Effects of Capacitor Bank Defects on Harmonic Distortion and Park's Pattern Analysis in Induction Motors

Properly sized capacitor banks are connected across induction motors for several reasons including power factor correction, reducing distortions, increasing capacity, etc. Total harmonic distortion (THD) and power factor (PF) are used in such cases to quantify the improvements obtained through connection of the external capacitor banks. On the other hand, one of the methods for assessing the motor internal condition is by the use of Park-s pattern analysis. In spite of taking adequate precautionary measures, the capacitor banks may sometimes malfunction. Such a minor fault in the capacitor bank is often not apparently discernible. This may however, give rise to substantial degradation of power factor correction performance and may also damage the supply profile. The case is more severe with the fact that the Park-s pattern gets distorted due to such external capacitor faults, and can give anomalous results about motor internal fault analyses. The aim of this paper is to present simulation and hardware laboratory test results to have an understanding of the anomalies in harmonic distortion and Park-s pattern analyses in induction motors due to capacitor bank defects.

Physicochemical Characterization of MFI–Ceramic Hollow Fibres Membranes for CO2 Separation with Alkali Metal Cation

This paper present some preliminary work on the preparation and physicochemical caracterization of nanocomposite MFI-alumina structures based on alumina hollow fibres. The fibers are manufactured by a wet spinning process. α-alumina particles were dispersed in a solution of polysulfone in NMP. The resulting slurry is pressed through the annular gap of a spinneret into a precipitation bath. The resulting green fibres are sintered. The mechanical strength of the alumina hollow fibres is determined by a three-point-bending test while the pore size is characterized by bubble-point testing. The bending strength is in the range of 110 MPa while the average pore size is 450 nm for an internal diameter of 1 mm and external diameter of 1.7 mm. To characterize the MFI membranes various techniques were used for physicochemical characterization of MFI–ceramic hollow fibres membranes: The nitrogen adsorption, X-ray diffractometry, scanning electron microscopy combined with X emission microanalysis. Scanning Electron Microscopy (SEM) and Energy Dispersive Microanalysis by the X-ray were used to observe the morphology of the hollow fibre membranes (thickness, infiltration into the carrier, defects, homogeneity). No surface film, has been obtained, as observed by SEM and EDX analysis and confirmed by high temperature variation of N2 and CO2 gas permeances before cation exchange. Local analysis and characterise (SEM and EDX) and overall (by ICP elemental analysis) were conducted on two samples exchanged to determine the quantity and distribution of the cation of cesium on the cross section fibre of the zeolite between the cavities.

The Effects of Software Size on Development Effort and Software Quality

Effective evaluation of software development effort is an important issue during project plan. This study provides a model to predict development effort based on the software size estimated with function points. We generalize the average amount of effort spent on each phase of the development, and give the estimates for the effort used in software building, testing, and implementation. Finally, this paper finds a strong correlation between software defects and software size. As the size of software constantly increases, the quality remains to be a matter which requires major concern.

Development of a Porous Silica Film by Sol-gel Process

In the present work homogeneous silica film on silicon was fabricated by colloidal silica sol. The silica sol precursor with uniformly granular particle was derived by the alkaline hydrolysis of tetraethoxyorthosilicate (TEOS) in presence of glycerol template. The film was prepared by dip coating process. The templated hetero-structured silica film was annealed at elevated temperatures to generate nano- and meso porosity in the film. The film was subsequently annealed at different temperatures to make it defect free and abrasion resistant. The sol and the film were characterized by the measurement of particle size distribution, scanning electron microscopy, XRD, FTIR spectroscopy, transmission electron microscopy, atomic force microscopy, measurement of the refractive index, thermal conductivity and abrasion resistance. The porosity of the films decreased whereas refractive index and dielectric constant of it `increased with the increase in the annealing temperature. The thermal conductivity of the films increased with the increase in the film thickness. The developed porous silica film holds strong potential for use in different areas.

