Diagnosis in SEMS Based on Cognitive Models View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-09-16

AUTHORS

Vladimir V. Korobkin , Anna E. Kolodenkova

ABSTRACT

Problem statement: SEMS is a complex dynamic object in the operational phase of which it is likely that abnormal situations may occur in which the state of the equipment goes beyond the normal functioning, which can subsequently lead to an accident. Despite the importance of the need and importance of effectively solving the problems of diagnostics of SEMS, at the present time there is no single approach to solving similar problems taking into account the variety of emergent contingencies. Therefore, the actual task is the development of methods, algorithms and special diagnostic tools that allow predicting the development of defects, diagnose processes and recognize violations of normal operation at an early stage of their development to ensure the efficiency, reliability and safety of the operation of SEMS in real time. Results: cognitive to diagnose SEMS in conditions of interval uncertainty and fuzzy initial data, cognitive and fuzzy cognitive modeling is used to reflect the problems of SEMS in a simplified form (in the model), to investigate possible scenarios for the emergence of risk situations at an early stage of their development, and to find ways to resolve them in the model of the situation. As an example, a fuzzy cognitive model of SEMS diagnostics is proposed. Pessimistic and optimistic scenarios of possible development of risk situations, developed with the help of impulse simulation, are given and their brief analysis is given. The system indices of the fuzzy cognitive model are calculated, allowing to identify which of the factors have the greatest impact on SEMS and vice versa; To search for the best values of factors reflecting the normal operation of SEMS. Practical significance: the ability to systematically take into account the long-term consequences of possible abnormal situations and identify side effects that allow to take into account the multifactority of the process of diagnosing SEMS in the process of operation. The task of identifying possible risks in general, and at the operational stage in particular, should be an important part of the diagnostics of SEMS equipment. More... »

