Search results for: single machine scheduling.

Authors: S. Ghorbani, N. I. Polushin

Abstract:

In this study, an approach to identify factors affecting on surface roughness in a machining process is presented. This study is based on 81 data about surface roughness over a wide range of cutting tools (conventional, cutting tool with holes, cutting tool with composite material), workpiece materials (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). A single decision tree (SDT) analysis was done to identify factors for predicting a model of surface roughness, and the CART algorithm was employed for building and evaluating regression tree. Results show that a single decision tree is better than traditional regression models with higher rate and forecast accuracy and strong value.

Keywords: Cutting condition, surface roughness, decision tree, CART algorithm.

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Authors: Mohammad Jafarifar

Abstract:

This paper presents a speed estimation scheme based on second-order sliding-mode Super Twisting Algorithm (STA) and Model Reference Adaptive System (MRAS) estimation theory for Sensorless control of multiphase induction machine. A stator current observer is designed based on the STA, which is utilized to take the place of the reference voltage model of the standard MRAS algorithm. The observer is insensitive to the variation of rotor resistance and magnetizing inductance when the states arrive at the sliding mode. Derivatives of rotor flux are obtained and designed as the state of MRAS, thus eliminating the integration. Compared with the first-order sliding-mode speed estimator, the proposed scheme makes full use of the auxiliary sliding-mode surface, thus alleviating the chattering behavior without increasing the complexity. Simulation results show the robustness and effectiveness of the proposed scheme.

Keywords: Multiphase induction machine, field oriented control, sliding mode, super twisting algorithm, MRAS algorithm.

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Authors: Adrià Arbués-Sangüesa, Coloma Ballester, Gloria Haro

Abstract:

Tracking sports players is a widely challenging scenario, specially in single-feed videos recorded in tight courts, where cluttering and occlusions cannot be avoided. This paper presents an analysis of several geometric and semantic visual features to detect and track basketball players. An ablation study is carried out and then used to remark that a robust tracker can be built with Deep Learning features, without the need of extracting contextual ones, such as proximity or color similarity, nor applying camera stabilization techniques. The presented tracker consists of: (1) a detection step, which uses a pretrained deep learning model to estimate the players pose, followed by (2) a tracking step, which leverages pose and semantic information from the output of a convolutional layer in a VGG network. Its performance is analyzed in terms of MOTA over a basketball dataset with more than 10k instances.

Keywords: Basketball, deep learning, feature extraction, single-camera, tracking.

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Authors: JaeHwan Yang, Da-Woon Jeong, Seung-Young Kho, Dong-Kyu Kim

Abstract:

In terms of ITS, information on link characteristic is an essential factor for plan or operation. But in practical cases, not every link has installed sensors on it. The link that does not have data on it is called “Missing Link”. The purpose of this study is to impute data of these missing links. To get these data, this study applies the machine learning method. With the machine learning process, especially for the deep learning process, missing link data can be estimated from present link data. For deep learning process, this study uses “Recurrent Neural Network” to take time-series data of road. As input data, Dedicated Short-range Communications (DSRC) data of Dalgubul-daero of Daegu Metropolitan Area had been fed into the learning process. Neural Network structure has 17 links with present data as input, 2 hidden layers, for 1 missing link data. As a result, forecasted data of target link show about 94% of accuracy compared with actual data.

Keywords: Data Estimation, link data, machine learning, road network.

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Authors: Tae Jin Choi, Jong Ok Kim, Sang Min Jin, Gilwon Yoon

Abstract:

Analysis of EEG brainwave provides information on mental or emotional states. One of the particular states that can have various applications in human machine interface (HMI) is concentration. 8-channel EEG signals were measured and analyzed. The concentration index was compared during resting and concentrating periods. Among eight channels, locations the frontal lobe (F_p1 and F_p2) showed a clear increase of the concentration index during concentration regardless of subjects. The rest six channels produced conflicting observations depending on subjects. At this time, it is not clear whether individual difference or how to concentrate made these results for the rest six channels. Nevertheless, it is expected that F_p1 and F_p2 are promising locations for extracting control signal for HMI applications.

Keywords: Concentration, EEG, human machine interface.

