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

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6135
Author(s):  
Jonas Aust ◽  
Antonija Mitrovic ◽  
Dirk Pons
Keyword(s):  
Visual Acuity ◽  
Statistical Analysis ◽  
Eye Tracking ◽  
Visual Inspection ◽  
Search Strategy ◽  
Aircraft Engine ◽  
Inspection Time ◽  
Defect Type ◽  
Inspection Performance ◽  
Different Levels

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.

scholarly journals A NEURAL-VISUALIZATION IDS FOR HONEYNET DATA

2012 ◽  
Vol 22 (02) ◽  
pp. 1250005 ◽  
Author(s):  
ÁLVARO HERRERO ◽  
URKO ZURUTUZA ◽  
EMILIO CORCHADO
Keyword(s):  
Intelligent Systems ◽  
Visual Inspection ◽  
False Positive Rate ◽  
Projection Methods ◽  
Neural Models ◽  
Detection Systems ◽  
Security Personnel ◽  
Real Domain ◽  
Positive Rate ◽  
Attack Patterns

Neural intelligent systems can provide a visualization of the network traffic for security staff, in order to reduce the widely known high false-positive rate associated with misuse-based Intrusion Detection Systems (IDSs). Unlike previous work, this study proposes an unsupervised neural models that generate an intuitive visualization of the captured traffic, rather than network statistics. These snapshots of network events are immensely useful for security personnel that monitor network behavior. The system is based on the use of different neural projection and unsupervised methods for the visual inspection of honeypot data, and may be seen as a complementary network security tool that sheds light on internal data structures through visual inspection of the traffic itself. Furthermore, it is intended to facilitate verification and assessment of Snort performance (a well-known and widely-used misuse-based IDS), through the visualization of attack patterns. Empirical verification and comparison of the proposed projection methods are performed in a real domain, where two different case studies are defined and analyzed.

A novel committee selection mechanism for combining classifiers to detect unsolicited emails

2016 ◽  
Vol 46 (4) ◽  
pp. 524-548 ◽  
Author(s):  
Shrawan Kumar Trivedi ◽  
Shubhamoy Dey
Keyword(s):  
False Positive ◽  
Classification Accuracy ◽  
False Positive Rate ◽  
Kernel Functions ◽  
Selection Mechanism ◽  
Combining Classifiers ◽  
Content Type ◽  
Positive Rate ◽  
Combined Classifier

Purpose The email is an important medium for sharing information rapidly. However, spam, being a nuisance in such communication, motivates the building of a robust filtering system with high classification accuracy and good sensitivity towards false positives. In that context, this paper aims to present a combined classifier technique using a committee selection mechanism where the main objective is to identify a set of classifiers so that their individual decisions can be combined by a committee selection procedure for accurate detection of spam. Design/methodology/approach For training and testing of the relevant machine learning classifiers, text mining approaches are used in this research. Three data sets (Enron, SpamAssassin and LingSpam) have been used to test the classifiers. Initially, pre-processing is performed to extract the features associated with the email files. In the next step, the extracted features are taken through a dimensionality reduction method where non-informative features are removed. Subsequently, an informative feature subset is selected using genetic feature search. Thereafter, the proposed classifiers are tested on those informative features and the results compared with those of other classifiers. Findings For building the proposed combined classifier, three different studies have been performed. The first study identifies the effect of boosting algorithms on two probabilistic classifiers: Bayesian and Naïve Bayes. In that study, AdaBoost has been found to be the best algorithm for performance boosting. The second study was on the effect of different Kernel functions on support vector machine (SVM) classifier, where SVM with normalized polynomial (NP) kernel was observed to be the best. The last study was on combining classifiers with committee selection where the committee members were the best classifiers identified by the first study i.e. Bayesian and Naïve bays with AdaBoost, and the committee president was selected from the second study i.e. SVM with NP kernel. Results show that combining of the identified classifiers to form a committee machine gives excellent performance accuracy with a low false positive rate. Research limitations/implications This research is focused on the classification of email spams written in English language. Only body (text) parts of the emails have been used. Image spam has not been included in this work. We have restricted our work to only emails messages. None of the other types of messages like short message service or multi-media messaging service were a part of this study. Practical implications This research proposes a method of dealing with the issues and challenges faced by internet service providers and organizations that use email. The proposed model provides not only better classification accuracy but also a low false positive rate. Originality/value The proposed combined classifier is a novel classifier designed for accurate classification of email spam.

