Methodology for Evaluating Risk of Visual Inspection Tasks of Aircraft Engine Blades

Aust, Jonas; Pons, Dirk J.

doi:10.3390/aerospace8040117

Cited by 13 publications

(8 citation statements)

References 64 publications

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“…It stands out that the easiest defects to detect were tears, tip curls, and tip rubs, with a 100% detection rate in almost all three inspection methods. Those were the defects with the most salient visual appearance and were also the most severe defects [23]. Tears imply the greatest risk as far as safety is concerned, since they have a high chance to propagate, cause material separation, and subsequent damage of the engine.…”

Section: Inspection Accuracymentioning

confidence: 99%

“…It need not be 100% because: (a) it is not achievable because of human factors [32]. Even if there were a technology with 100% detection accuracy (finding all suspicious features), it would not necessarily yield 100% inspection accuracy because a decision component would still need to be applied; (b) there are additional in-service inspections at regular intervals and periodic shop visits (tear-down) that can detect missed defects that have propagated to larger size but not yet failed; (c) the acceptable inspection accuracy depends on the criticality and type of defect [23], i.e., whether they aresafety-critical types (bend, dent, nick, and tear) or flight efficiency-reducing types (airfoil dent, tip curl, and tip rub). Possibly, and this is only a guess, an inspection accuracy around 75% [33] might be sufficient for safety-critical defects to allow safe flight status while minimising unnecessary scrappage, assuming normal inspections are adhered to.…”

Section: Defect Typesmentioning

confidence: 99%

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Comparison of Visual and Visual–Tactile Inspection of Aircraft Engine Blades

2021

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Background—In aircraft engine maintenance, the majority of parts, including engine blades, are inspected visually for any damage to ensure a safe operation. While this process is called visual inspection, there are other human senses encompassed in this process such as tactile perception. Thus, there is a need to better understand the effect of the tactile component on visual inspection performance and whether this effect is consistent for different defect types and expertise groups. Method—This study comprised three experiments, each designed to test different levels of visual and tactile abilities. In each experiment, six industry practitioners of three expertise groups inspected the same sample of N = 26 blades. A two-week interval was allowed between the experiments. Inspection performance was measured in terms of inspection accuracy, inspection time, and defect classification accuracy. Results—The results showed that unrestrained vision and the addition of tactile perception led to higher inspection accuracies of 76.9% and 84.0%, respectively, compared to screen-based inspection with 70.5% accuracy. An improvement was also noted in classification accuracy, as 39.1%, 67.5%, and 79.4% of defects were correctly classified in screen-based, full vision and visual–tactile inspection, respectively. The shortest inspection time was measured for screen-based inspection (18.134 s) followed by visual–tactile (22.140 s) and full vision (25.064 s). Dents benefited the most from the tactile sense, while the false positive rate remained unchanged across all experiments. Nicks and dents were the most difficult to detect and classify and were often confused by operators. Conclusions—Visual inspection in combination with tactile perception led to better performance in inspecting engine blades than visual inspection alone. This has implications for industrial training programmes for fault detection.

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Section: Inspection Accuracymentioning

confidence: 99%

Section: Defect Typesmentioning

confidence: 99%

Comparison of Visual and Visual–Tactile Inspection of Aircraft Engine Blades

2021

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“…Stone and Krishnamurthy 144 proposed a thrust force controller using an ANN to reduce the development of delaminations caused by the entry and removal of a drill bit to an FRP. Machine learning was employed to classify the Visual inspection [85][86][87][88][89][90] Ultrasonic inspection [91][92][93][94] Eddy current [95][96][97][98][99] Radiography 100-105 (e.g., x-ray) Thermography 97,[106][107][108][109][110] Acoustic emission [111][112][113][114][115] Fiber optic sensors [116][117][118][119][120][121] (e.g., fiber Bragg grating) Piezoelectric transducers [122][123][124][125] Laser vibrometry [126][127][128][129][130][131][132] failure methods of composite plates bolted together. 145 A beneficial application of ML is prediction making.…”

