- Title:
- Laser Ultrasonic Defect Detection Based on Loca; Outlier Factor and Isolated Forest
- Keywords:
- laser ultrasonic; defect detection; principal component analysis; isolated forest; local outlier factor; aluminium alloy
- CLC:
- TH16 ；TB551
- DOI:
- 10. 13705 / j. issn. 1671-6833. 2025. 01. 003
- Abstract:
- In response to the issue of artifacts in the maximum amplitude images in laser ultrasonic defect detection, this study integrates Principal Component Analysis (PCA) with two unsupervised machine learning algorithms: Local Outlier Factor (LOF) and Isolation Forest (IF), to perform unsupervised anomaly detection on LU data. Initially, the PCA algorithm is used to reduce the dimensionality of the LU data, simplifying its complexity. Subsequently, the LOF and IF algorithms are employed to identify outliers in the data, and the thresholds for these outliers are determined using the cumulative distribution function and kernel density estimation. Finally, a comparison of the detection results from the LOF, IF algorithms, and the maximum amplitude images reveals that the LOF algorithm offers superior defect detection precision and a lower false positive rate
- References:
-
[1] SEGOVIA RAMÍREZ I, GARCÍA MÁRQUEZ F P, PAPAELIAS M. Review on additive manufacturing and nondestructive testing [ J ] . Journal of Manufacturing Systems, 2023, 66: 260-286.
[2] 李阳, 彭笑永, 李大磊, 等. 激光超声无损测量 40Cr钢渗氮层深度的实验研究[ J] . 郑州大学学报(工学版) , 2020, 41(6) : 7-12.
LI Y, PENG X Y, LI D L, et al. Experimental investigation on non-destructive measuring of the nitriding thickness of 40Cr steel by laser ultrasonic [ J ] . Journal of Zhengzhou University ( Engineering Science) , 2020, 41 (6) : 7-12.
[3] 弓鹏飞, 张彦杰, 王法雨, 等. 棒材表面缺陷的激光超声无损检测方法研究 [ J ] . 激光杂志, 2022, 43 (9) : 44-49.
GONG P F, ZHANG Y J, WANG F Y, et al. Research on laser ultrasonic nondestructive testing method for surface defects of bar[ J] . Laser Journal, 2022, 43(9) : 44 -49.
[4] 杨连杰, 李阳, 孙俊杰, 等. 激光超声表面波在表面缺陷上的反射与透射[ J] . 激光 与光电子学进展, 2019, 56(4) : 146-151.
YANG L J, LI Y, SUN J J, et al. Reflection and transmission of laser ultrasonic waves on surface defects [ J] . Laser & Optoelectronics Progress, 2019, 56 ( 4 ) : 146 -151.
[5] 黄燕杰, 尚建华, 任立红, 等. 用于铝板缺陷无损检测的激光超声有限元模拟研究[ J] . 应用光学, 2019, 40(1) : 150-156.
HUANG Y J, SHANG J H, REN L H, et al. Finite element simulation in laser ultrasound for non-destructive testing of aluminum defect materials[ J] . Journal of Applied Optics, 2019, 40(1) : 150-156.
[6] LIU S Y, JIA K N, WAN H P, et al. Inspection of the internal defects with different size in Ni and Ti additive manufactured components using laser ultrasonic technology [J]. Optics & Laser Technology, 2022, 146: 107543.
[7] ZENG W, CAI M M, WANG P, et al. Application of laser ultrasonic technique for detecting weld defect based on FDST method[ J] . Optik, 2020, 221: 165366.
[8] 姜瀚彬, 高炜欣, 石萌萌. 基于卷积神经网络的激光超声缺陷检测研究[ J] . 激光杂志, 2022, 43(7) : 59- 64.
JIANG H B, GAO W X, SHI M M. Research on laser ultrasonic defect detection based on convolutional neural network[ J] . Laser Journal, 2022, 43(7) : 59-64.
[9] LIU Z X, HU Z L, WANG L X, et al. Effective detection of metal surface defects based on double-line laser ultrasonic with convolutional neural networks [ J] . Modern Physics Letters B, 2021, 35(15) : 2150263.
[10] GUO S F, FENG H W, FENG W, et al. Automatic quantification of subsurface defects by analyzing laser ultrasonic signals using convolutional neural networks and wavelet transform[ J] . IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2021, 68 ( 10) : 3216-3225.
[11] 魏博文, 高炜欣, 刘梦溪. 激光超声结合 CNN 的铸件缺陷检测方法[ J] . 激光与红外, 2022, 52(9) : 1327- 1334.
WEI B W, GAO W X, LIU M X. Casting defect detection method combined with laser ultrasound and convolutional neural network [ J] . Laser & Infrared, 2022, 52 (9) : 1327-1334.
[12] ZAREI A, PILLA S. An improved theory of thermoelasticity for ultrafast heating of materials using short and ultrashort laser pulses [ J ] . International Journal of Heat and Mass Transfer, 2023, 215: 124510.
[13] JAYA RAO V V S, KANNAN E, PRAKASH R V, et al. Fatigue damage characterization using surface acoustic wave nonlinearity in aluminum alloy AA7175-T7351[ J] . Journal of Applied Physics, 2008, 104(12) :123508.
[14] SOLODOV I, BAI J X, BUSSE G. Resonant ultrasound spectroscopy of defects: case study of flat-bottomed holes [J]. Journal of Applied Physics, 2013, 113(22): 223512.
[15] HU H Z, LIU J X, ZHANG X P, et al. An effective and adaptable K-means algorithm for big data cluster analysis [ J] . Pattern Recognition, 2023, 139: 109404.
[16] FAIRLEY N, BARGIELA P, HUANG W M, et al. Principal component analysis ( PCA) unravels spectral components present in XPS spectra of complex oxide films on iron foil[ J] . Applied Surface Science Advances, 2023, 17: 100447.
[17] MARUKATAT S. Tutorial on PCA and approximate PCA and approximate kernel PCA [ J] . Artificial Intelligence Review, 2023, 56(6) : 5445-5477.
[18] 吴志强,张胜,包晓玲,等. 针对 WSN 异常数据检测改进的孤立森林方法[ J] . 小型微型计算机系统, 2021, 42(1) :127-131.
WU Z Q, ZHANG S, BAO X L, et al. Improved isolation forest method for WSN anomaly data detection [ J] . Journal of Chinese Computer Systems, 2021, 42(1) : 127 -131.
[19] WANG H F, JIANG W, DENG X Y, et al. A new method for fault detection of aero-engine based on isolation forest[ J] . Measurement, 2021, 185: 110064.
[20] JEONG W, TSINGAS C, ALMUBARAK M S. Local outlier factor as part of a workflow for detecting and attenuating blending noise in simultaneously acquired data[ J] . Geophysical Prospecting, 2020, 68(5) : 1523-1539.
[21] CHIU A L M, FU A W C. Enhancements on local outlier detection[C]∥The Seventh International Database Engineering and Applications Symposium. Piscataway: IEEE, 2003: 298-307.
- Similar References:
-