Assessment Of Defect Severity Research Articles

Defect Severity Assessment Model for Sewer Pipeline Based on Automated Pipe Calibration

To address the low-efficiency issue of the manual assessment method for sewer pipe defects, we propose a defect severity assessment model based on automated pipe calibration (DSA-APC), which can provide automated and quantitative assessments. First, the cross-section feature is extracted by automated pipe calibration. A pipe cross-section feature extraction algorithm based on restricted Hough gradient transform (RHGT) is proposed. Then, a fine-defect feature extraction method based on edge detection is proposed to extract the features of pipe defects more finely. Finally, according to the assessment standards of the sewer pipe defect, a defect severity assessment table is constructed, and the area ratio of the defect feature and cross-section feature is used to evaluate the severity. Experiments are carried out on the Songbai data set and Level-sewer10 data set. The average absolute deviation of the DSA-APC model is 2.008%, and the average accuracy is 86.73%. The experimental results show that the DSA-APC model can correctly evaluate the severity level of sewer pipe defects, which has a good practical application value.

Bearing defect size assessment using wavelet transform based Deep Convolutional Neural Network (DCNN)

Bearing, an importunate component of any rotary machinery, is jeopardized to its failure during its operation in tough working conditions. The condition monitoring of bearing, to avoid its unforeseen failure, is important for its smooth working. Bearing damage assessment is mostly done by selecting features from the vibration signals, which is usually, a time consuming process. Consequently, it becomes importunate for us to achieve full automation for the safety purpose and reduction in the maintenance cost of the machinery. Towards this omnifarious effort, a wavelet transformed based Deep Convolutional Neural Network (DCNN) is proposed for the automatic identification of defective components and damage assessment of bearing, which is achieved by, firstly, processing vibration signals using continuous wavelet transform to form 2D grey scale images of time-frequency representation. Secondly, DCNN is trained using images for learning of defects severity. Through convolution and pooling operation layers, high level features are automatically extracted from images itself. Thereafter, trained 2D grey images are applied to DCNN so that defect severity assessment can be accurately carried out. The overall accuracy achieved using the proposed method is 100%.

Bearing defects diagnostics using the principal components analysis

In this paper a new method of bearing defects detecting and defect severity assessment based on the results of singular spectrum decomposition (SSD) of the vibration signal is proposed. The presented results of equipment diagnostics in the laboratory conditions have demonstrated the applicability of signal representation in the principal component basis to improve diagnostic sensitivity.

Body dysmorphia in dentistry and prosthodontics: A practice based study

The prevalence of Body Dysmorphic Disorder (BDD) and dysmorphic concern in dentistry and prosthodontics have not been properly assessed, yet the mouth and the teeth are amongst the top preoccupation for these individuals. ObjectivesTo evaluate the prevalence of dysmorphic symptoms and BDD using validated tools in patients presenting to general and specialist prosthodontic practice. MethodsPatients were recruited by two prosthodontics practices and three general dentist practices. Patients were given a Dysmorphic Concern Questionnaire (DCQ) integrated into a medical history form. Treating clinicians also completed a Baseline Rating Form assessing the patients´ reason for presentation. Two DCQ score cut-offs were used (9 and 12). ResultsTwo hundred and thirteen patients were recruited, the majority of patients showed DCQ scores below 9 (84%). The prevalence of BDD was 7% (cut-off ≥ 9) or 4% (cut-off ≥12). Women were more likely to present with high DCQ scores as well as people with a history of mental health problems. The cosmetic practice, the implant clinic, and the prosthodontic practice received a higher proportion of patients with high DCQ scores when compared with the general family practice. The type of procedure was not related to DCQ scores. The defect severity assessment and whether this was amenable to correction was moderately correlated with DCQ scores. ConclusionsThe DCQ seems to be a suitable tool to be used by dentists as part of history taking and patient examination due to its brevity, simplicity and the good sensitivity/specificity reported in the literature. Clinical significanceIdentifying patients with dysmorphia is important before irreversible treatment is carried out due to high levels of dissatisfaction, poor patient centred outcomes and the question of whether these individuals have the capacity to consent.