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Authors: Petter Eklund, Jonathan Sjölund, Sandra Eriksson, Mats Leijon

Abstract:

The spoke type rotor can be used to obtain magnetic flux concentration in permanent magnet machines. This allows the air gap magnetic flux density to exceed the remanent flux density of the permanent magnets but gives problems with leakage fluxes in the magnetic circuit. The end leakage flux of one spoke type permanent magnet rotor design is studied through measurements and finite element simulations. The measurements are performed in the end regions of a 12 kW prototype generator for a vertical axis wind turbine. The simulations are made using three dimensional finite elements to calculate the magnetic field distribution in the end regions of the machine. Also two dimensional finite element simulations are performed and the impact of the two dimensional approximation is studied. It is found that the magnetic leakage flux in the end regions of the machine is equal to about 20% of the flux in the permanent magnets. The overestimation of the performance by the two dimensional approximation is quantified and a curve-fitted expression for its behavior is suggested.

Keywords: End effects, end leakage flux, permanent magnet machine, spoke type rotor.

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Authors: V. Goremikins, K. Rocens, D. Serdjuks

Abstract:

One of the main problems of suspended cable structures is initial shape change under the action of non uniform load. The problem can be solved by increasing of weight of construction or by using of prestressing. But this methods cause increasing of materials consumption of suspended cable structure. The cable truss usage is another way how the problem of shape change under the action of non uniform load can be fixed. The cable trusses with the vertical and inclined suspensions, cross web and single cable were analyzed as the main load-bearing structures of suspension bridge. It was shown, that usage of cable truss allows to reduce the vertical displacements up to 32% in comparison with the single cable in case of non uniformly distributed load. In case of uniformly distributed load single cable is preferable.

Keywords: Cable trusses, Non uniform load, Suspension bridge, Vertical displacements.

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Authors: P. Panichnumsin, B. K. Ahring, A. Nopharatana, P. Chaipresert

Abstract:

In this study, we illustrated the performance and microbial community of single- and two-phase systems anaerobically co-digesting cassava pulp and pig manure. The results showed that the volatile solid reduction and biogas productivity of two-phase CSTR were 66 ± 4% and 2000 ± 210 ml l-1 d-1, while those of singlephase CSTR were 59 ± 1% and 1670 ± 60 ml l-1 d-1, respectively. Codigestion in two-phase CSTR gave higher 12% solid degradation and 25% methane production than single-phase CSTR. Phylogenetic analysis of 16S rDNA clone library revealed that the Bacteroidetes were the most abundant group, followed by the Clostridia in singlephase CSTR. In hydrolysis/acidification reactor of two-phase system, the bacteria within the phylum Firmicutes, especially Clostridium, Eubacteriaceae and Lactobacillus were the dominant phylogenetic groups. Among the Archaea, Methanosaeta sp. was the exclusive predominant in both digesters while the relative abundance of Methanosaeta sp. and Methanospirillum hungatei differed between the two systems.

Keywords: Anaerobic co-digestion, Cassava pulp, Microbialdiversity, Pig manure.

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Authors: Muhammad Awais Shafique, Eiji Hato, Hideki Yaginuma

Abstract:

Recently GPS data is used in a lot of studies to automatically reconstruct travel patterns for trip survey. The aim is to minimize the use of questionnaire surveys and travel diaries so as to reduce their negative effects. In this paper data acquired from GPS and accelerometer embedded in smart phones is utilized to predict the mode of transportation used by the phone carrier. For prediction, Support Vector Machine (SVM) and Adaptive boosting (AdaBoost) are employed. Moreover a unique method to improve the prediction results from these algorithms is also proposed. Results suggest that the prediction accuracy of AdaBoost after improvement is relatively better than the rest.

Keywords: Accelerometer, AdaBoost, GPS, Mode Prediction, Support vector Machine.

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Authors: Youguo Wang, Lenan Wu

Abstract:

We discuss the signal detection through nonlinear threshold systems. The detection performance is assessed by the probability of error Per . We establish that: (1) when the signal is complete suprathreshold, noise always degrades the signal detection both in the single threshold system and in the parallel array of threshold devices. (2) When the signal is a little subthreshold, noise degrades signal detection in the single threshold system. But in the parallel array, noise can improve signal detection, i.e., stochastic resonance (SR) exists in the array. (3) When the signal is predominant subthreshold, noise always can improve signal detection and SR always exists not only in the single threshold system but also in the parallel array. (4) Array can improve signal detection by raising the number of threshold devices. These results extend further the applicability of SR in signal detection.

