关键词: 缺陷边缘识别, 磁梯度张量不变量, 改进梯度下降法, 金属磁记忆

Abstract:  Aiming at the problems that the magnetic memory detection of oil and gas pipelines was greatly affected by the direction and the accurate identification of defect edges was difficult, a magnetic gradient tensor combination invariant algorithm was proposed for accurate identification of pipeline defect edges based on MGD optimization. Taking L245N pipeline steel as the test material, the circular hole defects with different depths and diameters were prefabricated, and the magnetic gradient tensor measurement system was designed. Combined with TSC-5M-32 magnetic memory instrument, the magnetic gradient tensor matrix of pipeline was obtained. In order to overcome the influences of the detection direction on the magnetic memory signals, the second invariant I1 and the third invariant I2 of the magnetic gradient tensor were extracted respectively. Further considering that these two invariants were easy to present ambiguity at the edges of the defects, the two invariants were improved according to the Cardano formula, and the weights a and b were set separately for superposition to obtain the combined invariant I. The fractional derivative improved gradient descent algorithm was used to determine the optimal weight, and the magnetic memory representation model of the pipeline defect edges was established. The verification results show that the average relative error of the model for defect edge recognition is as 3.59%, and the maximum relative error is as 6%, which provides a feasible method for accurate identification of pipeline defect edges in practical engineering.

Key words: defect edge recognition, magnetic gradient tensor invariant, modified gradient descent（MGD）, metal magnetic memory