scholarly journals Bowtie Methodology for Risk Analysis of Visual Borescope Inspection during Aircraft Engine Maintenance

Aerospace ◽  
2019 ◽  
Vol 6 (10) ◽  
pp. 110 ◽  
Author(s):  
Aust ◽  
Pons
Keyword(s):  
Visual Inspection ◽  
Aircraft Engine ◽  
Aircraft Maintenance ◽  
System Failure ◽  
Structured Interviews ◽  
Flight Operations ◽  
Inspection Tasks ◽  
Engine Maintenance ◽  
Serious Injuries ◽  
Is Value

Background—The inspection of aircraft parts is critical, as a defective part has many potentially adverse consequences. Faulty parts can initiate a system failure on an aircraft, which can lead to aircraft mishap if not well managed and has the potential to cause fatalities and serious injuries of passengers and crew. Hence, there is value in better understanding the risks in visual inspection during aircraft maintenance. Purpose—This paper identifies the risks inherent in visual inspection tasks during aircraft engine maintenance and how it differs from aircraft operations. Method—A Bowtie analysis was performed, and potential hazards, threats, consequences, and barriers were identified based on semi-structured interviews with industry experts and researchers’ insights gained by observation of the inspection activities. Findings—The Bowtie diagram for visual inspection in engine maintenance identifies new consequences in the maintenance context. It provides a new understanding of the importance of certain controls in the workflow. Originality—This work adapts the Bowtie analysis to provide a risk assessment of the borescope inspection activity on aircraft maintenance tasks, which was otherwise not shown in the literature. The consequences for maintenance are also different compared to flight operations, in the way operational economics are included.

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

Aerospace ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 117
Author(s):  
Jonas Aust ◽  
Dirk Pons
Keyword(s):  
Risk Assessment ◽  
Visual Inspection ◽  
Aircraft Engine ◽  
Risk Framework ◽  
Material Separation ◽  
Inspection Tasks ◽  
Risk Metric ◽  
Engine Components ◽  
Application Specific ◽  
Industrial Context

Risk assessment methods are widely used in aviation, but have not been demonstrated for visual inspection of aircraft engine components. The complexity in this field arises from the variety of defect types and the different manifestation thereof with each level of disassembly. A new risk framework was designed to include contextual factors. Those factors were identified using Bowtie analysis to be criticality, severity, and detectability. This framework yields a risk metric that describes the extent to which a defect might stay undetected during the inspection task, and result in adverse safety outcomes. A simplification of the framework provides a method for go/no-go decision-making. The results of the study reveal that the defect detectability is highly dependent on specific views of the blade, and the risk can be quantified. Defects that involve material separation or removal such as scratches, tip rub, nicks, tears, cracks, and breaking, are best shown in airfoil views. Defects that involve material deformation and change of shape, such as tip curl, dents on the leading edges, bents, and battered blades, have lower risk if edge views can be provided. This research proposes that many risk assessments may be reduced to three factors: consequence, likelihood, and a cofactor. The latter represents the industrial context, and can comprise multiple sub-factors that are application-specific. A method has been devised, including appropriate scales, for the inclusion of these into the risk assessment.

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

Aerospace ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 313
Author(s):  
Jonas Aust ◽  
Antonija Mitrovic ◽  
Dirk Pons
Keyword(s):  
Classification Accuracy ◽  
Visual Inspection ◽  
False Positive Rate ◽  
Aircraft Engine ◽  
Tactile Perception ◽  
Inspection Time ◽  
Positive Rate ◽  
Training Programmes ◽  
Engine Maintenance ◽  
Inspection Performance

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.

scholarly journals Efficient Binarized Convolutional Layers for Visual Inspection Applications on Resource-Limited FPGAs and ASICs

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1511
Author(s):  
Taylor Simons ◽  
Dah-Jye Lee
Keyword(s):  
Neural Networks ◽  
Visual Inspection ◽  
Deep Neural Networks ◽  
Computational Cost ◽  
Quality Inspection ◽  
Agricultural Produce ◽  
Resource Limited ◽  
Inspection Tasks ◽  
Computational Resources ◽  
Small Models

There has been a recent surge in publications related to binarized neural networks (BNNs), which use binary values to represent both the weights and activations in deep neural networks (DNNs). Due to the bitwise nature of BNNs, there have been many efforts to implement BNNs on ASICs and FPGAs. While BNNs are excellent candidates for these kinds of resource-limited systems, most implementations still require very large FPGAs or CPU-FPGA co-processing systems. Our work focuses on reducing the computational cost of BNNs even further, making them more efficient to implement on FPGAs. We target embedded visual inspection tasks, like quality inspection sorting on manufactured parts and agricultural produce sorting. We propose a new binarized convolutional layer, called the neural jet features layer, that learns well-known classic computer vision kernels that are efficient to calculate as a group. We show that on visual inspection tasks, neural jet features perform comparably to standard BNN convolutional layers while using less computational resources. We also show that neural jet features tend to be more stable than BNN convolution layers when training small models.

scholarly journals Learning-Based Autonomous UAV System for Electrical and Mechanical (E&M) Device Inspection

