Automated defect detection for Fluorescent Penetrant Inspection using Random Forest
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Most research focuses on defect detection on turbine blades rather than compressor blades, mainly crack detection.
“Furthermore, most research focuses on defect detection on turbine blades rather than compressor blades. This encompasses mainly crack detection [9,33,37,38,42], as this is the most critical type of defect and the main source of failure.”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [42]
In aircraft visual inspection in particular, the defect detection rate was stated to be 68% to 74%.
“In aircraft visual inspection in particular, the defect detection rate was stated to be 68% to 74% [42,99].”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [42]
The detection rate is comparable with the performance of neural networks in the reviewed literature, which ranged from 64.4% to 85%.
“The detection rate is comparable with the performance of neural networks in the reviewed literature [21,35,42,48], which ranged from 64.4% to 85%.”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [42]
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565 · see citing works
Random forest in remote sensing: A review of applications and future directions
Belgiu, M., & Drăguţ, L. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2016.01.011 · Cited by 7,228 · see citing works
Biau, G., & Scornet, E. (2016) · Test · doi:10.1007/s11749-016-0481-7 · Cited by 4,149 · see citing works
Zaki, M. J., & Meira, W. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108564175.026 · Cited by 1,437 · see citing works
Signal Detection Theory and ROC Analysis in Psychology and Diagnostics: Collected Papers
Swets, J. A. (1996) · Medical Entomology and Zoology · Cited by 782 · see citing works
The Origins of Logistic RegressionPreprint
Cramer, J. S. (2003) · SSRN Electronic Journal · doi:10.2139/ssrn.360300 · Cited by 511 · see citing works
Mucherino, A., Papajorgji, P. J., & Pardalos, P. M. (2009) · Springer optimization and its applications · doi:10.1007/978-0-387-88615-2_6 · Cited by 219 · see citing works
Evaluating diagnostic tests: The area under the ROC curve and the balance of errors
Hand, D. J. (2010) · Statistics in Medicine · doi:10.1002/sim.3859 · Cited by 140 · see citing works
Chan, J. C.-W., Beckers, P., Spanhove, T., et al. (2012) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2012.01.002 · Cited by 64 · see citing works
The FROC Curve: A Representation of the Observer's Performance for the Method of Free Response
Miller, H. (1969) · The Journal of the Acoustical Society of America · doi:10.1121/1.1911889 · Cited by 33 · see citing works
Comparison of two non-destructive inspection techniques on the basis of sensitivity and reliability
Şi̇mşi̇r, M., & Ankara, A. (2006) · Materials & Design (1980-2015) · doi:10.1016/j.matdes.2006.03.019 · Cited by 23 · see citing works
Gas turbine engine disk cyclic life prediction
Sattar, S., & Sundt, C. V. (1975) · Journal of Aircraft · doi:10.2514/3.44456 · Cited by 19 · see citing works
Application of quantum dots as a fluorescent-penetrant for weld crack detection
Daneshvar, K., & Doğan, B. (2010) · Materials at High Temperatures · doi:10.3184/096034010x12813744660988 · Cited by 15 · see citing works
Fully automated dye-penetrant inspection of automotive parts
Abend, K. (1999) · Computer Standards & Interfaces · doi:10.1016/s0920-5489(99)92144-x · Cited by 11 · see citing works
Larson, B. F. (2002) · Defense Technical Information Center (DTIC) · Cited by 9 · see citing works
Design of advanced automatic inspection system for turbine blade FPI analysis
Zheng, J., Xie, W., Viens, M., Birglen, L., & Mantegh, I. (2013) · AIP conference proceedings · doi:10.1063/1.4789103 · Cited by 9 · see citing works
Machine Learning [Guest editor's introduction]
Alexander, F. J. (2013) · Computing in Science & Engineering · doi:10.1109/mcse.2013.107 · Cited by 5 · see citing works
Image acquisition and processing in an attempt to automate the fluorescent penetrant inspection
Alba, C. A. C. (2011) · Espace ÉTS (ETS) · Cited by 4 · see citing works
Brasche, L. J. H. (2003) · AIP conference proceedings · doi:10.1063/1.1570285 · Cited by 3 · see citing works
Automated Fluorescent Penetrant Inspection (FPI) System Is Triple A
Adair, T. L., Kindrew, M. G., Winter, H. I., et al. (1998) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/983124 · Cited by 3 · see citing works
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Research works printed but not linked: 1
- Y. Tang, A. Niu, W. G. Wee, C. Y. Han, C. Science, An automated inspection system for detecting metal surface cracks from Fluorescent Penetrant images, Machine Vision applications in industrial inspection III 2423. · DOI
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Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection
Aust, J., Shankland, S., Pons, D., Mukundan, R., & Mitrovic, A. (2021) · Aerospace · doi:10.3390/aerospace8020030 · Cited by 66 · see citing works
“Some researchers only reported the TP rate [35,42] or the FP and FN rates [20], while others reported the error rate [48], detection rate [21,43] or accuracy [43], and still, others made it dependent of the defect size and used a probability of detection curve [33].”
BackgroundMethodResultInfluentialMachine Learning-Based Detection Technique for NDT in Industrial Manufacturing
Niccolai, A., Caputo, D., Chieco, L., Grimaccia, F., & Mussetta, M. (2021) · Mathematics · doi:10.3390/math9111251 · Cited by 61 · see citing works
- Authors
- Shipway, N. J., Barden, T. J., Huthwaite, P., & Lowe, M. J. S.
- Affiliations
- Imperial College London (GB)
- Rolls-Royce (United Kingdom) (GB)
- Corresponding author
- Shipway, N.J.
- Venue
- NDT & E International
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 101
- Pages
- 113-123
- ISSN
- 0963-8695 (ISSN-L)
- OpenAlex
- W2898232272
- Cited by
- 87
- References
- 22
- Citations by year
- 2026: 12 · 2025: 19 · 2024: 8 · 2023: 12 · 2022: 17 · 2021: 13 · 2020: 4 · 2019: 2
- FWCI
- 3.93
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- NDT & E International (publishedVersion)
- Spiral (Imperial College London) (submittedVersion, cc-by-nc-nd)
- Funders
- Engineering and Physical Sciences Research Council (EP/L015587/1, EP/M020207/1)
- Rolls-Royce plc (ETC00807)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- fluorescent penetrant inspection, automated defect detection, random forest, non-destructive testing, titanium plates, aerospace components, image-based inspection
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