Keywords: Probability of error, signal detection, stochasticresonance, threshold system.

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Authors: Hyun-Woo Cho

Abstract:

Detection of incipient abnormal events is important to improve safety and reliability of machine operations and reduce losses caused by failures. Improper set-ups or aligning of parts often leads to severe problems in many machines. The construction of prediction models for predicting faulty conditions is quite essential in making decisions on when to perform machine maintenance. This paper presents a multivariate calibration monitoring approach based on the statistical analysis of machine measurement data. The calibration model is used to predict two faulty conditions from historical reference data. This approach utilizes genetic algorithms (GA) based variable selection, and we evaluate the predictive performance of several prediction methods using real data. The results shows that the calibration model based on supervised probabilistic principal component analysis (SPPCA) yielded best performance in this work. By adopting a proper variable selection scheme in calibration models, the prediction performance can be improved by excluding non-informative variables from their model building steps.

Keywords: Prediction, operation monitoring, on-line data, nonlinear statistical methods, empirical model.

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Authors: Abdenour Bounsiar, Edith Grall, Pierre Beauseroy

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In this paper we are interested in classification problems with a performance constraint on error probability. In such problems if the constraint cannot be satisfied, then a rejection option is introduced. For binary labelled classification, a number of SVM based methods with rejection option have been proposed over the past few years. All of these methods use two thresholds on the SVM output. However, in previous works, we have shown on synthetic data that using thresholds on the output of the optimal SVM may lead to poor results for classification tasks with performance constraint. In this paper a new method for supervised classification with rejection option is proposed. It consists in two different classifiers jointly optimized to minimize the rejection probability subject to a given constraint on error rate. This method uses a new kernel based linear learning machine that we have recently presented. This learning machine is characterized by its simplicity and high training speed which makes the simultaneous optimization of the two classifiers computationally reasonable. The proposed classification method with rejection option is compared to a SVM based rejection method proposed in recent literature. Experiments show the superiority of the proposed method.

Keywords: rejection, Chow's rule, error-reject tradeoff, SupportVector Machine.

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Authors: Ava Dodd, Jonathan Adams

Abstract:

The purpose of this study is to explore the characteristics of developing a machine learning application using synthetic data. The study is structured to develop the application for the purpose of deploying the computer vision model. The findings discuss the realities of attempting to develop a computer vision model for practical purpose, and detail the processes, tools and techniques that were used to meet accuracy requirements. The research reveals that synthetic data represent another variable that can be adjusted to improve the performance of a computer vision model. Further, a suite of tools and tuning recommendations are provided.

Keywords: computer vision, machine learning, synthetic data, YOLOv4

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Authors: R. Pilafkan, M. Kaffash Irzarahimi, S. F. Asbaghian Namin

Abstract:

The biaxial buckling behavior of single-layered graphene sheets (SLGSs) is studied in the present work. To consider the size-effects in the analysis, Eringen’s nonlocal elasticity equations are incorporated into classical plate theory (CLPT). A Generalized Differential Quadrature Method (GDQM) approach is utilized and numerical solutions for the critical buckling loads are obtained. Then, molecular dynamics (MD) simulations are performed for a series of zigzag SLGSs with different side-lengths and with various boundary conditions, the results of which are matched with those obtained by the nonlocal plate model to numerical the appropriate values of nonlocal parameter relevant to each type of boundary conditions.

Keywords: Biaxial buckling, single-layered graphene sheets, nonlocal elasticity, molecular dynamics simulation, classical plate theory.

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Authors: Mitchell J. McHugh, John J. Case

Abstract:

Systems engineering is a holistic discipline that seeks to organize and optimize complex, interdisciplinary systems. With the growth of artificial intelligence, systems engineers must face the challenge of leveraging artificial intelligence systems to solve complex problems. This paper analyzes the integration of systems engineering and artificial intelligence and discusses how artificial intelligence systems embody the systems decision process (SDP). The SDP is a four-stage problem-solving framework that outlines how systems engineers can design and implement solutions using value-focused thinking. This paper argues that artificial intelligence models can replicate the SDP, thus validating its flexible, value-focused foundation. The authors demonstrate this by developing a machine vision mobile application that can classify weapons to augment the decision-making role of an Army subject matter expert. This practical application was an end-to-end design challenge that highlights how artificial intelligence systems embody systems engineering principles. The impact of this research demonstrates that the SDP is a dynamic tool that systems engineers should leverage when incorporating artificial intelligence within the systems that they develop.

