Alternating current field measurement instrument for defects intelligent detection based on FPGA
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摘要: 石油石化行业中，碳钢结构物长期受到腐蚀、应力等因素的影响，表面易产生复杂裂纹，复杂裂纹的检测分析需要强大的数据处理能力，对检测仪器的功能架构提出了诸多挑战。首先基于交流电磁场检测（ACFM）技术，建立了不同类型裂纹的仿真模型，分析结构物表面感应电流分布，探究缺陷形貌-电流扰动-磁场畸变之间的映射关系，提出了基于特征信号Bz的缺陷智能识别算法；然后基于FPGA （现场可编程逻辑门阵列）平台，构建交流电磁场缺陷智能检测仪，实现了激励信号发生、检测信号采集、处理和显示等功能，并开展了人工预制裂纹的识别试验。试验结果表明，该交流电磁场缺陷智能检测仪可以实现不同角度直线裂纹和复杂裂纹的表面轮廓重构与识别。
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关键词:
- 交流电磁场检测 /
- 现场可编程逻辑门阵列 /
- 智能检测仪器 /
- 智能识别 /
- 轮廓重构
Abstract: In the petroleum industry, carbon steel structure is affected by corrosion and stress for a long time, complex cracks are easily introduced in the surface of the carbon steel structures. The detection and analysis of complex cracks require strong data processing ability, which poses many challenges to the functional architecture of detection instruments. Based on the principle of alternating current field measurement (ACFM), the finite element model of different types of cracks was established in the paper. The induced current distribution on the surface of the structure was analyzed. The mapping relationship between defect morphology-current perturbation-magnetic field distortion was explored. An intelligent defect identification algorithm based on the characteristic signal Bz was proposed. Based on FPGA (Field Programmable Gate Array) platform, a ACFM instrument for defects intelligent detection was built. The instrument realized the excitation signal generation and the detection signal collection, processing and display. Artificial crack identification experiments were carried out. The experimental results showed that the ACFM instrument for defects intelligent detection can realize the reconstruction and recognition of linear cracks and complex cracks at different angles. -
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