Real-time Laser Monitoring based on Pipe Detective Operation

The pipe inspection operation is the difficult detective performance. Almost applications are mainly relies on a manual recognition of defective areas that have carried out detection by an engineer. Therefore, an automation process task becomes a necessary in order to avoid the cost incurred in such a manual process. An automated monitoring method to obtain a complete picture of the sewer condition is proposed in this work. The focus of the research is the automated identification and classification of discontinuities in the internal surface of the pipe. The methodology consists of several processing stages including image segmentation into the potential defect regions and geometrical characteristic features. Automatic recognition and classification of pipe defects are carried out by means of using an artificial neural network technique (ANN) based on Radial Basic Function (RBF). Experiments in a realistic environment have been conducted and results are presented.

Evaluation of Housing Defects in Build-Then- Sell Houses: A Study of Six Residential Areas

The Build-Then-Sell (BTS) is a novel system implemented in Malaysia after a number of complaints have been received from buyers in the preceding Sell-Then-Build (STB) system. When the government announced the implementation of the BTS in 2007, proponents of BTS have asserted that the new system will provide houses with low level of defects. Their argument however is not supported by any empirical data. Hence, this study is conducted to evaluate the housing defects in BTS houses. Six BTS residential areas have been surveyed to collect the defects data. Questionnaires were administered directly to the occupiers in each of the BTS houses through door-to-door visits. The result has shown that the rate of defects for the six residential areas is ranged from minor to slight, which only affect the aesthetic value of the house.

DRE - A Quality Metric for Component based Software Products

The overriding goal of software engineering is to provide a high quality system, application or a product. To achieve this goal, software engineers must apply effective methods coupled with modern tools within the context of a mature software process [2]. In addition, it is also must to assure that high quality is realized. Although many quality measures can be collected at the project levels, the important measures are errors and defects. Deriving a quality measure for reusable components has proven to be challenging task now a days. The results obtained from the study are based on the empirical evidence of reuse practices, as emerged from the analysis of industrial projects. Both large and small companies, working in a variety of business domains, and using object-oriented and procedural development approaches contributed towards this study. This paper proposes a quality metric that provides benefit at both project and process level, namely defect removal efficiency (DRE).

Risk Factors for Defective Autoparts Products Using Bayesian Method in Poisson Generalized Linear Mixed Model

This research investigates risk factors for defective products in autoparts factories. Under a Bayesian framework, a generalized linear mixed model (GLMM) in which the dependent variable, the number of defective products, has a Poisson distribution is adopted. Its performance is compared with the Poisson GLM under a Bayesian framework. The factors considered are production process, machines, and workers. The products coded RT50 are observed. The study found that the Poisson GLMM is more appropriate than the Poisson GLM. For the production Process factor, the highest risk of producing defective products is Process 1, for the Machine factor, the highest risk is Machine 5, and for the Worker factor, the highest risk is Worker 6.

A Contribution to 3D Modeling of Manufacturing Tolerance Optimization

The study of the generated defects on manufactured parts shows the difficulty to maintain parts in their positions during the machining process and to estimate them during the pre-process plan. This work presents a contribution to the development of 3D models for the optimization of the manufacturing tolerances. An experimental study allows the measurement of the defects of part positioning for the determination of ε and the choice of an optimal setup of the part. An approach of 3D tolerance based on the small displacements method permits the determination of the manufacturing errors upstream. A developed tool, allows an automatic generation of the tolerance intervals along the three axes.

2D Gabor Functions and FCMI Algorithm for Flaws Detection in Ultrasonic Images

In this paper we present a new approach to detecting a flaw in T.O.F.D (Time Of Flight Diffraction) type ultrasonic image based on texture features. Texture is one of the most important features used in recognizing patterns in an image. The paper describes texture features based on 2D Gabor functions, i.e., Gaussian shaped band-pass filters, with dyadic treatment of the radial spatial frequency range and multiple orientations, which represent an appropriate choice for tasks requiring simultaneous measurement in both space and frequency domains. The most relevant features are used as input data on a Fuzzy c-mean clustering classifier. The classes that exist are only two: 'defects' or 'no defects'. The proposed approach is tested on the T.O.F.D image achieved at the laboratory and on the industrial field.

