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In order to quantitatively identify drill pipe defect levels by using Metal Magnetic Memory (MMM) technology, an optimized multi-classification FSVM model is first proposed. The material of drill pipes is S355 low carbon alloy steel. The MMM signal characteristics of different damage levels are obtained through a lot of field testing. Considering the dispersion and small samples of MMM data in practical engineering, SVM is introduced to solve the bottleneck of small samples and dispersion. The fussy membership degree function and the parameter combinatorial optimization are adopted to improve the robustness of SVM. By extracting four dimension characteristic parameters, the quantitative MMM identification model is proposed based on optimized multi-classification FSVM algorithm. The test results show that the model precision is 90.17%, which provides a new method for the quantitative MMM evaluation on drilling tool damage levels.

Quantitative Identification Modeling of Drilling Pipe Damage Levels Based on Metal Magnetic Memory Characteristic Parameters