Fault Detection Using Vibration Signal Analysis Of Rolling Element Bearing In Time Domain using an Innovative Time Scalar Indicator

Rolling element bearings are critical components of rotating mechanisms and the presence of defects may cause catastrophic failure. The early identification of such defects with the severity of damage under operating may avoid malfunctioning and breakdown. Defective bearings are the source of vibration and its signals can be used to assess the faulty bearings. The diagnosis of fault and assessment of defect severity using fault scalar indicator extracted from the time domain signal through increase in size has been presented. Six time scalar indicators, namely peak, RMS, crest factor, kurtosis, impulse factor, and shape factor have been considered. The results obtained from these fault indicators for assessing fault severity using simulation have been compared and found that they can be used for early prediction. A new scalar indicator has been developed to improve the diagnosis of defect up to a point of bearing failure and helps to take necessary action to avoid the failure. [Received 31 August 2016; Accepted 06 February 2017]

Fault detection using vibration signal analysis of rolling element bearing in time domain using an innovative time scalar indicator

Rolling element bearings are critical components of rotating mechanisms and the presence of defects may cause catastrophic failure. The early identification of such defects with the severity of damage under operating may avoid malfunctioning and breakdown. Defective bearings are the source of vibration and its signals can be used to assess the faulty bearings. The diagnosis of fault and assessment of defect severity using fault scalar indicator extracted from the time domain signal through increase in size has been presented. Six time scalar indicators, namely peak, RMS, crest factor, kurtosis, impulse factor, and shape factor have been considered. The results obtained from these fault indicators for assessing fault severity using simulation have been compared and found that they can be used for early prediction. A new scalar indicator has been developed to improve the diagnosis of defect up to a point of bearing failure and helps to take necessary action to avoid the failure. [Received 31 August 2016; Accepted 06 February 2017]

Characterization of defects using ultrasonic arrays: a dynamic classifier approach.

In the field of nondestructive evaluation, accurate characterization of defects is required for the assessment of defect severity. Defect characterization is studied in this paper through the use of the ultrasonic scattering matrix, which can be extracted from the array measurements. Defects that have different shapes are classified into different defect classes, and this essentially allows us to distinguish between crack-like defects and volumetric voids. Principal component analysis (PCA) is used for feature extraction, and several representational principal component subsets are found through exhaustive searching in which quadratic discriminant analysis (QDA) and support vector machine (SVM) are used as the pattern classifiers. Instead of choosing a single optimal classifier, the best classifier is dynamically selected for different measurements by estimating the local classifier accuracy. The proposed approach is validated in simulation and experiments. In simulation, the depths (lengths of the minor axes) of 4441 out of 4636 test samples are measured accurately, and the measurement errors (with respect to the defect size) are below 10%. Arbitrarily shaped rough volumetric defects are identified as ellipses, which are reasonably good matches in shape to the original defects. Experimentally, six subwavelength scatterers are characterized and sized to within 0.14λ.

Combining thermography and computer simulation to identify and assess insulation defects in the construction of building façades

Thermography is a non-destructive testing technique used to evaluate the thermal performance of buildings, typically post-construction. However, establishing objective criteria for the interpretation of test results is not straightforward and the process can appear subjective. In building design, computer modelling may be used to analyse the thermal performance of construction details, including the expected distribution of surface temperatures at junctions and openings. The objective of this paper is to explore how the two techniques may be used together to support the identification and assessment of insulation defects in the construction of building façades. Firstly, a literature review identifies the main parameters relevant to modelling heat transfer through construction details and also the parameters that influence the assessment of surface temperatures by thermography. Combining the two techniques can support thermal image interpretation and the assessment of defect severity. Procedures to ensure a consistent approach between the two techniques are developed. Two case studies demonstrate the application of these procedures and illustrate the complementary use of modelling and thermography in a practical context. The approach discussed in the paper could help to verify the as-built energy performance of new buildings by linking design predictions of thermal performance with thermographic testing.

Severity Assessment of Software Defect Reports using Text Classification

Defect severity assessment is essential in order to allocate testing resources and effectively plan testing activities. In this paper, we use text classification techniques to predict and assess the severity of defects. The results are based on defect description of issue requirements obtained from NASA project. We have used Support Vector Machine technique to predict defect severity from issue reports.