Visual Inspection of Circuit Boards: Effect of Gender, Age, Defect type, and Defect Proportion

1998 ◽  
Vol 42 (16) ◽  
pp. 1161-1164
Author(s):  
J. Klamklay ◽  
R.R. Bishu
Keyword(s):  
Visual Inspection ◽  
Printed Circuit Boards ◽  
Basic Program ◽  
Inspection Time ◽  
Circuit Boards ◽  
Defect Type ◽  
Printed Circuit ◽  
Independent Variables ◽  
And Gender ◽  
Inspection Performance

Visual inspection of printed circuit boards was evaluated through a simulated experiment. Photographs of circuit boards were scanned into a PC and the images were manipulated using a Visual Basic program. The visual inspection performance measured reaction time in seconds and accuracy by measuring the number of incorrect responses given. The independent variables studied in this research were age, gender, defect type, board size, inspection pace, and proportion of defects. Forty subjects participated in this experiment. In summary, the study showed that defect proportion, defect type, age and gender do influence both inspection time and inspection accuracy. Perhaps the most interesting result is that rate of false alarm (decision 2) and misses (decision 1) depend on defect type and proportion. This has interesting ramification for quality professionals.

scholarly journals Tanimoto Gaussian Kernelized Feature Extraction Based Multinomial Gentleboost Machine Learning for Multi-Spectral Aerial Image Classification

2019 ◽  
Vol 8 (10S) ◽  
pp. 208-216
Keyword(s):  
Feature Extraction ◽  
False Positive ◽  
Classification Accuracy ◽  
False Positive Rate ◽  
Classification Performance ◽  
Extraction Time ◽  
Aerial Images ◽  
Aerial Image ◽  
Scene Classification ◽  
Positive Rate

Aerial images provide a landscape view of earth surfaces that utilized to monitor the large areas. Each Aerial image comprises the different scenes to identify the objects on the digital maps. The several methodologies have been developed to solve the problem of the scene classification using input aerial images. The method does not improve the classification performance using more aerial images. In order to improve the classification performance, a Tanimoto Gaussian Kernelized Feature Extraction Based Multinomial GentleBoost Classification (TGKFE-MGBC) technique is introduced. The TGKFE-MGBC technique comprises three major processes namely object-based segmentation, feature extraction and aerial image scene classification. At first, object-based segmentation partitions the aerial image into several sub-bands. Aerial image with more than two objects is called as multi-spectral. The objects in spectral bands are identified by Tanimoto pixel similarity measure. This process helps to reduce the feature extraction time. Each object has different features like shape, size, color, texture and so on. After that, Gaussian Kernelized Feature Extraction is carried out to extracts the features from the objects with minimal time. Finally, the Multinomial GentleBoost Classification is applied for categorizing the scenes into different classes with the extracted features. The GentleBoost is an ensemble technique uses multinomial naïve Bayes probabilistic classifier as a weak learner and it combines to makes a strong one for classifying the scenes. The strong classifier result improves the aerial image scene classification accuracy and minimizes the false positive rate. Simulation is conducted using aerial image database with different factors such as feature extraction time, aerial image scene classification accuracy and false positive rate. The results showed that the TGKFE-MGBC technique effectively improves the aerial image scene classification accuracy and minimizes the feature extraction time as well as the false positive rate.

scholarly journals LNNLS-KH: A Feature Selection Method for Network Intrusion Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Xin Li ◽  
Peng Yi ◽  
Wei Wei ◽  
Yiming Jiang ◽  
Le Tian
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
False Positive Rate ◽  
Optimal Solution ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Krill Herd ◽  
Positive Rate

As an important part of intrusion detection, feature selection plays a significant role in improving the performance of intrusion detection. Krill herd (KH) algorithm is an efficient swarm intelligence algorithm with excellent performance in data mining. To solve the problem of low efficiency and high false positive rate in intrusion detection caused by increasing high-dimensional data, an improved krill swarm algorithm based on linear nearest neighbor lasso step (LNNLS-KH) is proposed for feature selection of network intrusion detection. The number of selected features and classification accuracy are introduced into fitness evaluation function of LNNLS-KH algorithm, and the physical diffusion motion of the krill individuals is transformed by a nonlinear method. Meanwhile, the linear nearest neighbor lasso step optimization is performed on the updated krill herd position in order to derive the global optimal solution. Experiments show that the LNNLS-KH algorithm retains 7 features in NSL-KDD dataset and 10.2 features in CICIDS2017 dataset on average, which effectively eliminates redundant features while ensuring high detection accuracy. Compared with the CMPSO, ACO, KH, and IKH algorithms, it reduces features by 44%, 42.86%, 34.88%, and 24.32% in NSL-KDD dataset, and 57.85%, 52.34%, 27.14%, and 25% in CICIDS2017 dataset, respectively. The classification accuracy increased by 10.03% and 5.39%, and the detection rate increased by 8.63% and 5.45%. Time of intrusion detection decreased by 12.41% and 4.03% on average. Furthermore, LNNLS-KH algorithm quickly jumps out of the local optimal solution and shows good performance in the optimal fitness iteration curve, convergence speed, and false positive rate of detection.

