| 姚俊宇,张颖,赵鹏程,王雪琴,钱一呈.FCM-FastICA的点蚀声发射信号分离识别方法*[J].,2024,43(1):169-177 |
| FCM-FastICA的点蚀声发射信号分离识别方法* |
| Pitting acoustic emission signal separation and recognition method based on FCM-FastICA |
| 投稿时间：2022-08-17 修订日期：2023-12-28 |
| 中文摘要: |
| 金属点蚀是一种破坏性和隐患较大的设备损伤形式,点蚀会产生声发射信号。点蚀过程中产生的多种声源类型会造成信号混叠,影响腐蚀进程的判断。针对点蚀信号混叠问题,提出一种FCM聚类与FastICA算法相结合的点蚀信号分离识别方法。通过分析单、双点蚀声发射数据将点蚀分为钝化膜破裂阶段、点蚀诱导成核及发展阶段,由聚类确定信号类别并用FastICA分离混合信号,利用相关性函数验证分离效果。结果表明：单点蚀过程存在三类原信号,双点蚀过程存在七类信号,其中包含单个信号与混合信号；单个信号与原信号相关性极高,达到0.8以上,混合信号的分离分量与原信号相关性达到0.6以上,分离效果较好。该方法可对点蚀混合信号进行有效分离和识别,为腐蚀进程判断提供支持。 |
| 英文摘要: |
| Metal pitting is a destructive and hidden damage form of equipment, which will generate acoustic emission signals. The various sound source types generated in the pitting process will cause signal aliasing and affect the judgment of corrosion process. Aiming at the problem of pitting signal aliasing, a pitting signal separation and recognition method based on FCM clustering and FastICA algorithm was proposed. By analyzing single and double pitting AE data, the pitting process was divided into passive film rupture stage, pitting induced nucleation stage and development stage. The signal categories were determined by clustering and the mixed signals were separated by FastICA, and the separation effect was verified by correlation function. The results show that there are three kinds of original signals in single pitting process and seven kinds of signals in double pitting process, including single signal and mixed signal. The correlation between a single signal and the original signal is extremely high, reaching above 0.8. The correlation between the separation component of the mixed signal and the original signal is more than 0.6. The separation effect is good. This method can effectively separate and identify the pitting mixture signals and provide support for judging the corrosion process. |
| DOI：10.11684/j.issn.1000-310X.2024.01.020 |
| 中文关键词: 点蚀混合信号 盲源分离 相关性系数 信号分离 聚类 |
| 英文关键词: Pitting mixed signal Blind source separation Coefficient of correlation Signal separation Clustering |
| 基金项目:中国石油天然气股份有限公司—常州大学创新联合体科技合作项目 |
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