Recurrence plot entropy for machine defect severity assessment

This paper presents a nonlinear time series analysis technique for evaluating machine defect severity, based on the Recurrence Plot (RP) entropy. The RP entropy is calculated from the probability distribution of the diagonal line length in the recurrence plot, which graphically depicts a system's dynamics and provides a global picture of the autocorrelation in a time series over all available time-scales. Results of experimental studies conducted on a spindle-bearing test bed have demonstrated that, as the working condition of the bearing deteriorates due to the initiation and/or progression of structural damages, the frequency information contained in the vibration signal becomes increasingly complex, leading to the increase of the RP entropy. As a result, RP entropy can serve as an effective indicator for defect severity assessment of rolling bearings.

Rolling bearing defect severity assessment under varying operating conditions

This paper investigates a multilayer feed-forward neural network structure for automated bearing defect severity assessment under varying operating conditions. A bearing health index was proposed, based on the Weibull theory. Defect-related frequency features were used as inputs to the neural network. The neural network was trained to establish relationship between defect characteristic and bearing conditions, and outputs specific a health index value corresponding the defect severity. Experiments have shown that the neural network was effective in differentiating faulty bearings from a 'healthy' bearing, with a 99% and 97% success rate for classifying defects in the inner and outer raceways, respectively.

The continuing importance of thyroid scintigraphy in the era of high-resolution ultrasound.

At the molecular level, the uptake of radioiodine and pertechnetate is proportional to the expression of the thyroidal sodium/iodine symporter (NIS). Qualitative and quantitative scintigraphic evaluation of the thyroid is performed with a gamma camera fitted with an on-line computer system and enables determination of the iodine uptake or the technetium uptake (TCTU) as an iodine clearance equivalent. Despite new molecular genetic insights into congenital hypothyroidism, the iodine-123 or pertechnetate scan remains the most accurate test for the detection of ectopic thyroid tissue. Following the identification of specific mutations of the genes coding for the NIS, thyroid peroxidase and pendrin, the discharge test has lost its role in establishing the diagnosis of inherited dyshormonogenesis, but it is still of value in the assessment of defect severity. In PDS mutations the test can be used to establish the diagnosis of syndromic disease. Quantitative pertechnetate scintigraphy is the most sensitive and specific technique for the diagnosis and quantification of thyroid autonomy. The method has proved to be valuable in risk stratification of spontaneous or iodine-induced hyperthyroidism, in the estimation of the target volume prior to radioiodine therapy and in the evaluation of therapeutic success after definitive treatment. In iodine deficiency areas the thyroid scan remains indispensable for the functional characterisation of a thyroid nodule and is still a first-line diagnostic procedure in cases of suspected thyroid malignancy. This is especially of importance in patients with Graves' disease, among whom a relatively high prevalence of cancer has been found in cold thyroid nodules. While determination of the TCTU is without any value in the differentiation between autoimmune thyroiditis and Graves' disease in most cases, it is of substantial importance in the differentiation between hyperthyroid autoimmune thyroiditis and Graves' disease.

Nondestructive evaluation of steel structures using a superconducting quantum interference device magnetometer and a neural network system

This work combines two state-of-the-art techniques in the area of magnetic nondestructive evaluation: the application of the superconducting quantum interference device (SQUID) as the magnetic field sensor; and the use of artificial neural networks as analysis tools applied to the detected magnetic signals. Pioneering measurements using the SQUID sensor have been made in steel samples containing various types of flaws, and a neural network system, based on the time-delay neural network and radial basis function algorithms, has been implemented to characterize the flaws. The neural network system aims to, based on the measured magnetic field, provide information about defect geometry, thus allowing the assessment of defect severity, as a basis for maintenance and repair procedures.

Reproducibility of polar map generation and assessment of defect severity and extent assessment in myocardial perfusion imaging using positron emission tomography.

