Assessment of the Effect of Cleanliness on the Visual Inspection of Aircraft Engine Blades: An Eye Tracking Study.

MRO aircraft engine maintenance attentional trajectory blade inspection decision making eye tracking inspection visual perception visual search strategy

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
13 Sep 2021
Historique:
received: 18 08 2021
revised: 03 09 2021
accepted: 07 09 2021
entrez: 28 9 2021
pubmed: 29 9 2021
medline: 30 9 2021
Statut: epublish

Résumé

Background-The visual inspection of aircraft parts such as engine blades is crucial to ensure safe aircraft operation. There is a need to understand the reliability of such inspections and the factors that affect the results. In this study, the factor 'cleanliness' was analysed among other factors. Method-Fifty industry practitioners of three expertise levels inspected 24 images of parts with a variety of defects in clean and dirty conditions, resulting in a total of N = 1200 observations. The data were analysed statistically to evaluate the relationships between cleanliness and inspection performance. Eye tracking was applied to understand the search strategies of different levels of expertise for various part conditions. Results-The results show an inspection accuracy of 86.8% and 66.8% for clean and dirty blades, respectively. The statistical analysis showed that cleanliness and defect type influenced the inspection accuracy, while expertise was surprisingly not a significant factor. In contrast, inspection time was affected by expertise along with other factors, including cleanliness, defect type and visual acuity. Eye tracking revealed that inspectors (experts) apply a more structured and systematic search with less fixations and revisits compared to other groups. Conclusions-Cleaning prior to inspection leads to better results. Eye tracking revealed that inspectors used an underlying search strategy characterised by edge detection and differentiation between surface deposits and other types of damage, which contributed to better performance.

Identifiants

pubmed: 34577343
pii: s21186135
doi: 10.3390/s21186135
pmc: PMC8473167
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Christchurch Engine Centre
ID : 2018-01

Références

Hum Factors. 1998 Jun;40(2):187-97
pubmed: 9720456
J Digit Imaging. 2019 Aug;32(4):597-604
pubmed: 31044392
Med Phys. 2010 Nov;37(11):5728-36
pubmed: 21158284
Perception. 2014;43(2-3):145-54
pubmed: 24919350
J Digit Imaging. 2016 Aug;29(4):496-506
pubmed: 26961982
Cogn Res Princ Implic. 2021 Feb 15;6(1):7
pubmed: 33587219
JAMA. 1996 Dec 4;276(21):1752-5
pubmed: 8940325
Acad Radiol. 2005 Jul;12(7):830-40
pubmed: 16039537
Acta Psychol (Amst). 1987 Jul;65(2):133-46
pubmed: 3687475
Front Psychol. 2014 Oct 01;5:1099
pubmed: 25324806
Behav Res Methods Instrum Comput. 2002 Nov;34(4):455-70
pubmed: 12564550
Ergonomics. 2009 Mar;52(3):325-33
pubmed: 19296326
Appl Ergon. 2002 Nov;33(6):559-70
pubmed: 12507340
Chronobiol Int. 2010 Jul;27(6):1304-16
pubmed: 20653456
Invest Radiol. 1990 Feb;25(2):133-40
pubmed: 2312249
Radiology. 2016 Dec;281(3):805-815
pubmed: 27409563
Percept Psychophys. 1996 Oct;58(7):969-76
pubmed: 8920834
Sensors (Basel). 2021 Jun 23;21(13):
pubmed: 34201734
J Vis. 2013 Aug 28;13(3):
pubmed: 23986539
J Med Imaging (Bellingham). 2020 Sep;7(5):051203
pubmed: 37476351
Sensors (Basel). 2020 Dec 03;20(23):
pubmed: 33287228
J Exp Psychol Hum Percept Perform. 1999 Dec;25(6):1595-608
pubmed: 10641312
Psychol Sci. 2004 May;15(5):302-6
pubmed: 15102138
Med Phys. 2013 Oct;40(10):101906
pubmed: 24089908
Appl Ergon. 1979 Sep;10(3):145-54
pubmed: 15676359
Atten Percept Psychophys. 2019 Jul;81(5):1297-1311
pubmed: 30684203
Behav Res Methods. 2018 Oct;50(5):1853-1863
pubmed: 28879442
J Exp Psychol Learn Mem Cogn. 1984 Jul;10(3):333-52
pubmed: 6235306
Comput Intell Neurosci. 2016;2016:8343842
pubmed: 27239190
Sensors (Basel). 2021 Jun 27;21(13):
pubmed: 34199068
J Rehabil Assist Technol Eng. 2018 Jun 11;5:2055668318773991
pubmed: 31191938
Behav Res Methods. 2018 Feb;50(1):213-227
pubmed: 28205131
Hum Factors. 2015 Dec;57(8):1427-42
pubmed: 26342002
Aviat Space Environ Med. 2014 Jul;85(7):740-4
pubmed: 25022162
J Surg Res. 2014 Sep;191(1):169-78
pubmed: 24881471
Sci Data. 2021 Mar 25;8(1):92
pubmed: 33767191
J Biomed Inform. 2017 Feb;66:171-179
pubmed: 28087402
Adv Health Sci Educ Theory Pract. 2017 Aug;22(3):765-787
pubmed: 27436353
Int J Psychophysiol. 2020 Sep;155:49-62
pubmed: 32504653
Sensors (Basel). 2021 Feb 24;21(5):
pubmed: 33668291
Comput Intell Neurosci. 2016;2016:7831469
pubmed: 27761140
Hum Factors. 2002 Spring;44(1):18-27
pubmed: 12118870
PLoS One. 2014 Aug 01;9(8):e103447
pubmed: 25084012
Aviat Space Environ Med. 2004 May;75(5):420-8
pubmed: 15152894
Surg Endosc. 2012 Dec;26(12):3536-40
pubmed: 22733194
Philos Trans R Soc Lond B Biol Sci. 2013 Sep 09;368(1628):20130067
pubmed: 24018728
Vision Res. 2000;40(10-12):1489-506
pubmed: 10788654
Int J Occup Med Environ Health. 2015;28(6):941-54
pubmed: 26294197
Ergonomics. 1973 Jul;16(4):365-379
pubmed: 28086275
Sensors (Basel). 2020 Sep 18;20(18):
pubmed: 32961984
Vision Res. 1997 Mar;37(5):617-31
pubmed: 9156206
Sensors (Basel). 2021 Mar 23;21(6):
pubmed: 33806750
Med Teach. 2018 Jan;40(1):62-69
pubmed: 29172823
Cogn Res Princ Implic. 2019 Feb 22;4(1):7
pubmed: 30796618

Auteurs

Jonas Aust (J)

Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand.

Antonija Mitrovic (A)

Department of Computer Science and Software Engineering, University of Canterbury, Christchurch 8041, New Zealand.

Dirk Pons (D)

Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand.

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