Keywords: Computer vision, machine learning, mobile application, systems engineering, systems decision process.

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Authors: Sunthorn S., Kittiphat R.

Abstract:

The edge waviness in hot rolled steel is a common defect. Variables that affect such defect include raw material and machine. These variables are necessary to consider to understand such defect. This research studied the defect of edge waviness for SS 400 of metal sheet manufacture. Defect of metal sheets were divided into two groups. The specimens were investigated on chemical composition and mechanical properties to find the difference. The results of investigation showed that the difference was not significant. Therefore the roll mill machine should be used to adjust to support another location on a roller to avoide edge waviness.

Keywords: Edge waviness, Hot rolling steel, Metal sheet defect, SS 400, Roll leveler.

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Authors: Neamat El Gayar

Abstract:

Learning using labeled and unlabelled data has received considerable amount of attention in the machine learning community due its potential in reducing the need for expensive labeled data. In this work we present a new method for combining labeled and unlabeled data based on classifier ensembles. The model we propose assumes each classifier in the ensemble observes the input using different set of features. Classifiers are initially trained using some labeled samples. The trained classifiers learn further through labeling the unknown patterns using a teaching signals that is generated using the decision of the classifier ensemble, i.e. the classifiers self-supervise each other. Experiments on a set of object images are presented. Our experiments investigate different classifier models, different fusing techniques, different training sizes and different input features. Experimental results reveal that the proposed self-supervised ensemble learning approach reduces classification error over the single classifier and the traditional ensemble classifier approachs.

Keywords: Multiple Classifier Systems, classifier ensembles, learning using labeled and unlabelled data, K-nearest neighbor classifier, Bayes classifier.

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Authors: Salsabil Trabelsi, Zied Elouedi, Khaled Mellouli

Abstract:

The belief decision tree (BDT) approach is a decision tree in an uncertain environment where the uncertainty is represented through the Transferable Belief Model (TBM), one interpretation of the belief function theory. The uncertainty can appear either in the actual class of training objects or attribute values of objects to classify. In this paper, we develop a post-pruning method of belief decision trees in order to reduce size and improve classification accuracy on unseen cases. The pruning of decision tree has a considerable intention in the areas of machine learning.

Keywords: machine learning, uncertainty, belief function theory, belief decision tree, pruning.

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Authors: Berna Ulutas

Abstract:

Single Minute Exchange of Dies (SMED) mainly focuses on recognition of internal and external activities. It is concerned particularly with transferring internal activities into external ones in as many numbers as possible, by also minimizing the internal ones. The validity of the method and procedures are verified by an application a Styrofoam manufacturing process where setup times are critical for time reduction. Significant time savings have been achieved with minimum investment. Further, the issues related with employer safety and ergonomics principles during die exchange are noted.

Keywords: Die exchange, internal-external set-up, lean manufacturing, single minute die exchange.

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Authors: Yuhong Lu

Abstract:

Graves’ Disease (GD), an autoimmune health condition caused by the over reactiveness of the thyroid, affects about 1 in 200 people worldwide. GD is not caused by one specific single nucleotide polymorphism (SNP) or gene mutation, but rather determined by multiple factors, each differing from each other. Malfunction of the genes in Human Leukocyte Antigen (HLA) family tend to play a major role in autoimmune diseases, but other genes, such as LOC101929163, have functions that still remain ambiguous. Currently, little studies were done to study GD, resulting in inconclusive results. This study serves not only to introduce background knowledge about GD, but also to organize and pinpoint the major SNPs and genes that are potentially related to the occurrence of GD in humans. Collected from multiple sources from genome-wide association studies (GWAS) Central, the potential SNPs related to the causes of GD are included in this study. This study has located the genes that are related to those SNPs and closely examines a selected sample. Using the data from this study, scientists will then be able to focus on the most expressed genes in GD patients and develop a treatment for GD.

Keywords: CTLA4, Graves’ Disease, HLA, single nucleotide polymorphism, SNP.

