Optimal sensor placement based on dynamic condensation using multi-objective optimization algorithm View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2022-07-14

AUTHORS

Chen Yang, Yuanqing Xia

ABSTRACT

Based on the effective independence method and dynamic condensation approach, a sensor placement method is proposed and solved using a modified NSGA-II in this paper, which is evaluated by a novel distribution index. Based on the relationship between the optimal sensor placement in modal identification and the choice of master degrees of freedom in dynamic condensation, this study aims to realize the multi-objective optimization of position selections that connect the aforementioned research fields. Two objectives are constituted based on the determinant of the Fisher information matrix in the effective independence method and the condensation accuracy of the reduced model in a dynamic reduction approach, which comprises the multi-objective optimal sensor placement problem. A novel distribution index to appraise multi-objective non-dominated solutions is investigated to assess the suitability of multi-objective optimization solutions based on the Pareto front distributions. Furthermore, based on this novel index, a modified NSGA-II is constructed by updating the process to enhance the efficiency of the proposed optimal sensor placement method. Finally, two numerical examples are used to verify the effectiveness and accuracy of the proposed method, along with a comprehensive discussion. More... »

PAGES

210

References to SciGraph publications

  • 2021-12-18. Damage identification in plate structures based on the topological derivative method in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2017-11-10. Multi-objective probabilistic optimum monitoring planning considering fatigue damage detection, maintenance, reliability, service life and cost in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2022-01-18. Structural damage identification based on estimated additional virtual masses and Bayesian theory in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2018-06-21. Sensor placement optimization applied to laminated composite plates under vibration in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2022-03-02. Optimal Sensor Placement Considering Both Sensor Faults Under Uncertainty and Sensor Clustering for Vibration-Based Damage Detection in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2020-10-31. An efficient multi-objective optimization method based on the adaptive approximation model of the radial basis function in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2017-11-20. A General Hybrid Optimization Strategy for Curve Fitting in the Non-uniform Rational Basis Spline Framework in JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
  • 2016-10-20. Energy-aware framework with Markov chain-based parallel simulated annealing algorithm for dynamic management of virtual machines in cloud data centers in THE JOURNAL OF SUPERCOMPUTING
  • 2021-07-05. Vibration-based damage detection of structures employing Bayesian data fusion coupled with TLBO optimization algorithm in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00158-022-03307-9