Improving Injection Moulding Processes Using Experimental Design

Moulded parts contribute to more than 70% of components in products. However, common defects particularly in plastic injection moulding exist such as: warpage, shrinkage, sink marks, and weld lines. In this paper Taguchi experimental design methods are applied to reduce the warpage defect of thin plate Acrylonitrile Butadiene Styrene (ABS) and are demonstrated in two levels; namely, orthogonal arrays of Taguchi and the Analysis of Variance (ANOVA). Eight trials have been run in which the optimal parameters that can minimize the warpage defect in factorial experiment are obtained. The results obtained from ANOVA approach analysis with respect to those derived from MINITAB illustrate the most significant factors which may cause warpage in injection moulding process. Moreover, ANOVA approach in comparison with other approaches like S/N ratio is more accurate and with the interaction of factors it is possible to achieve higher and the better outcomes.

Resistive Switching in TaN/AlNx/TiN Cell

Resistive switching of aluminum nitride (AlNx) thin film was demonstrated in a TaN/AlNx/TiN memory cell that was prepared by sputter deposition techniques. The memory cell showed bipolar switching of resistance between +3.5 V and –3.5 V. The resistance ratio of high resistance state (HRS) to low resistance state (HRS), RHRS/RLRS, was about 2 over 100 cycles of endurance test. Both the LRS and HRS of the memory cell exhibited ohmic conduction at low voltages and Poole-Frenkel emission at high voltages. The electrical conduction in the TaN/AlNx/TiN memory cell was possibly attributed to the interactions between charges and defects in the AlNx film.

Enhanced Performance for Support Vector Machines as Multiclass Classifiers in Steel Surface Defect Detection

Steel surface defect detection is essentially one of pattern recognition problems. Support Vector Machines (SVMs) are known as one of the most proper classifiers in this application. In this paper, we introduce a more accurate classification method by using SVMs as our final classifier of the inspection system. In this scheme, multiclass classification task is performed based on the "one-againstone" method and different kernels are utilized for each pair of the classes in multiclass classification of the different defects. In the proposed system, a decision tree is employed in the first stage for two-class classification of the steel surfaces to "defect" and "non-defect", in order to decrease the time complexity. Based on the experimental results, generated from over one thousand images, the proposed multiclass classification scheme is more accurate than the conventional methods and the overall system yields a sufficient performance which can meet the requirements in steel manufacturing.

Functionalization of Carbon Nanotubes Using Nitric Acid Oxidation and DBD Plasma

In this study, multiwall carbon nanotubes (MWNTs) were modified with nitric acid chemically and by dielectric barrier discharge (DBD) plasma in an oxygen-based atmosphere. Used carbon nanotubes (CNTs) were prepared by chemical vapour deposition (CVD) floating catalyst method. For removing amorphous carbon and metal catalyst, MWNTs were exposed to dry air and washed with hydrochloric acid. Heating purified CNTs under helium atmosphere caused elimination of acidic functional groups. Fourier transformed infrared spectroscopy (FTIR) shows formation of oxygen containing groups such as C=O and COOH. Brunauer, Emmett, Teller (BET) analysis revealed that functionalization causes generation of defects on the sidewalls and opening of the ends of CNTs. Results of temperature-programmed desorption (TPD) and gas chromatography(GC) indicate that nitric acid treatment create more acidic groups than plasma treatment.

Six Sigma Solutions and its Benefit-Cost Ratio for Quality Improvement

This is an application research presenting the improvement of production quality using the six sigma solutions and the analyses of benefit-cost ratio. The case of interest is the production of tile-concrete. Such production has faced with the problem of high nonconforming products from an inappropriate surface coating and had low process capability based on the strength property of tile. Surface coating and tile strength are the most critical to quality of this product. The improvements followed five stages of six sigma solutions. After the improvement, the production yield was improved to 80% as target required and the defective products from coating process was remarkably reduced from 29.40% to 4.09%. The process capability based on the strength quality was increased from 0.87 to 1.08 as customer oriented. The improvement was able to save the materials loss for 3.24 millions baht or 0.11 million dollars. The benefits from the improvement were analyzed from (1) the reduction of the numbers of non conforming tile using its factory price for surface coating improvement and (2) the materials saved from the increment of process capability. The benefit-cost ratio of overall improvement was high as 7.03. It was non valuable investment in define, measure, analyses and the initial of improve stages after that it kept increasing. This was due to there were no benefits in define, measure, and analyze stages of six sigma since these three stages mainly determine the cause of problem and its effects rather than improve the process. The benefit-cost ratio starts existing in the improve stage and go on. Within each stage, the individual benefitcost ratio was much higher than the accumulative one as there was an accumulation of cost since the first stage of six sigma. The consideration of the benefit-cost ratio during the improvement project helps make decisions for cost saving of similar activities during the improvement and for new project. In conclusion, the determination of benefit-cost ratio behavior through out six sigma implementation period provides the useful data for managing quality improvement for the optimal effectiveness. This is the additional outcome from the regular proceeding of six sigma.