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Authors: A. B. Khoshnevis, F. Zeydabadi, F. Sokhanvar

Abstract:

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.

Keywords: Self-similarity, wake of single circular cylinder

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Authors: Ioannis P. Panapakidis, Marios N. Moschakis

Abstract:

With the continuous increment of smart meter installations across the globe, the need for processing of the load data is evident. Clustering-based load profiling is built upon the utilization of unsupervised machine learning tools for the purpose of formulating the typical load curves or load profiles. The most commonly used algorithm in the load profiling literature is the K-means. While the algorithm has been successfully tested in a variety of applications, its drawback is the strong dependence in the initialization phase. This paper proposes a novel modified form of the K-means that addresses the aforementioned problem. Simulation results indicate the superiority of the proposed algorithm compared to the K-means.

Keywords: Clustering, load profiling, load modeling, machine learning, energy efficiency and quality.

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Authors: Hema C. R., Paulraj M. P., S. Yaacob, A. H. Adom, R. Nagarajan

Abstract:

Motor imagery classification provides an important basis for designing Brain Machine Interfaces [BMI]. A BMI captures and decodes brain EEG signals and transforms human thought into actions. The ability of an individual to control his EEG through imaginary mental tasks enables him to control devices through the BMI. This paper presents a method to design a four state BMI using EEG signals recorded from the C3 and C4 locations. Principle features extracted through principle component analysis of the segmented EEG are analyzed using two novel classification algorithms using Elman recurrent neural network and functional link neural network. Performance of both classifiers is evaluated using a particle swarm optimization training algorithm; results are also compared with the conventional back propagation training algorithm. EEG motor imagery recorded from two subjects is used in the offline analysis. From overall classification performance it is observed that the BP algorithm has higher average classification of 93.5%, while the PSO algorithm has better training time and maximum classification. The proposed methods promises to provide a useful alternative general procedure for motor imagery classification

Keywords: Motor Imagery, Brain Machine Interfaces, Neural Networks, Particle Swarm Optimization, EEG signal processing.

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Authors: J. Kloypayan, W. Pimpakan

Abstract:

The pipe taper thread measurement and uncertainty  normally used the four-wire probe according to the JIS B 0262.  Besides, according to the EA-10/10 standard, the pipe thread could be  measured using the three-wire probe. This research proposed to use  the three-wire probe measuring the pitch diameter of the pipe taper  thread. The measuring accessory component was designed and made,  then, assembled to one side of the ULM 828 CiM machine.  Therefore, this machine could be used to measure and calibrate both  the pipe thread and the pipe taper thread. The equations and the  expanded uncertainty for pitch diameter measurement were  formulated. After the experiment, the results showed that the pipe  taper thread had the pitch diameter equal to 19.165mm and the  expanded uncertainty equal to 1.88µm. Then, the experiment results  were compared to the results from the National Institute of Metrology  Thailand. The equivalence ratio from the comparison showed that  both results were related. Thus, the proposed method of using the  three-wire probe measured the pitch diameter of the pipe taper thread  was acceptable.

Keywords: Pipe taper thread, Three-wire probe, Measure and Calibration, The Universal length measuring machine.

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Authors: L. Parisi, D. Hamili, N. Azlan

Abstract:

The aim of this study was to design and simulate a particular type of Asynchronous State Machine (ASM), namely a ‘traffic light controller’ (TLC), operated at a frequency of 0.5 Hz. The design task involved two main stages: firstly, designing a 4-bit binary counter using J-K flip flops as the timing signal and, subsequently, attaining the digital logic by deploying ASM design process. The TLC was designed such that it showed a sequence of three different colours, i.e. red, yellow and green, corresponding to set thresholds by deploying the least number of AND, OR and NOT gates possible. The software Multisim was deployed to design such circuit and simulate it for circuit troubleshooting in order for it to display the output sequence of the three different colours on the traffic light in the correct order. A clock signal, an asynchronous 4- bit binary counter that was designed through the use of J-K flip flops along with an ASM were used to complete this sequence, which was programmed to be repeated indefinitely. Eventually, the circuit was debugged and optimized, thus displaying the correct waveforms of the three outputs through the logic analyser. However, hazards occurred when the frequency was increased to 10 MHz. This was attributed to delays in the feedback being too high.