    DOI

    http://dx.doi.org/10.1007/s00158-022-03307-9

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1149471805


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Mathematical Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0103", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Numerical and Computational Mathematics", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "School of Automation, Beijing Institute of Technology, 100081, Beijing, China", 
              "id": "http://www.grid.ac/institutes/grid.43555.32", 
              "name": [
                "School of Automation, Beijing Institute of Technology, 100081, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Yang", 
            "givenName": "Chen", 
            "id": "sg:person.011006110361.28", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011006110361.28"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "School of Automation, Beijing Institute of Technology, 100081, Beijing, China", 
              "id": "http://www.grid.ac/institutes/grid.43555.32", 
              "name": [
                "School of Automation, Beijing Institute of Technology, 100081, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Xia", 
            "givenName": "Yuanqing", 
            "id": "sg:person.015427123271.52", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015427123271.52"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s10957-017-1192-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092811465", 
              "https://doi.org/10.1007/s10957-017-1192-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00158-021-02980-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1139364621", 
              "https://doi.org/10.1007/s00158-021-02980-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00158-017-1849-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1092614397", 
              "https://doi.org/10.1007/s00158-017-1849-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00158-018-2024-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1105050145", 
              "https://doi.org/10.1007/s00158-018-2024-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00158-021-03159-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1145981888", 
              "https://doi.org/10.1007/s00158-021-03159-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00158-020-02766-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1132237176", 
              "https://doi.org/10.1007/s00158-020-02766-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00158-021-03145-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1144003252", 
              "https://doi.org/10.1007/s00158-021-03145-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00158-021-03156-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1144755090", 
              "https://doi.org/10.1007/s00158-021-03156-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11227-016-1900-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013268303", 
              "https://doi.org/10.1007/s11227-016-1900-y"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2022-07-14", 
        "datePublishedReg": "2022-07-14", 
        "description": "Based on the effective independence method and dynamic condensation approach, a sensor placement method is proposed and solved using a modified NSGA-II in this paper, which is evaluated by a novel distribution index. Based on the relationship between the optimal sensor placement in modal identification and the choice of master degrees of freedom in dynamic condensation, this study aims to realize the multi-objective optimization of position selections that connect the aforementioned research fields. Two objectives are constituted based on the determinant of the Fisher information matrix in the effective independence method and the condensation accuracy of the reduced model in a dynamic reduction approach, which comprises the multi-objective optimal sensor placement problem. A novel distribution index to appraise multi-objective non-dominated solutions is investigated to assess the suitability of multi-objective optimization solutions based on the Pareto front distributions. Furthermore, based on this novel index, a modified NSGA-II is constructed by updating the process to enhance the efficiency of the proposed optimal sensor placement method. Finally, two numerical examples are used to verify the effectiveness and accuracy of the proposed method, along with a comprehensive discussion.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s00158-022-03307-9", 
        "isAccessibleForFree": false, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.8899002", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1050630", 
            "issn": [
              "1615-147X", 
              "1615-1488"
            ], 
            "name": "Structural and Multidisciplinary Optimization", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "7", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "65"
          }
        ], 
        "keywords": [
          "effective independence method", 
          "sensor placement method", 
          "optimal sensor placement", 
          "sensor placement", 
          "optimal sensor placement method", 
          "dynamic condensation", 
          "dynamic condensation approach", 
          "independence method", 
          "optimal sensor placement problem", 
          "modal identification", 
          "sensor placement problem", 
          "multi-objective optimization", 
          "placement method", 
          "aforementioned research fields", 
          "front distribution", 
          "multi-objective optimization solutions", 
          "multi-objective optimization algorithm", 
          "distribution index", 
          "NSGA-II", 
          "optimization solution", 
          "non-dominated solutions", 
          "numerical examples", 
          "optimization algorithm", 
          "reduction approach", 
          "Fisher information matrix", 
          "position selection", 
          "comprehensive discussion", 
          "method", 
          "condensation approach", 
          "accuracy", 
          "solution", 
          "placement problem", 
          "optimization", 
          "information matrix", 
          "efficiency", 
          "matrix", 
          "research field", 
          "condensation", 
          "field", 
          "suitability", 
          "novel index", 
          "process", 
          "approach", 
          "algorithm", 
          "distribution", 
          "model", 
          "placement", 
          "effectiveness", 
          "problem", 
          "freedom", 
          "example", 
          "index", 
          "degree", 
          "objective", 
          "selection", 
          "study", 
          "choice", 
          "identification", 
          "discussion", 
          "relationship", 
          "master's degree", 
          "determinants", 
          "paper"
        ], 
        "name": "Optimal sensor placement based on dynamic condensation using multi-objective optimization algorithm", 
        "pagination": "210", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1149471805"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s00158-022-03307-9"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s00158-022-03307-9", 
          "https://app.dimensions.ai/details/publication/pub.1149471805"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-12-01T06:44", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/article/article_929.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s00158-022-03307-9"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00158-022-03307-9'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00158-022-03307-9'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00158-022-03307-9'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00158-022-03307-9'


     

    This table displays all metadata directly associated to this object as RDF triples.

