Redundancy relations and robust failure detection View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

1985

AUTHORS

Edward Y. Chow , Xi-Cheng Lou , George C. Verghese , Alan S. Willsky

ABSTRACT

All failure detection methods are based on the use of redundancy, that is on (possible dynamic) relations among the measured variables. Consequently the robustness of the failure detection process depends to a great degree on the reliability of the redundancy relations given the inevitable presence of model uncertainties. In this paper we address the problem of determining redundancy relations which are optimally robust in a sense which includes the major issues of importance in practical failure detection and which provides us with a significant amount of intuition concerning the geometry of robust failure detection. More... »

PAGES

275-293

Book

TITLE

Detection of Abrupt Changes in Signals and Dynamical Systems

ISBN

3-540-16043-4

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bfb0006396

DOI

http://dx.doi.org/10.1007/bfb0006396

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1052821872


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/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Massachusetts Institute of Technology", 
          "id": "https://www.grid.ac/institutes/grid.116068.8", 
          "name": [
            "Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 02139\u00a0Cambridge, Massachusetts"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chow", 
        "givenName": "Edward Y.", 
        "id": "sg:person.011136601066.73", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011136601066.73"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Massachusetts Institute of Technology", 
          "id": "https://www.grid.ac/institutes/grid.116068.8", 
          "name": [
            "Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 02139\u00a0Cambridge, Massachusetts"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lou", 
        "givenName": "Xi-Cheng", 
        "id": "sg:person.014775042133.81", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014775042133.81"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Massachusetts Institute of Technology", 
          "id": "https://www.grid.ac/institutes/grid.116068.8", 
          "name": [
            "Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 02139\u00a0Cambridge, Massachusetts"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Verghese", 
        "givenName": "George C.", 
        "id": "sg:person.0725124105.86", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0725124105.86"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Massachusetts Institute of Technology", 
          "id": "https://www.grid.ac/institutes/grid.116068.8", 
          "name": [
            "Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 02139\u00a0Cambridge, Massachusetts"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Willsky", 
        "givenName": "Alan S.", 
        "id": "sg:person.010070142507.94", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010070142507.94"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "1985", 
    "datePublishedReg": "1985-01-01", 
    "description": "All failure detection methods are based on the use of redundancy, that is on (possible dynamic) relations among the measured variables. Consequently the robustness of the failure detection process depends to a great degree on the reliability of the redundancy relations given the inevitable presence of model uncertainties. In this paper we address the problem of determining redundancy relations which are optimally robust in a sense which includes the major issues of importance in practical failure detection and which provides us with a significant amount of intuition concerning the geometry of robust failure detection.", 
    "editor": [
      {
        "familyName": "Basseville", 
        "givenName": "Mich\u00e8le", 
        "type": "Person"
      }, 
      {
        "familyName": "Benveniste", 
        "givenName": "Albert", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/bfb0006396", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "3-540-16043-4"
      ], 
      "name": "Detection of Abrupt Changes in Signals and Dynamical Systems", 
      "type": "Book"
    }, 
    "name": "Redundancy relations and robust failure detection", 
    "pagination": "275-293", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/bfb0006396"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "fb9b3b44b57849f9240fd1ba5694f98eced067b1b34317618c922b42e55f0615"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1052821872"
        ]
      }
    ], 
    "publisher": {
      "location": "Berlin/Heidelberg", 
      "name": "Springer-Verlag", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/bfb0006396", 
      "https://app.dimensions.ai/details/publication/pub.1052821872"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-16T00:38", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8700_00000091.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/BFb0006396"
  }
]
 

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/bfb0006396'

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/bfb0006396'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bfb0006396'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bfb0006396'


 