Section: Composite Applications With Machine Learningmentioning

confidence: 99%

The intersection of damage evaluation of fiber-reinforced composite materials with machine learning: A review

Nelon

Myers

Hall

2022

Journal of Composite Materials

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Machine learning (ML) has emerged as a useful predictive tool based on mathematical and statistical relationships for various engineering problems. The pairing of structural health monitoring (SHM) and nondestructive evaluation (NDE) methods with ML algorithms has yielded beneficial results in addressing the damage state of a material or system. Damage state descriptions addressed with ML include detecting a damage mechanism, locating a mechanism, identifying the type of mechanism, assessing the extent of the damage mechanism, and estimating the useful remaining life of a material or system. Damage evaluation research of composite materials has progressed with the increased usage of composite structural elements in the aerospace industry. NDE methods are a viable candidate for pairing with ML algorithms to improve damage state monitoring of composite materials due to the complexity associated with the structure of composites. Fiber-reinforced polymers (FRP), for example, contain at least two constituent materials a fiber and matrix material whose mechanical behavior and interactions contribute to the performance of an FRP. Unlike conventional composite analytical models that require explicit information about the constituents and microstructure of a laminate, an ML algorithm can construct damage evaluation predictions when employing exclusively past operational performance or data from an SHM or NDE method. A researcher determines the type of data selected when applying an ML model for trend analysis, anomaly detection, or prediction making. However, no one specific input feature is required for utilizing an ML model, and examples of possible data features include material properties, physical dimensions, and collected evaluation data. In the present review, applications of ML combined with the damage state evaluation of composite materials, particularly examining FRPs, are discussed to demonstrate the predictive capabilities of ML and its viability for future applications, especially in industrial environments, to minimize costs and improve damage detection rates.

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“… Aust and Pons (2021) suggest an extended framework for risk assessment that involves the use of cofactors , which are risk modifiers based on situational conditions, like weather, human health and affective status, equipment condition, etc. In particular, they propose the use of multiplicative cofactors

, with risk taking the form

…”

Section: Frameworkmentioning

confidence: 99%

Generalized Behavior Framework for Mobile Robots Teaming With Humans in Harsh Environments

2022

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Industrial contexts, typically characterized by highly unstructured environments, where task sequences are difficult to hard-code and unforeseen events occur daily (e.g., oil and gas, energy generation, aeronautics) cannot completely rely upon automation to substitute the human dexterity and judgment skills. Robots operating in these conditions have the common requirement of being able to deploy appropriate behaviours in highly dynamic and unpredictable environments, while aiming to achieve a more natural human-robot interaction and a broad range of acceptability in providing useful and efficient services. The goal of this paper is to introduce a deliberative framework able to acquire, reuse and instantiate a collection of behaviours that promote an extension of the autonomy periods of mobile robotic platforms, with a focus on maintenance, repairing and overhaul applications. Behavior trees are employed to design the robotic system’s high-level deliberative intelligence, which integrates: social behaviors, aiming to capture the human’s emotional state and intention; the ability to either perform or support various process tasks; seamless planning and execution of human-robot shared work plans. In particular, the modularity, reactiveness and deliberation capacity that characterize the behaviour tree formalism are leveraged to interpret the human’s health and cognitive load for supporting her/him, and to complete a shared mission by collaboration or complete take-over. By enabling mobile robotic platforms to take-over risky jobs which the human cannot, should not or do not want to perform the proposed framework bears high potential to significantly improve the safety, productivity and efficiency in harsh working environments.

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Methodology for Evaluating Risk of Visual Inspection Tasks of Aircraft Engine Blades

Cited by 13 publications

References 64 publications

Comparison of Visual and Visual–Tactile Inspection of Aircraft Engine Blades

Comparison of Visual and Visual–Tactile Inspection of Aircraft Engine Blades

The intersection of damage evaluation of fiber-reinforced composite materials with machine learning: A review

Generalized Behavior Framework for Mobile Robots Teaming With Humans in Harsh Environments

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