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
  • 2022-01-03. Multi-objective Bayesian topology optimization of a lattice-structured heat sink in natural convection 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/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/s00158-021-02980-6", 
            "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/s00158-021-03156-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"
          }, 
          {
            "id": "sg:pub.10.1007/s00158-021-03092-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1144344969", 
              "https://doi.org/10.1007/s00158-021-03092-x"
            ], 
            "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/s00158-020-02766-2", 
            "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/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/s00158-021-03159-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1145981888", 
              "https://doi.org/10.1007/s00158-021-03159-9"
            ], 
            "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": "2022-09-02T16:07", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20220902/entities/gbq_results/article/article_946.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.

    169 TRIPLES      21 PREDICATES      97 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 N6ee6b278a6d94439afa96e801a4803e6
    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/s00158-021-03092-x
    9 sg:pub.10.1007/s00158-021-03145-1
    10 sg:pub.10.1007/s00158-021-03156-y
    11 sg:pub.10.1007/s00158-021-03159-9
    12 sg:pub.10.1007/s10957-017-1192-2
    13 sg:pub.10.1007/s11227-016-1900-y
    14 schema:datePublished 2022-07-14
    15 schema:datePublishedReg 2022-07-14
    16 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.
    17 schema:genre article
    18 schema:isAccessibleForFree false
    19 schema:isPartOf N5038d0f67de54b04aaa8bffc4e51f5a3
    20 N7bb66d0f6d9e444c9fa4b4cf6f7637c3
    21 sg:journal.1050630
    22 schema:keywords Fisher information matrix
    23 NSGA-II
    24 accuracy
    25 aforementioned research fields
    26 algorithm
    27 approach
    28 choice
    29 comprehensive discussion
    30 condensation
    31 condensation approach
    32 degree
    33 determinants
    34 discussion
    35 distribution
    36 distribution index
    37 dynamic condensation
    38 dynamic condensation approach
    39 effective independence method
    40 effectiveness
    41 efficiency
    42 example
    43 field
    44 freedom
    45 front distribution
    46 identification
    47 independence method
    48 index
    49 information matrix
    50 master's degree
    51 matrix
    52 method
    53 modal identification
    54 model
    55 multi-objective optimization
    56 multi-objective optimization algorithm
    57 multi-objective optimization solutions
    58 non-dominated solutions
    59 novel index
    60 numerical examples
    61 objective
    62 optimal sensor placement
    63 optimal sensor placement method
    64 optimal sensor placement problem
    65 optimization
    66 optimization algorithm
    67 optimization solution
    68 paper
    69 placement
    70 placement method
    71 placement problem
    72 position selection
    73 problem
    74 process
    75 reduction approach
    76 relationship
    77 research field
    78 selection
    79 sensor placement
    80 sensor placement method
    81 sensor placement problem
    82 solution
    83 study
    84 suitability
    85 schema:name Optimal sensor placement based on dynamic condensation using multi-objective optimization algorithm
    86 schema:pagination 210
    87 schema:productId Na8baba1c19b84c0bb0118c99f4fa53c8
    88 Ndcc93123d3804fd791791188f509fb38
    89 schema:sameAs https://app.dimensions.ai/details/publication/pub.1149471805
    90 https://doi.org/10.1007/s00158-022-03307-9
    91 schema:sdDatePublished 2022-09-02T16:07
    92 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    93 schema:sdPublisher N216305187edc45388903dfec4c0768fd
    94 schema:url https://doi.org/10.1007/s00158-022-03307-9
    95 sgo:license sg:explorer/license/
    96 sgo:sdDataset articles
    97 rdf:type schema:ScholarlyArticle