Yield Prediction Using Support Vectors Based Under-Sampling in Semiconductor Process

It is important to predict yield in semiconductor test process in order to increase yield. In this study, yield prediction means finding out defective die, wafer or lot effectively. Semiconductor test process consists of some test steps and each test includes various test items. In other world, test data has a big and complicated characteristic. It also is disproportionably distributed as the number of data belonging to FAIL class is extremely low. For yield prediction, general data mining techniques have a limitation without any data preprocessing due to eigen properties of test data. Therefore, this study proposes an under-sampling method using support vector machine (SVM) to eliminate an imbalanced characteristic. For evaluating a performance, randomly under-sampling method is compared with the proposed method using actual semiconductor test data. As a result, sampling method using SVM is effective in generating robust model for yield prediction.

Investigation of Self-Similarity Solution for Wake Flow of a Cylinder

The data measurement of mean velocity has been taken for the wake of single circular cylinder with three different diameters for two different velocities. The effects of change in diameter and in velocity are studied in self-similar coordinate system. The spatial variations of velocity defect and that of the half-width have been investigated. The results are compared with those published by H.Schlichting. In the normalized coordinates, it is also observed that all cases except for the first station are self-similar. By attention to self-similarity profiles of mean velocity, it is observed for all the cases at the each station curves tend to zero at a same point.

Laser Welded Ni-Cr Dental Alloys Inspection

Minor problems arising from optimizations by welding of fixed prostheses frameworks can be identified by macroscopic and microscopic visual inspection. The purpose of this study was to highlight the visible discontinuities present in the laser welds of dental Ni-Cr alloys. Ni-Cr base metal alloys designated for fixed prostheses manufacture were selected for the experiments. Using cast plates, preliminary tests were conducted by laser welding. Macroscopic visual inspection was done carefully to assess the defects of the welding rib. Electron microscopy images allowed visualization of small discontinuities, which escapes visual inspection. Making comparison to Ni-Cr alloys taken in the experiment and laser welded, after visual analysis, the best welds appear for Heraenium NA alloy.

Trispectral Analysis of Voiced Sounds Defective Audition and Tracheotomisian Cases

This paper presents the cepstral and trispectral analysis of a speech signal produced by normal men, men with defective audition (deaf, deep deaf) and others affected by tracheotomy, the trispectral analysis based on parametric methods (Autoregressive AR) using the fourth order cumulant. These analyses are used to detect and compare the pitches and the formants of corresponding voiced sounds (vowel \a\, \i\ and \u\). The first results appear promising, since- it seems after several experimentsthere is no deformation of the spectrum as one could have supposed it at the beginning, however these pathologies influenced the two characteristics: The defective audition influences to the formants contrary to the tracheotomy, which influences the fundamental frequency (pitch).

Classification of Defects by the SVM Method and the Principal Component Analysis (PCA)

Analyses carried out on examples of detected defects echoes showed clearly that one can describe these detected forms according to a whole of characteristic parameters in order to be able to make discrimination between a planar defect and a volumic defect. This work answers to a problem of ultrasonics NDT like Identification of the defects. The problems as well as the objective of this realized work, are divided in three parts: Extractions of the parameters of wavelets from the ultrasonic echo of the detected defect - the second part is devoted to principal components analysis (PCA) for optimization of the attributes vector. And finally to establish the algorithm of classification (SVM, Support Vector Machine) which allows discrimination between a plane defect and a volumic defect. We have completed this work by a conclusion where we draw up a summary of the completed works, as well as the robustness of the various algorithms proposed in this study.