PAGES

275-284

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-99759-9_22

DOI

http://dx.doi.org/10.1007/978-3-319-99759-9_22

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1107040386


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/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/17", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Psychology and Cognitive Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "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/2008/1701", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Psychology", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Acad. Kalyaev Scientific Research Institute of Multiprocessor Computer Systems, Taganrog, Russia", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Acad. Kalyaev Scientific Research Institute of Multiprocessor Computer Systems, Taganrog, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Korobkin", 
        "givenName": "Vladimir V.", 
        "id": "sg:person.016015727221.88", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016015727221.88"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department Chair \u00abInformation Technologies\u00bb, Samara State Technical University, Samara, Russia", 
          "id": "http://www.grid.ac/institutes/grid.445792.9", 
          "name": [
            "Department Chair \u00abInformation Technologies\u00bb, Samara State Technical University, Samara, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kolodenkova", 
        "givenName": "Anna E.", 
        "id": "sg:person.07525540060.35", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07525540060.35"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2018-09-16", 
    "datePublishedReg": "2018-09-16", 
    "description": "Problem statement: SEMS is a complex dynamic object in the operational phase of which it is likely that abnormal situations may occur in which the state of the equipment goes beyond the normal functioning, which can subsequently lead to an accident. Despite the importance of the need and importance of effectively solving the problems of diagnostics of SEMS, at the present time there is no single approach to solving similar problems taking into account the variety of emergent contingencies. Therefore, the actual task is the development of methods, algorithms and special diagnostic tools that allow predicting the development of defects, diagnose processes and recognize violations of normal operation at an early stage of their development to ensure the efficiency, reliability and safety of the operation of SEMS in real time. Results: cognitive to diagnose SEMS in conditions of interval uncertainty and fuzzy initial data, cognitive and fuzzy cognitive modeling is used to reflect the problems of SEMS in a simplified form (in the model), to investigate possible scenarios for the emergence of risk situations at an early stage of their development, and to find ways to resolve them in the model of the situation. As an example, a fuzzy cognitive model of SEMS diagnostics is proposed. Pessimistic and optimistic scenarios of possible development of risk situations, developed with the help of impulse simulation, are given and their brief analysis is given. The system indices of the fuzzy cognitive model are calculated, allowing to identify which of the factors have the greatest impact on SEMS and vice versa; To search for the best values of factors reflecting the normal operation of SEMS. Practical significance: the ability to systematically take into account the long-term consequences of possible abnormal situations and identify side effects that allow to take into account the multifactority of the process of diagnosing SEMS in the process of operation. The task of identifying possible risks in general, and at the operational stage in particular, should be an important part of the diagnostics of SEMS equipment.", 
    "editor": [
      {
        "familyName": "Gorodetskiy", 
        "givenName": "Andrey E.", 
        "type": "Person"
      }, 
      {
        "familyName": "Tarasova", 
        "givenName": "Irina L.", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-99759-9_22", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-319-99758-2", 
        "978-3-319-99759-9"
      ], 
      "name": "Smart Electromechanical Systems", 
      "type": "Book"
    }, 
    "keywords": [
      "fuzzy cognitive model", 
      "cognitive model", 
      "abnormal situations", 
      "fuzzy cognitive modeling", 
      "possible abnormal situations", 
      "complex dynamic objects", 
      "dynamic objects", 
      "diagnose process", 
      "real time", 
      "normal operation", 
      "cognitive modeling", 
      "process of operation", 
      "risk situations", 
      "fuzzy initial data", 
      "actual task", 
      "operational phase", 
      "long-term consequences", 
      "interval uncertainty", 
      "emergent contingencies", 
      "problem of diagnostics", 
      "task", 
      "special diagnostic tool", 
      "impulse simulation", 
      "similar problems", 
      "scenarios", 
      "operation", 
      "important part", 
      "algorithm", 
      "development of methods", 
      "operational stage", 
      "single approach", 
      "possible scenarios", 
      "objects", 
      "Based", 
      "normal functioning", 
      "brief analysis", 
      "situation", 
      "functioning", 
      "initial data", 
      "model", 
      "contingencies", 
      "equipment", 
      "multifactority", 
      "tool", 
      "best value", 
      "reliability", 
      "modeling", 
      "simulations", 
      "process", 
      "account", 
      "system indices", 
      "help", 
      "problem", 
      "efficiency", 
      "early stages", 
      "example", 
      "great impact", 
      "time", 
      "development", 
      "way", 
      "ability", 
      "violation", 
      "uncertainty", 
      "importance", 
      "method", 
      "need", 
      "data", 
      "consequences", 
      "factors", 
      "diagnostics", 
      "SEMS", 
      "accidents", 
      "variety", 
      "vice", 
      "stage", 
      "development of defects", 
      "impact", 
      "part", 
      "state", 
      "emergence", 
      "safety", 
      "possible risks", 
      "possible development", 
      "present time", 
      "effect", 
      "approach", 
      "analysis", 
      "risk", 
      "form", 
      "index", 
      "phase", 
      "conditions", 
      "values", 
      "optimistic scenario", 
      "diagnostic tool", 
      "diagnosis", 
      "side effects", 
      "defects", 
      "operation of SEMS", 
      "SEMS diagnostics", 
      "SEMS equipment", 
      "SEMS Based"
    ], 
    "name": "Diagnosis in SEMS Based on Cognitive Models", 
    "pagination": "275-284", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1107040386"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-99759-9_22"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-99759-9_22", 
      "https://app.dimensions.ai/details/publication/pub.1107040386"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2021-11-01T19:00", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20211101/entities/gbq_results/chapter/chapter_425.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-319-99759-9_22"
  }
]
 

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.10.1007/978-3-319-99759-9_22'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-99759-9_22'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-99759-9_22'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-99759-9_22'


 