The purpose of this study was to determine the reliability of new software developed for the analysis of cardiac tomographic data. The algorithm delineates the long axis and defines the basal plane and subsequently generates polar maps to quantitatively and reproducibly assess the size and severity of perfusion defects. The developed technique requires an initial manual estimate of the left ventricular long axis and calculates the volumetric maximum myocardial activity distribution. This surface is used to map three-dimensional tracer accumulation onto a two-dimensional representation (polar map), which is the basis for further processing. The spatial information is used to compute geometrical and mechanical properties of a solid model of the left ventricle including the left heart chamber. A new estimate of the axis is determined from this model, and the previously outlined procedure is repeated together with an automated definition of the valve plane until differences between the polar maps can be neglected. This quantitative analysis software was validated in phantom studies with defects of known masses and in ten data sets from normals and patients with coronary artery disease of various severity. We investigated the reproducibility of the maps with the introduction of a similarity criterion where the ratio of two corresponding polar map elements lies within a 10% interval. The maps were also used to measure intra-and interobserver variability in respect of defect size and severity. In the phantom studies, it was possible to reliably assess mass information over a wide range of defects from 5 to 60 g (slope: 1.02, offset -0.68, r = 0.972). Patient studies revealed a statistically significant increase in the reproducibility of the automatic technique compared with the manual approach: 54%+/-19% (manual) compared with 88%+/-9% (automatic) for observer 1 and 61%+/-20% vs 82%+/-5% for observer 2, respectively. The intervariability analysis showed a significant improvement from 59%+/-14% to 83%+/-7% in similar polar map elements and a significantly improved correlation in the calculation of severity (from r = 0.908 to 0.989) and extent (from r = 0.963 to r = 0.992) of the perfusion defects when the automated procedure was applied. It is concluded that, assuming a constant wall thickness and tissue density, absolute defect mass can be reliably estimated. Furthermore, the proposed software demonstrates a significant improvement in the generation of volumetric polar maps for the quantitative assessment of perfusion defects.

Comparison between rest thallium 201 and technetium 99m-labeled tetrofosmin imanging for assessment of defect severity after myocardial infarction

Comparison between rest thallium 201 and technetium 99m-labeled tetrofosmin imanging for assessment of defect severity after myocardial infarction

Influence of the assessment of defect severity and intravenous nitrate administration during tracer injection on the detection of viable hibernating myocardium with data-based quantitative technetium 99m-labeled sestamibi single-photon emission computed tomography

Background. This study aimed to verify whether the assessment of defect severity and the infusion of nitrates during tracer injection improve the capability of data-based 99mTc-labeled sestamibi single-photon emission computed tomography (SPECT) to recognize hibernating myocardium. Methods and Results. Of 66 asynergic coronary territories in 40 patients with left ventricular dysfunction, 28 had postrevascularization functional recovery (hibernating) and 38 had unchanged dysfunction (fibrotic). Defect severity was lower in the hibernating than in the fibrotic territories on both baseline ( p < 0.01) and nitrate SPECT ( p < 0.002). Nitrate was superior to baseline SPECT to differentiate the hibernating from the fibrotic territories (sensitivity 96% vs 75%, p < 0.05; receiver-operating characteristic curve area 0.75 vs 0.63, p < 0.001) and to identify the patients with improved left ventricular ejection fraction (receiver-operating characteristic curve area 0.68 vs 0.58; p < 0.05). Conclusions. The analysis of defect severity in combination with nitrate infusion clearly improves the value of 99mTc-labeled sestamibi SPECT for the recognition of hibernating myocardium and the prediction of postrevascularization recovery.

The application of vibration signature analysis and acoustic emission source location to on-line condition monitoring of anti-friction bearings

The mechanical noise, vibration and heat generated by defects in fabrications, process plant and machinery are a measure of component condition, and once characterised allow on-line assessment of defect severity. Unexpected failures which might threaten the entire production process can thereby be avoided. There is an increasing need for reliable and robust condition monitoring instrumentation with on-line data processing, particularly as the trend contunues towards larger production units with little or no standby capacity for use in the event of unscheduled stoppages due to mechanical failure. This article describes the detection of incipient failure of rolling element bearings by kurtosis and the location of fatigue cracks in slowly rotating bearings by acoustic emission