of buried steel pipeline geomagnetic environment exactly,and to overcome the drawback of the existing magnetic testing,which lacks effective detection of high sensitivity multiple probe array and signal processing technology,a kind of method using magnetic gradient tensor with resonance sparse decomposition and bias monostable stochastic resonance(BMSR)to evaluate the pipeline damage identification is put forward.Firstly,the probe arrangement is in the form of a cross tensor array.Secondly,according to the characteristics of the pipeline defect and on-site interference signals,the resonance sparse decomposition of the signal with different quality factors is used to eliminate some interference signals.Finally,the stochastic resonance system with different time domain recovery is added with quantum genetic algorithm for parameter optimization.The identification method is used for the actual station pipeline tensor detection signal,compared with the results using the traditional low-pass filtering,stochastic resonance system combined with different resonance sparse decomposition,the magnetic gradient tensor resonance sparse decomposition and bias monostable processing algorithm is verified to be effective on extracting pipeline damage characterization of magnetic field and pipeline stress concentration.