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

90 TRIPLES      22 PREDICATES      27 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/bfb0006396 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author Nc0a2f429944a4fc1bd0dbc85bb5352a1
4 schema:datePublished 1985
5 schema:datePublishedReg 1985-01-01
6 schema:description All failure detection methods are based on the use of redundancy, that is on (possible dynamic) relations among the measured variables. Consequently the robustness of the failure detection process depends to a great degree on the reliability of the redundancy relations given the inevitable presence of model uncertainties. In this paper we address the problem of determining redundancy relations which are optimally robust in a sense which includes the major issues of importance in practical failure detection and which provides us with a significant amount of intuition concerning the geometry of robust failure detection.
7 schema:editor N0b80bb388c5b48459ab925ee556eb190
8 schema:genre chapter
9 schema:inLanguage en
10 schema:isAccessibleForFree true
11 schema:isPartOf N6e424f9d6f914044a0d5e114892b71da
12 schema:name Redundancy relations and robust failure detection
13 schema:pagination 275-293
14 schema:productId Na76c7c0995c74cb185092c3d2b0148ba
15 Ncbcc536be6cd41deb31cb794e994720f
16 Ne2265f192b0e42a9bea5f071fd11da7b
17 schema:publisher N0945e420d1be44f88a9ae6f283de56bc
18 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052821872
19 https://doi.org/10.1007/bfb0006396
20 schema:sdDatePublished 2019-04-16T00:38
21 schema:sdLicense https://scigraph.springernature.com/explorer/license/
22 schema:sdPublisher N0797dc5307e54d88bd9f1a125b6c3a8d
23 schema:url http://link.springer.com/10.1007/BFb0006396
24 sgo:license sg:explorer/license/
25 sgo:sdDataset chapters
26 rdf:type schema:Chapter
27 N0797dc5307e54d88bd9f1a125b6c3a8d schema:name Springer Nature - SN SciGraph project
28 rdf:type schema:Organization
29 N0945e420d1be44f88a9ae6f283de56bc schema:location Berlin/Heidelberg
30 schema:name Springer-Verlag
31 rdf:type schema:Organisation
32 N0b80bb388c5b48459ab925ee556eb190 rdf:first N626d3fd4a77f4ded98d59936a164a17d
33 rdf:rest N99da8ba6afc949a79a2b283b8fd75637
34 N55bcd98f9b454d1cb299ed18d298cd9b schema:familyName Benveniste
35 schema:givenName Albert
36 rdf:type schema:Person
37 N626d3fd4a77f4ded98d59936a164a17d schema:familyName Basseville
38 schema:givenName Michèle
39 rdf:type schema:Person
40 N6e424f9d6f914044a0d5e114892b71da schema:isbn 3-540-16043-4
41 schema:name Detection of Abrupt Changes in Signals and Dynamical Systems
42 rdf:type schema:Book
43 N99da8ba6afc949a79a2b283b8fd75637 rdf:first N55bcd98f9b454d1cb299ed18d298cd9b
44 rdf:rest rdf:nil
45 Na76c7c0995c74cb185092c3d2b0148ba schema:name readcube_id
46 schema:value fb9b3b44b57849f9240fd1ba5694f98eced067b1b34317618c922b42e55f0615
47 rdf:type schema:PropertyValue
48 Nbcf2f094b8fd42ee82d8f29d93630e1e rdf:first sg:person.0725124105.86
49 rdf:rest Nc4c0c02d5675408da39671e8476762a3
50 Nc0a2f429944a4fc1bd0dbc85bb5352a1 rdf:first sg:person.011136601066.73
51 rdf:rest Nca158fc07894440aa4fb61810c298fb2
52 Nc4c0c02d5675408da39671e8476762a3 rdf:first sg:person.010070142507.94
53 rdf:rest rdf:nil
54 Nca158fc07894440aa4fb61810c298fb2 rdf:first sg:person.014775042133.81
55 rdf:rest Nbcf2f094b8fd42ee82d8f29d93630e1e
56 Ncbcc536be6cd41deb31cb794e994720f schema:name dimensions_id
57 schema:value pub.1052821872
58 rdf:type schema:PropertyValue
59 Ne2265f192b0e42a9bea5f071fd11da7b schema:name doi
60 schema:value 10.1007/bfb0006396
61 rdf:type schema:PropertyValue
62 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
63 schema:name Information and Computing Sciences
64 rdf:type schema:DefinedTerm
65 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
66 schema:name Artificial Intelligence and Image Processing
67 rdf:type schema:DefinedTerm
68 sg:person.010070142507.94 schema:affiliation https://www.grid.ac/institutes/grid.116068.8
69 schema:familyName Willsky
70 schema:givenName Alan S.
71 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010070142507.94
72 rdf:type schema:Person
73 sg:person.011136601066.73 schema:affiliation https://www.grid.ac/institutes/grid.116068.8
74 schema:familyName Chow
75 schema:givenName Edward Y.
76 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011136601066.73
77 rdf:type schema:Person
78 sg:person.014775042133.81 schema:affiliation https://www.grid.ac/institutes/grid.116068.8
79 schema:familyName Lou
80 schema:givenName Xi-Cheng
81 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014775042133.81
82 rdf:type schema:Person
83 sg:person.0725124105.86 schema:affiliation https://www.grid.ac/institutes/grid.116068.8
84 schema:familyName Verghese
85 schema:givenName George C.
86 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0725124105.86
87 rdf:type schema:Person
88 https://www.grid.ac/institutes/grid.116068.8 schema:alternateName Massachusetts Institute of Technology
89 schema:name Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 02139 Cambridge, Massachusetts
90 rdf:type schema:Organization
 




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


...