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1385
Author(s):  
Yurong Feng ◽  
Kwaiwa Tse ◽  
Shengyang Chen ◽  
Chih-Yung Wen ◽  
Boyang Li
Keyword(s):  
System Design ◽  
State Estimation ◽  
Unmanned Aerial Vehicles ◽  
Visual Inspection ◽  
Spatial Information ◽  
Free Flight ◽  
Object State ◽  
Aerial Vehicles ◽  
Inspection Tasks ◽  
Autonomous Inspection

The inspection of electrical and mechanical (E&M) devices using unmanned aerial vehicles (UAVs) has become an increasingly popular choice in the last decade due to their flexibility and mobility. UAVs have the potential to reduce human involvement in visual inspection tasks, which could increase efficiency and reduce risks. This paper presents a UAV system for autonomously performing E&M device inspection. The proposed system relies on learning-based detection for perception, multi-sensor fusion for localization, and path planning for fully autonomous inspection. The perception method utilizes semantic and spatial information generated by a 2-D object detector. The information is then fused with depth measurements for object state estimation. No prior knowledge about the location and category of the target device is needed. The system design is validated by flight experiments using a quadrotor platform. The result shows that the proposed UAV system enables the inspection mission autonomously and ensures a stable and collision-free flight.

Measuring Human Detection Performance in Aircraft Visual Inspection

1997 ◽  
Vol 41 (1) ◽  
pp. 304-308 ◽  
Author(s):  
Colin G. Drury ◽  
Floyd W. Spencer ◽  
Donald L. Schurman
Keyword(s):  
Visual Acuity ◽  
Individual Differences ◽  
Visual Inspection ◽  
Individual Characteristics ◽  
Human Detection ◽  
Field Independence ◽  
Multiple Fault ◽  
Industrial Inspection ◽  
Inspection Tasks ◽  
Non Destructive

In airworthiness assurance, while there is a long tradition of measuring inspection reliability for machine-aided Non-Destructive Inspection (NDI), the more common visual inspection has received little attention. Yet inspection reliability measurements are needed if we are to set appropriate inspection intervals for airframe components. Visual inspection of aircraft is characterized as using multiple senses (despite its name) and having to inspect for multiple fault types, in contrast to NDI which is used for single specific fault types. The study here used 12 professional inspectors to perform nine visual inspection tasks on a long-service Boeing 737 aircraft. Each inspector worked over two days. Measures were taken of performance, strategy and individual differences. Only a fraction of the results are presented here, with a major finding that aircraft visual inspection has approximately the same reliability as industrial inspection. Individual differences were found, as well as correlations between certain aspects of performance and individual characteristics such as Field Independence and Peripheral Visual Acuity. However, there was little correlation between an individual inspector's performance on the different tasks, showing the difficulty of designing selection and placement procedures for such a wide-ranging job.

scholarly journals Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection

Aerospace ◽  
2020 ◽  
Vol 7 (12) ◽  
pp. 171
Author(s):  
Anil Doğru ◽  
Soufiane Bouarfa ◽  
Ridwan Arizar ◽  
Reyhan Aydoğan
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Visual Inspection ◽  
Hybrid Approach ◽  
Lightning Strike ◽  
Aircraft Maintenance ◽  
Original Dataset ◽  
Wide Range ◽  
Augmentation Techniques

Convolutional Neural Networks combined with autonomous drones are increasingly seen as enablers of partially automating the aircraft maintenance visual inspection process. Such an innovative concept can have a significant impact on aircraft operations. Though supporting aircraft maintenance engineers detect and classify a wide range of defects, the time spent on inspection can significantly be reduced. Examples of defects that can be automatically detected include aircraft dents, paint defects, cracks and holes, and lightning strike damage. Additionally, this concept could also increase the accuracy of damage detection and reduce the number of aircraft inspection incidents related to human factors like fatigue and time pressure. In our previous work, we have applied a recent Convolutional Neural Network architecture known by MASK R-CNN to detect aircraft dents. MASK-RCNN was chosen because it enables the detection of multiple objects in an image while simultaneously generating a segmentation mask for each instance. The previously obtained F1 and F2 scores were 62.67% and 59.35%, respectively. This paper extends the previous work by applying different techniques to improve and evaluate prediction performance experimentally. The approach uses include (1) Balancing the original dataset by adding images without dents; (2) Increasing data homogeneity by focusing on wing images only; (3) Exploring the potential of three augmentation techniques in improving model performance namely flipping, rotating, and blurring; and (4) using a pre-classifier in combination with MASK R-CNN. The results show that a hybrid approach combining MASK R-CNN and augmentation techniques leads to an improved performance with an F1 score of (67.50%) and F2 score of (66.37%).

A systematic approach to assess visual inspection tasks: an assembly line case

2020 ◽  
Vol 29 (3) ◽  
pp. 418
Author(s):  
Mustafa Can Tuncer ◽  
Necmettin Firat Ozkan ◽  
Berna Haktanirlar Ulutas
Keyword(s):  
Visual Inspection ◽  
Assembly Line ◽  
Systematic Approach ◽  
Inspection Tasks
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