Keywords: Asynchronous State Machine, Traffic Light Controller, Circuit Design, Digital Electronics.

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Authors: S. Sarumathi, N. Shanthi

Abstract:

Due to the fast and flawless technological innovation there is a tremendous amount of data dumping all over the world in every domain such as Pattern Recognition, Machine Learning, Spatial Data Mining, Image Analysis, Fraudulent Analysis, World Wide Web etc., This issue turns to be more essential for developing several tools for data mining functionalities. The major aim of this paper is to analyze various tools which are used to build a resourceful analytical or descriptive model for handling large amount of information more efficiently and user friendly. In this survey the diverse tools are illustrated with their extensive technical paradigm, outstanding graphical interface and inbuilt multipath algorithms in which it is very useful for handling significant amount of data more indeed.

Keywords: Classification, Clustering, Data Mining, Machine learning, Visualization.

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Authors: Baljit Singh Khehra, Amar Partap Singh Pharwaha

Abstract:

Clusters of Microcalcifications (MCCs) are most frequent symptoms of Ductal Carcinoma in Situ (DCIS) recognized by mammography. Least-Square Support Vector Machine (LS-SVM) is a variant of the standard SVM. In the paper, LS-SVM is proposed as a classifier for classifying MCCs as benign or malignant based on relevant extracted features from enhanced mammogram. To establish the credibility of LS-SVM classifier for classifying MCCs, a comparative evaluation of the relative performance of LS-SVM classifier for different kernel functions is made. For comparative evaluation, confusion matrix and ROC analysis are used. Experiments are performed on data extracted from mammogram images of DDSM database. A total of 380 suspicious areas are collected, which contain 235 malignant and 145 benign samples, from mammogram images of DDSM database. A set of 50 features is calculated for each suspicious area. After this, an optimal subset of 23 most suitable features is selected from 50 features by Particle Swarm Optimization (PSO). The results of proposed study are quite promising.

Keywords: Clusters of Microcalcifications, Ductal Carcinoma in Situ, Least-Square Support Vector Machine, Particle Swarm Optimization.

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Authors: Uzair Muhammad, Kashif Bilal, Atif Khan, M. Nasir Khan

Abstract:

Machine Translation (MT 3) of English text to its Urdu equivalent is a difficult challenge. Lot of attempts has been made, but a few limited solutions are provided till now. We present a direct approach, using an expert system to translate English text into its equivalent Urdu, using The Unicode Standard, Version 4.0 (ISBN 0-321-18578-1) Range: 0600–06FF. The expert system works with a knowledge base that contains grammatical patterns of English and Urdu, as well as a tense and gender-aware dictionary of Urdu words (with their English equivalents).

Keywords: Machine Translation, Multiword Expressions, Urdulanguage processing, POS12 Tagging for Urdu, Expert Systems.

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Authors: Y. Abdelrazig, A. Ghanem

Abstract:

Construction projects are information intensive in nature and involve many activities that are related to each other. Wireless technologies can be used to improve the accuracy and timeliness of data collected from construction sites and shares it with appropriate parties. Nonetheless, the construction industry tends to be conservative and shows hesitation to adopt new technologies. A main concern for owners, contractors or any person in charge on a job site is the cost of the technology in question. Wireless technologies are not cheap. There are a lot of expenses to be taken into consideration, and a study should be completed to make sure that the importance and savings resulting from the usage of this technology is worth the expenses. This research attempts to assess the effectiveness of using the appropriate wireless technologies based on criteria such as performance, reliability, and risk. The assessment is based on a utility function model that breaks down the selection issue into alternatives attribute. Then the attributes are assigned weights and single attributes are measured. Finally, single attribute are combined to develop one single aggregate utility index for each alternative.

Keywords: Analytic Hierarchy Process, Utility Function, Wireless Technologies, construction management.

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Authors: B. Ladghem Chikouche

Abstract:

The influence of slot wedges permeability on the electromagnetic performance of three-phase permanent magnet synchronous machine is investigated in this paper. It is shown that the back-EMF waveform, electromagnetic torque and electromagnetic torque ripple are all significantly affected by slot wedges permeability. The paper presents an accurate analytical subdomain model and confirmed by finite-element analyses.

Keywords: Exact analytical calculation, finite-element method, magnetic field distribution, permanent magnet machines performance, stator slot wedges permeability.

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