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

Aerospace ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 18
Author(s):  
Jonas Aust ◽  
Dirk Pons ◽  
Antonija Mitrovic
Keyword(s):  
Eye Tracking ◽  
Visual Inspection ◽  
Influence Factors ◽  
Aircraft Engine ◽  
Detection Rates ◽  
Severity Level ◽  
Discrimination Ability ◽  
Inspection Process ◽  
Part Inspection ◽  
Inspection Performance

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.

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.

Improving diagnosis of acute appendicitis with atypical findings by Tc-99m HMPAO leukocyte scan

2002 ◽  
Vol 41 (01) ◽  
pp. 37-41 ◽  
Author(s):  
S. Shung-Shung ◽  
S. Yu-Chien ◽  
Y. Mei-Due ◽  
W. Hwei-Chung ◽  
A. Kao
Keyword(s):  
Acute Appendicitis ◽  
False Positive ◽  
False Positive Rate ◽  
Accurate Method ◽  
Clinical Findings ◽  
Pathological Findings ◽  
Lower Quadrant ◽  
Predictive Values ◽  
Positive Rate

Summary Aim: Even with careful observation, the overall false-positive rate of laparotomy remains 10-15% when acute appendicitis was suspected. Therefore, the clinical efficacy of Tc-99m HMPAO labeled leukocyte (TC-WBC) scan for the diagnosis of acute appendicitis in patients presenting with atypical clinical findings is assessed. Patients and Methods: Eighty patients presenting with acute abdominal pain and possible acute appendicitis but atypical findings were included in this study. After intravenous injection of TC-WBC, serial anterior abdominal/pelvic images at 30, 60, 120 and 240 min with 800k counts were obtained with a gamma camera. Any abnormal localization of radioactivity in the right lower quadrant of the abdomen, equal to or greater than bone marrow activity, was considered as a positive scan. Results: 36 out of 49 patients showing positive TC-WBC scans received appendectomy. They all proved to have positive pathological findings. Five positive TC-WBC were not related to acute appendicitis, because of other pathological lesions. Eight patients were not operated and clinical follow-up after one month revealed no acute abdominal condition. Three of 31 patients with negative TC-WBC scans received appendectomy. They also presented positive pathological findings. The remaining 28 patients did not receive operations and revealed no evidence of appendicitis after at least one month of follow-up. The overall sensitivity, specificity, accuracy, positive and negative predictive values for TC-WBC scan to diagnose acute appendicitis were 92, 78, 86, 82, and 90%, respectively. Conclusion: TC-WBC scan provides a rapid and highly accurate method for the diagnosis of acute appendicitis in patients with equivocal clinical examination. It proved useful in reducing the false-positive rate of laparotomy and shortens the time necessary for clinical observation.

Predicting Fetal Chromosome Anomalies in the First Trimester Using Pregnancy Associated Plasma Protein-A: A Comparison of Statistical Methods

1993 ◽  
Vol 32 (02) ◽  
pp. 175-179 ◽  
Author(s):  
B. Brambati ◽  
T. Chard ◽  
J. G. Grudzinskas ◽  
M. C. M. Macintosh
Keyword(s):  
Logistic Regression ◽  
General Population ◽  
Likelihood Ratio ◽  
False Positive ◽  
False Positive Rate ◽  
Ratio Method ◽  
Detection Rates ◽  
Gaussian Distributions ◽  
Positive Rate ◽  
Likelihood Ratio Method

Abstract:The analysis of the clinical efficiency of a biochemical parameter in the prediction of chromosome anomalies is described, using a database of 475 cases including 30 abnormalities. A comparison was made of two different approaches to the statistical analysis: the use of Gaussian frequency distributions and likelihood ratios, and logistic regression. Both methods computed that for a 5% false-positive rate approximately 60% of anomalies are detected on the basis of maternal age and serum PAPP-A. The logistic regression analysis is appropriate where the outcome variable (chromosome anomaly) is binary and the detection rates refer to the original data only. The likelihood ratio method is used to predict the outcome in the general population. The latter method depends on the data or some transformation of the data fitting a known frequency distribution (Gaussian in this case). The precision of the predicted detection rates is limited by the small sample of abnormals (30 cases). Varying the means and standard deviations (to the limits of their 95% confidence intervals) of the fitted log Gaussian distributions resulted in a detection rate varying between 42% and 79% for a 5% false-positive rate. Thus, although the likelihood ratio method is potentially the better method in determining the usefulness of a test in the general population, larger numbers of abnormal cases are required to stabilise the means and standard deviations of the fitted log Gaussian distributions.

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