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    A Simplified Method for Optimal Sensor Distribution for Process Fault Diagnosis in Multistation Assembly Processes

    Source: Journal of Manufacturing Science and Engineering:;2008:;volume( 130 ):;issue: 005::page 51002
    Author:
    Lai Xinmin
    ,
    Tian Zhaoqing
    ,
    Lin Zhongqin
    DOI: 10.1115/1.2951943
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The fault diagnosis plays an important role for product quality improvement in the multistation assembly processes (MAPs) and the efficiency of diagnosis significantly depends on the sensor distribution strategy, such as the number and location of the sensor. The diagnosis-oriented sensor distribution optimization in MAP has been studied for the purpose of a full diagnosis of the process faults with the minimum sensing stations number as well as the minimum sensor number. However, the existing studies are time consuming with the complex analysis and calculation processes, and no intuitive principles are given directly according to the process configuration. In this paper, a simplified method for the optimal sensor distribution is presented for a fully diagnosis of the process faults. First, two different types of assembly modes are defined and the variation transmissibility ratios for these two assembly modes are discussed based on the process configuration. Then, the conditions for between-station diagnosability and within-station diagnosability are analyzed, respectively. Based on the results, the optimal sensor distribution method is derived finally. After comparing with the former methods, the optimal sensor distribution in this paper is based only on the process configuration without using for model-based matrix computation. Therefore, the proposed method greatly simplified the process.
    keyword(s): Sensors , Manufacturing AND Optimization ,
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      A Simplified Method for Optimal Sensor Distribution for Process Fault Diagnosis in Multistation Assembly Processes

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    http://yetl.yabesh.ir/yetl1/handle/yetl/138659
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    contributor authorLai Xinmin
    contributor authorTian Zhaoqing
    contributor authorLin Zhongqin
    date accessioned2017-05-09T00:29:19Z
    date available2017-05-09T00:29:19Z
    date copyrightOctober, 2008
    date issued2008
    identifier issn1087-1357
    identifier otherJMSEFK-28030#051002_1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/138659
    description abstractThe fault diagnosis plays an important role for product quality improvement in the multistation assembly processes (MAPs) and the efficiency of diagnosis significantly depends on the sensor distribution strategy, such as the number and location of the sensor. The diagnosis-oriented sensor distribution optimization in MAP has been studied for the purpose of a full diagnosis of the process faults with the minimum sensing stations number as well as the minimum sensor number. However, the existing studies are time consuming with the complex analysis and calculation processes, and no intuitive principles are given directly according to the process configuration. In this paper, a simplified method for the optimal sensor distribution is presented for a fully diagnosis of the process faults. First, two different types of assembly modes are defined and the variation transmissibility ratios for these two assembly modes are discussed based on the process configuration. Then, the conditions for between-station diagnosability and within-station diagnosability are analyzed, respectively. Based on the results, the optimal sensor distribution method is derived finally. After comparing with the former methods, the optimal sensor distribution in this paper is based only on the process configuration without using for model-based matrix computation. Therefore, the proposed method greatly simplified the process.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleA Simplified Method for Optimal Sensor Distribution for Process Fault Diagnosis in Multistation Assembly Processes
    typeJournal Paper
    journal volume130
    journal issue5
    journal titleJournal of Manufacturing Science and Engineering
    identifier doi10.1115/1.2951943
    journal fristpage51002
    identifier eissn1528-8935
    keywordsSensors
    keywordsManufacturing AND Optimization
    treeJournal of Manufacturing Science and Engineering:;2008:;volume( 130 ):;issue: 005
    contenttypeFulltext
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