    165 TRIPLES      21 PREDICATES      96 URIs      79 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s00158-022-03307-9 schema:about anzsrc-for:01
    2 anzsrc-for:0103
    3 schema:author Na4a203b65a7f4ed8b5c54df9bce9909f
    4 schema:citation sg:pub.10.1007/s00158-017-1849-3
    5 sg:pub.10.1007/s00158-018-2024-1
    6 sg:pub.10.1007/s00158-020-02766-2
    7 sg:pub.10.1007/s00158-021-02980-6
    8 sg:pub.10.1007/s00158-021-03145-1
    9 sg:pub.10.1007/s00158-021-03156-y
    10 sg:pub.10.1007/s00158-021-03159-9
    11 sg:pub.10.1007/s10957-017-1192-2
    12 sg:pub.10.1007/s11227-016-1900-y
    13 schema:datePublished 2022-07-14
    14 schema:datePublishedReg 2022-07-14
    15 schema:description Based on the effective independence method and dynamic condensation approach, a sensor placement method is proposed and solved using a modified NSGA-II in this paper, which is evaluated by a novel distribution index. Based on the relationship between the optimal sensor placement in modal identification and the choice of master degrees of freedom in dynamic condensation, this study aims to realize the multi-objective optimization of position selections that connect the aforementioned research fields. Two objectives are constituted based on the determinant of the Fisher information matrix in the effective independence method and the condensation accuracy of the reduced model in a dynamic reduction approach, which comprises the multi-objective optimal sensor placement problem. A novel distribution index to appraise multi-objective non-dominated solutions is investigated to assess the suitability of multi-objective optimization solutions based on the Pareto front distributions. Furthermore, based on this novel index, a modified NSGA-II is constructed by updating the process to enhance the efficiency of the proposed optimal sensor placement method. Finally, two numerical examples are used to verify the effectiveness and accuracy of the proposed method, along with a comprehensive discussion.
    16 schema:genre article
    17 schema:isAccessibleForFree false
    18 schema:isPartOf N05e46981128e4befa23b73bc85e99533
    19 Ne6f2fbf4d0904bfd834115aab5db8e69
    20 sg:journal.1050630
    21 schema:keywords Fisher information matrix
    22 NSGA-II
    23 accuracy
    24 aforementioned research fields
    25 algorithm
    26 approach
    27 choice
    28 comprehensive discussion
    29 condensation
    30 condensation approach
    31 degree
    32 determinants
    33 discussion
    34 distribution
    35 distribution index
    36 dynamic condensation
    37 dynamic condensation approach
    38 effective independence method
    39 effectiveness
    40 efficiency
    41 example
    42 field
    43 freedom
    44 front distribution
    45 identification
    46 independence method
    47 index
    48 information matrix
    49 master's degree
    50 matrix
    51 method
    52 modal identification
    53 model
    54 multi-objective optimization
    55 multi-objective optimization algorithm
    56 multi-objective optimization solutions
    57 non-dominated solutions
    58 novel index
    59 numerical examples
    60 objective
    61 optimal sensor placement
    62 optimal sensor placement method
    63 optimal sensor placement problem
    64 optimization
    65 optimization algorithm
    66 optimization solution
    67 paper
    68 placement
    69 placement method
    70 placement problem
    71 position selection
    72 problem
    73 process
    74 reduction approach
    75 relationship
    76 research field
    77 selection
    78 sensor placement
    79 sensor placement method
    80 sensor placement problem
    81 solution
    82 study
    83 suitability
    84 schema:name Optimal sensor placement based on dynamic condensation using multi-objective optimization algorithm
    85 schema:pagination 210
    86 schema:productId Naf1bd98a14454f9699599be65ce93021
    87 Nff110df584f3495e9d09d6a874903a86
    88 schema:sameAs https://app.dimensions.ai/details/publication/pub.1149471805
    89 https://doi.org/10.1007/s00158-022-03307-9
    90 schema:sdDatePublished 2022-12-01T06:44
    91 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    92 schema:sdPublisher N51a04b836f93432b99dcab58ebd8a51a