    98 N0b13688a6f4340c2a872a00dfe1bb190 rdf:first sg:person.015427123271.52
    99 rdf:rest rdf:nil
    100 N216305187edc45388903dfec4c0768fd schema:name Springer Nature - SN SciGraph project
    101 rdf:type schema:Organization
    102 N5038d0f67de54b04aaa8bffc4e51f5a3 schema:volumeNumber 65
    103 rdf:type schema:PublicationVolume
    104 N6ee6b278a6d94439afa96e801a4803e6 rdf:first sg:person.011006110361.28
    105 rdf:rest N0b13688a6f4340c2a872a00dfe1bb190
    106 N7bb66d0f6d9e444c9fa4b4cf6f7637c3 schema:issueNumber 7
    107 rdf:type schema:PublicationIssue
    108 Na8baba1c19b84c0bb0118c99f4fa53c8 schema:name doi
    109 schema:value 10.1007/s00158-022-03307-9
    110 rdf:type schema:PropertyValue
    111 Ndcc93123d3804fd791791188f509fb38 schema:name dimensions_id
    112 schema:value pub.1149471805
    113 rdf:type schema:PropertyValue
    114 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
    115 schema:name Mathematical Sciences
    116 rdf:type schema:DefinedTerm
    117 anzsrc-for:0103 schema:inDefinedTermSet anzsrc-for:
    118 schema:name Numerical and Computational Mathematics
    119 rdf:type schema:DefinedTerm
    120 sg:grant.8899002 http://pending.schema.org/fundedItem sg:pub.10.1007/s00158-022-03307-9
    121 rdf:type schema:MonetaryGrant
    122 sg:journal.1050630 schema:issn 1615-147X
    123 1615-1488
    124 schema:name Structural and Multidisciplinary Optimization
    125 schema:publisher Springer Nature
    126 rdf:type schema:Periodical
    127 sg:person.011006110361.28 schema:affiliation grid-institutes:grid.43555.32
    128 schema:familyName Yang
    129 schema:givenName Chen
    130 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011006110361.28
    131 rdf:type schema:Person
    132 sg:person.015427123271.52 schema:affiliation grid-institutes:grid.43555.32
    133 schema:familyName Xia
    134 schema:givenName Yuanqing
    135 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015427123271.52
    136 rdf:type schema:Person
    137 sg:pub.10.1007/s00158-017-1849-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092614397
    138 https://doi.org/10.1007/s00158-017-1849-3
    139 rdf:type schema:CreativeWork
    140 sg:pub.10.1007/s00158-018-2024-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1105050145
    141 https://doi.org/10.1007/s00158-018-2024-1
    142 rdf:type schema:CreativeWork
    143 sg:pub.10.1007/s00158-020-02766-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1132237176
    144 https://doi.org/10.1007/s00158-020-02766-2
    145 rdf:type schema:CreativeWork
    146 sg:pub.10.1007/s00158-021-02980-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1139364621
    147 https://doi.org/10.1007/s00158-021-02980-6
    148 rdf:type schema:CreativeWork
    149 sg:pub.10.1007/s00158-021-03092-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1144344969
    150 https://doi.org/10.1007/s00158-021-03092-x
    151 rdf:type schema:CreativeWork
    152 sg:pub.10.1007/s00158-021-03145-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1144003252
    153 https://doi.org/10.1007/s00158-021-03145-1
    154 rdf:type schema:CreativeWork
    155 sg:pub.10.1007/s00158-021-03156-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1144755090
    156 https://doi.org/10.1007/s00158-021-03156-y
    157 rdf:type schema:CreativeWork
    158 sg:pub.10.1007/s00158-021-03159-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1145981888
    159 https://doi.org/10.1007/s00158-021-03159-9
    160 rdf:type schema:CreativeWork
    161 sg:pub.10.1007/s10957-017-1192-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092811465
    162 https://doi.org/10.1007/s10957-017-1192-2
    163 rdf:type schema:CreativeWork
    164 sg:pub.10.1007/s11227-016-1900-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1013268303
    165 https://doi.org/10.1007/s11227-016-1900-y
    166 rdf:type schema:CreativeWork
    167 grid-institutes:grid.43555.32 schema:alternateName School of Automation, Beijing Institute of Technology, 100081, Beijing, China
    168 schema:name School of Automation, Beijing Institute of Technology, 100081, Beijing, China
    169 rdf:type schema:Organization
     




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


    ...