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 A Systematic Methodology for Developing Bowtie in Risk Assessment: Application to Borescope Inspection

Aerospace ◽  
2020 ◽  
Vol 7 (7) ◽  
pp. 86
Author(s):  
Jonas Aust ◽  
Dirk Pons
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Gas Turbine ◽  
Visual Inspection ◽  
Development Process ◽  
Ad Hoc ◽  
Risk Assessments ◽  
Specific Knowledge ◽  
Systematic Methodology ◽  
Application Specific

Background—Bowtie analysis is a broadly used tool in risk management to identify root causes and consequences of hazards and show barriers that can prevent or mitigate the events to happen. Limitations of the method are reliance on judgement and an ad hoc development process. Purpose—Systematic approaches are needed to identify threats and consequences, and to ascertain mitigation and prevention barriers. Results—A new conceptual framework is introduced by combining the Bowtie method with the 6M structure of Ishikawa to categorise the threats, consequences and barriers. The method is developed for visual inspection of gas turbine components, for which an example is provided. Originality—Provision of a more systematic methodology has the potential to result in more comprehensive Bowtie risk assessments, with less chance of serious omissions. The method is expected to find application in the broader industry, and to support operators who are non-risk experts but have application-specific knowledge, when performing Bowtie risk assessment.

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 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.

scholarly journals Repair of Precision Castings Made of the Inconel 713C Alloy

2017 ◽  
Vol 17 (3) ◽  
pp. 210-216
Author(s):  
K. Łyczkowska ◽  
J. Adamiec
Keyword(s):  
Model Test ◽  
Aircraft Engine ◽  
Casting Defects ◽  
Exhaust Gas ◽  
Plasma Arc ◽  
Nickel Based Superalloy ◽  
Melted Zone ◽  
Base Materials ◽  
Inconel 713C ◽  
Engine Components

Abstract Inconel 713C precision castings are used as aircraft engine components exposed to high temperatures and the aggressive exhaust gas environment. Industrial experience has shown that precision-cast components of such complexity contain casting defects like microshrinkage, porosity, and cracks. This necessitates the development of repair technologies for castings of this type. This paper presents the results of metallographic examinations of melted areas and clad welds on the Inconel 713C nickel-based superalloy, made by TIG, plasma arc, and laser. The cladding process was carried out on model test plates in order to determine the technological and material-related problems connected with the weldability of Inconel 713C. The studies included analyses of the macro- and microstructure of the clad welds, the base materials, and the heat-affected zones. The results of the structural analyses of the clad welds indicate that Inconel 713C should be classified as a low-weldability material. In the clad welds made by laser, cracks were identified mainly in the heat-affected zone and at the melted zone interface, crystals were formed on partially-melted grains. Cracks of this type were not identified in the clad welds made using the plasma-arc method. It has been concluded that due to the possibility of manual cladding and the absence of welding imperfections, the technology having the greatest potential for application is plasma-arc cladding.

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.

A Risk Framework for Munitions and Explosives of Concern in Support of U.S. Offshore Wind Energy Development

2019 ◽  
Vol 53 (2) ◽  
pp. 6-20 ◽  
Author(s):  
Geoffrey Carton ◽  
Carter DuVal ◽  
Arthur Trembanis
Keyword(s):  
Risk Assessment ◽  
Wind Energy ◽  
Hazard Assessment ◽  
Risk Mitigation ◽  
Energy Generation ◽  
Offshore Wind ◽  
Offshore Wind Energy ◽  
Wind Energy Generation ◽  
Operation And Maintenance ◽  
Risk Framework

AbstractMunitions and explosives of concern (MEC) in U.S. waters can present a risk to the development and operation of offshore wind energy resources. Therefore, the U.S. Bureau of Ocean Energy Management requires offshore wind energy developers to evaluate the risk MEC poses to the development, operation, and maintenance of offshore wind energy generation and transmission systems. This article describes an MEC risk management framework consisting of the following steps: (1) MEC hazard assessment, (2) MEC risk assessment, (3) MEC risk validation, and (4) MEC risk mitigation. The MEC hazard assessment involves historical research to identify MEC potentially present in the development area. The MEC risk assessment evaluates the development activities and provides a relative MEC risk ranking for those activities. The developer determines the acceptability of these risks, and any potentially unacceptable MEC risks undergo risk validation through field surveys. The developer then considers the tolerability of the validated risks and develops and implements an appropriate MEC risk mitigation strategy based on actual site conditions. A risk framework provides a structured method to plan and operationalize the identification, evaluation, and mitigation of MEC risk throughout the development, operation, and maintenance life cycle of an offshore wind energy generation and transmission project.

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