    93 schema:url https://doi.org/10.1007/s00158-022-03307-9
    94 sgo:license sg:explorer/license/
    95 sgo:sdDataset articles
    96 rdf:type schema:ScholarlyArticle
    97 N05e46981128e4befa23b73bc85e99533 schema:volumeNumber 65
    98 rdf:type schema:PublicationVolume
    99 N3885e800f0144df080894db7fbcff74f rdf:first sg:person.015427123271.52
    100 rdf:rest rdf:nil
    101 N51a04b836f93432b99dcab58ebd8a51a schema:name Springer Nature - SN SciGraph project
    102 rdf:type schema:Organization
    103 Na4a203b65a7f4ed8b5c54df9bce9909f rdf:first sg:person.011006110361.28
    104 rdf:rest N3885e800f0144df080894db7fbcff74f
    105 Naf1bd98a14454f9699599be65ce93021 schema:name dimensions_id
    106 schema:value pub.1149471805
    107 rdf:type schema:PropertyValue
    108 Ne6f2fbf4d0904bfd834115aab5db8e69 schema:issueNumber 7
    109 rdf:type schema:PublicationIssue
    110 Nff110df584f3495e9d09d6a874903a86 schema:name doi
    111 schema:value 10.1007/s00158-022-03307-9
    112 rdf:type schema:PropertyValue
    113 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
    114 schema:name Mathematical Sciences
    115 rdf:type schema:DefinedTerm
    116 anzsrc-for:0103 schema:inDefinedTermSet anzsrc-for:
    117 schema:name Numerical and Computational Mathematics
    118 rdf:type schema:DefinedTerm
    119 sg:grant.8899002 http://pending.schema.org/fundedItem sg:pub.10.1007/s00158-022-03307-9
    120 rdf:type schema:MonetaryGrant
    121 sg:journal.1050630 schema:issn 1615-147X
    122 1615-1488
    123 schema:name Structural and Multidisciplinary Optimization
    124 schema:publisher Springer Nature
    125 rdf:type schema:Periodical
    126 sg:person.011006110361.28 schema:affiliation grid-institutes:grid.43555.32
    127 schema:familyName Yang
    128 schema:givenName Chen
    129 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011006110361.28
    130 rdf:type schema:Person
    131 sg:person.015427123271.52 schema:affiliation grid-institutes:grid.43555.32
    132 schema:familyName Xia
    133 schema:givenName Yuanqing
    134 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015427123271.52
    135 rdf:type schema:Person
    136 sg:pub.10.1007/s00158-017-1849-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092614397
    137 https://doi.org/10.1007/s00158-017-1849-3
    138 rdf:type schema:CreativeWork
    139 sg:pub.10.1007/s00158-018-2024-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105050145
    140 https://doi.org/10.1007/s00158-018-2024-1
    141 rdf:type schema:CreativeWork
    142 sg:pub.10.1007/s00158-020-02766-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1132237176
    143 https://doi.org/10.1007/s00158-020-02766-2
    144 rdf:type schema:CreativeWork
    145 sg:pub.10.1007/s00158-021-02980-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1139364621
    146 https://doi.org/10.1007/s00158-021-02980-6
    147 rdf:type schema:CreativeWork
    148 sg:pub.10.1007/s00158-021-03145-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1144003252
    149 https://doi.org/10.1007/s00158-021-03145-1
    150 rdf:type schema:CreativeWork
    151 sg:pub.10.1007/s00158-021-03156-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1144755090
    152 https://doi.org/10.1007/s00158-021-03156-y
    153 rdf:type schema:CreativeWork
    154 sg:pub.10.1007/s00158-021-03159-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1145981888
    155 https://doi.org/10.1007/s00158-021-03159-9
    156 rdf:type schema:CreativeWork
    157 sg:pub.10.1007/s10957-017-1192-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092811465
    158 https://doi.org/10.1007/s10957-017-1192-2
    159 rdf:type schema:CreativeWork
    160 sg:pub.10.1007/s11227-016-1900-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1013268303
    161 https://doi.org/10.1007/s11227-016-1900-y
    162 rdf:type schema:CreativeWork
    163 grid-institutes:grid.43555.32 schema:alternateName School of Automation, Beijing Institute of Technology, 100081, Beijing, China
    164 schema:name School of Automation, Beijing Institute of Technology, 100081, Beijing, China
    165 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...