189 research outputs found

    Review of data mining applications for quality assessment in manufacturing industry: Support Vector Machines

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    In many modern manufacturing industries, data that characterize the manufacturing process are electronically collected and stored in the databases. Due to advances in data collection systems and analysis tools, data mining (DM) has widely been applied for quality assessment (QA) in manufacturing industries. In DM, the choice of technique to use in analyzing a dataset and assessing the quality depend on the understanding of the analyst. On the other hand, with the advent of improved and efficient prediction techniques, there is a need for an analyst to know which tool performs best for a particular type of data set. Although a few review papers have recently been published to discuss DM applications in manufacturing for QA, this paper provides an extensive review to investigate the application of a special DM technique, namely support vector machine (SVM) to solve QA problems. The review provides a comprehensive analysis of the literature from various points of view as DM preliminaries, data preprocessing, DM applications for each quality task, SVM preliminaries, and application results. Summary tables and figures are also provided besides to the analyses. Finally, conclusions and future research directions are provided

    Applied Metaheuristic Computing

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    For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

    Multiple Damage Identification of Beam Structure Using Vibration Analysis and Artificial Intelligence Techniques

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    This thesis investigates the problem of multiple damage detection in vibrating structural members using the dynamic response of the system. Changes in the loading patterns, weakening/degeneration of structures with time and influence of environment may cause cracks in the structure, especially in engineering structures which are developed for prolonged life. Hence, early detection of presence of damage can prevent the catastrophic failure of the structures by appropriately monitoring the response of the system. In recent times, condition monitoring of structural systems have attracted scientists and researchers to develop on line damage diagnostic tool. Primarily, the structural health monitoring technique utilizes the methodology for damage assessment using the monitored vibration parameters. In the current analysis, special attention has been focused on those methods capable of detecting multiple cracks present in system by comparing the information for damaged and undamaged state of the structure. In the current research, methodologies have been developed for damage detection of a cracked cantilever beam with multiple cracks using analytical, Finite Element Analysis (FEA), fuzzy logic, neural network, fuzzy neuro, MANFIS, Genetic Algorithm and hybrid techniques such as GA-fuzzy, GA-neural, GA-neuro- fuzzy. Analytical study has been performed on the cantilever beam with multiple cracks to obtain the vibration characteristics of the beam member by using the expressions of strain energy release rate and stress intensity factor. The presence of cracks in a structural member introduces local flexibility that affects its dynamic response. The local stiffness matrices have been measured using the inverse of local dimensionless compliance matrix for finding out the deviation in the vibrating signatures of the cracked cantilever beam from that of the intact beam. Finite Element Analysis has been carried out to derive the vibration indices of the cracked structure using the overall flexibility matrix, total flexibility matrix, flexibility matrix of the intact beam. From the research done here, it is concluded that the performance of the damage assessment methods depends on several factors for example, the number of cracks, the number of sensors used for acquiring the dynamic response, location and severity of damages. Different artificial intelligent model based on fuzzy logic, neural network, genetic algorithm, MANFIS and hybrid techniques have been designed using the computed vibration signatures for multiple crack diagnosis in cantilever beam structures with higher accuracy and considerably low computational time

    Study of Computational and Experimental Methodologies for Cracks Recognition of Vibrating Systems using Modal Parameters

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    Mostly the structural members and machine elements are subjected to progressive static and dynamic loading and that may cause initiation of defects in the form of crack. The cause of damage may be due to the normal operation, accidents or severe natural calamities such as earthquake or storm. That may lead to catastrophic failure or collapse of the structures. Thereby the importance of identification of damage in the structures is not only for leading safe operation but also to prevent the loss of economy and lives. The condition monitoring of the engineering systems is attracted by the researchers and scientists very much to invent the automated fault diagnosis mechanism using the change in vibration response before and after damage. The structural steel is widely used in various engineering systems such as bridges, railway coaches, ships, automobiles, etc. The glass fiber reinforced epoxy layered composite material has become popular for constructing the various engineering structures due to its valuable characteristics such as higher stiffness and strength to weight ratio, better damage tolerance capacity and wear resistance. Therefore, layered composite and structural steel have been taken into account in the current study. The theoretical analysis has been performed to measure the vibration signatures (Natural Frequencies and Mode Shapes) of multiple cracked composite and structural steel. The presence of the crack in structures generates an additional flexibility. That is evaluated by strain energy release rate given by linear fracture mechanics. The additional flexibility alters the dynamic signatures of cracked beam. The local stiffness matrix has been calculated by the inverse of local dimensionless compliance matrix. The finite element analysis has been carried out to measure the vibration signatures of cracked cantilever beam using commercially available finite element software package ANSYS. It is observed from the current analysis, the various factors such as the orientation of cracks, number and position of the cracks affect the performance and effectiveness of damage detection techniques. The various automated artificial intelligent (AI) techniques such as fuzzy controller, neural network and hybrid AI techniques based multiple faults diagnosis systems are developed using vibration response of cracked cantilever beams. The experiments have been conducted to verify the performance and accuracy of proposed methods. A good agreement is observed between the results

    Applied Methuerstic computing

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    For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

    Advances in Evolutionary Algorithms

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    With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

    Computational Intelligence for Modeling, Control, Optimization, Forecasting and Diagnostics in Photovoltaic Applications

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    This book is a Special Issue Reprint edited by Prof. Massimo Vitelli and Dr. Luigi Costanzo. It contains original research articles covering, but not limited to, the following topics: maximum power point tracking techniques; forecasting techniques; sizing and optimization of PV components and systems; PV modeling; reconfiguration algorithms; fault diagnosis; mismatching detection; decision processes for grid operators

    Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

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    This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

    Bioinformatics Applications Based On Machine Learning

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    The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems
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