Inspection Intervals Optimization for Aircraft Composite Structures Considering Dent Damage

Chen, Xi; Ren, He; Bil, Cees

doi:10.2514/1.c032377

Cited by 15 publications

(16 citation statements)

References 10 publications

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“…Studies on inspection accuracies within the aviation domain presented detection rates of 68% for the visual inspection of aircraft fuselage [ 32 ], 42% in cargo bay inspection [ 33 ], and 57–98% for the magnetic particle inspection of landing gear components [ 34 ]. The main focus in this industry is on crack and dent detection, predominantly in composite materials, and thus inspection results are commonly reported as probability of detection (PoD) curves, i.e., the chance of defect detection depending on the defect size [ 23 , 24 , 25 , 26 , 35 ].…”

Section: Literature Reviewmentioning

confidence: 99%

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

Aust

Mitrović

Pons

2021

Sensors

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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 66.8% and 86.8% 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.

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Section: Literature Reviewmentioning

confidence: 99%

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

Aust

Mitrović

Pons

2021

Sensors

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“…Not all studies reported a specific detection rate, since it is dependent on the defect type and size. Hence, for cracks and dents-the most common defects investigated in previous studies-the results were commonly reported as probability of detection (PoD) curves, which visualises the likelihood of detecting the defect (y-axis) as a function of the defect size (x-axis) [14][15][16][17][18]. For such PoD curves, a minimum sample size of N = 60 per defect type is required [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of Influence Factors on the Visual Inspection Performance of Aircraft Engine Blades

2021

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Background—There are various influence factors that affect visual inspection of aircraft engine blades including type of inspection, defect type, severity level, blade perspective and background colour. The effect of those factors on the inspection performance was assessed. Method—The inspection accuracy of fifty industry practitioners was measured for 137 blade images, leading to N = 6850 observations. The data were statistically analysed to identify the significant factors. Subsequent evaluation of the eye tracking data provided additional insights into the inspection process. Results—Inspection accuracies in borescope inspections were significantly lower compared to piece-part inspection at 63.8% and 82.6%, respectively. Airfoil dents (19.0%), cracks (11.0%), and blockage (8.0%) were the most difficult defects to detect, while nicks (100.0%), tears (95.5%), and tip curls (89.0%) had the highest detection rates. The classification accuracy was lowest for airfoil dents (5.3%), burns (38.4%), and tears (44.9%), while coating loss (98.1%), nicks (90.0%), and blockage (87.5%) were most accurately classified. Defects of severity level S1 (72.0%) were more difficult to detect than increased severity levels S2 (92.8%) and S3 (99.0%). Moreover, visual perspectives perpendicular to the airfoil led to better inspection rates (up to 87.5%) than edge perspectives (51.0% to 66.5%). Background colour was not a significant factor. The eye tracking results of novices showed an unstructured search path, characterised by numerous fixations, leading to longer inspection times. Experts in contrast applied a systematic search strategy with focus on the edges, and showed a better defect discrimination ability. This observation was consistent across all stimuli, thus independent of the influence factors. Conclusions—Eye tracking identified the challenges of the inspection process and errors made. A revised inspection framework was proposed based on insights gained, and support the idea of an underlying mental model.

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“…PMT has initially used in assessing health risk behaviours which later has been applied widely in other fields as well particularly related to the study of pro-environmental behaviours. The Protection Motivation Theory [34] is shown in the Figure 3 clearly demonstrates the context, constructs and the resultant behaviour. The new Corona Virus which was initially named as Novel Corona Virus emerged in Wuhan, China in December 2019.…”

Section: Fig 2 Green It Life Cyclementioning

confidence: 99%

The Pro-environmental Behaviour and the Effect of COVID-19 Pandemic in Malaysia on Green IT Practices

Alla

Hassan

Chen

2020

2020 International Conference on Computational Intelligence (ICCI)

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The COVID-19 pandemic has caused disruption to human life throughout the world in 2020. This virus has recorded above 21 Million infected cases and 750,000 deaths by early August 2020 [7]. As there is no cure and vaccine available yet, this unprecedented infectious virus is still claiming its toll on daily basis. The year 2020 will be remembered throughout the world for the pandemic and is the same in Malaysia too. Malaysia observed stringent movement control orders (MCO) by locking down the economy from March 18th, 2020. This has greatly reduced the carbon emissions by commercial/ educational entities throughout the country. At the same time, since people were required to stay home and the use of communication and work channels through digital platforms escalated, it has caused the surge in the residential network and energy demand to the peak. The online meetings and the Directto-Home (DTH) connections caused enormous load on the network infrastructure and the related energy surge. This study is an attempt to study the effects of pro-environmental behaviour and the Green IT practices during the lockdown period through applying the Protection Motivation Theory (PMT). Our research indicated that the constructs of PMT has the significant capability to define the pro-environmental behaviour in the individuals. The PMT constructs, 'selfefficacy' and 'response-efficacy', along with the extrinsic and intrinsic rewards of intentions can derive pro-environmental behaviour in the given circumstances related to this study.

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Inspection Intervals Optimization for Aircraft Composite Structures Considering Dent Damage

Cited by 15 publications

References 10 publications

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

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

Evaluation of Influence Factors on the Visual Inspection Performance of Aircraft Engine Blades

The Pro-environmental Behaviour and the Effect of COVID-19 Pandemic in Malaysia on Green IT Practices

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