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

185 TRIPLES      23 PREDICATES      129 URIs      120 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-319-99759-9_22 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 anzsrc-for:17
4 anzsrc-for:1701
5 schema:author N2e76c80e175640bb8d6cd7167d7eb7b8
6 schema:datePublished 2018-09-16
7 schema:datePublishedReg 2018-09-16
8 schema:description Problem statement: SEMS is a complex dynamic object in the operational phase of which it is likely that abnormal situations may occur in which the state of the equipment goes beyond the normal functioning, which can subsequently lead to an accident. Despite the importance of the need and importance of effectively solving the problems of diagnostics of SEMS, at the present time there is no single approach to solving similar problems taking into account the variety of emergent contingencies. Therefore, the actual task is the development of methods, algorithms and special diagnostic tools that allow predicting the development of defects, diagnose processes and recognize violations of normal operation at an early stage of their development to ensure the efficiency, reliability and safety of the operation of SEMS in real time. Results: cognitive to diagnose SEMS in conditions of interval uncertainty and fuzzy initial data, cognitive and fuzzy cognitive modeling is used to reflect the problems of SEMS in a simplified form (in the model), to investigate possible scenarios for the emergence of risk situations at an early stage of their development, and to find ways to resolve them in the model of the situation. As an example, a fuzzy cognitive model of SEMS diagnostics is proposed. Pessimistic and optimistic scenarios of possible development of risk situations, developed with the help of impulse simulation, are given and their brief analysis is given. The system indices of the fuzzy cognitive model are calculated, allowing to identify which of the factors have the greatest impact on SEMS and vice versa; To search for the best values of factors reflecting the normal operation of SEMS. Practical significance: the ability to systematically take into account the long-term consequences of possible abnormal situations and identify side effects that allow to take into account the multifactority of the process of diagnosing SEMS in the process of operation. The task of identifying possible risks in general, and at the operational stage in particular, should be an important part of the diagnostics of SEMS equipment.
9 schema:editor N16895397ee0a4892a05891a738e63bef
10 schema:genre chapter
11 schema:inLanguage en
12 schema:isAccessibleForFree false
13 schema:isPartOf Ne24ae5289dcd45c6846ae30a82d9a46f
14 schema:keywords Based
15 SEMS
16 SEMS Based
17 SEMS diagnostics
18 SEMS equipment
19 ability
20 abnormal situations
21 accidents
22 account
23 actual task
24 algorithm
25 analysis
26 approach
27 best value
28 brief analysis
29 cognitive model
30 cognitive modeling
31 complex dynamic objects
32 conditions
33 consequences
34 contingencies
35 data
36 defects
37 development
38 development of defects
39 development of methods
40 diagnose process
41 diagnosis
42 diagnostic tool
43 diagnostics
44 dynamic objects
45 early stages
46 effect
47 efficiency
48 emergence
49 emergent contingencies
50 equipment
51 example
52 factors
53 form
54 functioning
55 fuzzy cognitive model
56 fuzzy cognitive modeling
57 fuzzy initial data
58 great impact
59 help
60 impact
61 importance
62 important part
63 impulse simulation
64 index
65 initial data
66 interval uncertainty
67 long-term consequences
68 method
69 model
70 modeling
71 multifactority
72 need
73 normal functioning
74 normal operation
75 objects
76 operation
77 operation of SEMS
78 operational phase
79 operational stage
80 optimistic scenario
81 part
82 phase
83 possible abnormal situations
84 possible development
85 possible risks
86 possible scenarios
87 present time
88 problem
89 problem of diagnostics
90 process
91 process of operation
92 real time
93 reliability
94 risk
95 risk situations
96 safety
97 scenarios
98 side effects
99 similar problems
100 simulations
101 single approach
102 situation
103 special diagnostic tool
104 stage
105 state
106 system indices
107 task
108 time
109 tool
110 uncertainty
111 values
112 variety
113 vice
114 violation
115 way
116 schema:name Diagnosis in SEMS Based on Cognitive Models
117 schema:pagination 275-284
118 schema:productId N69bad4473a4b49f7ad2e2aeab07ba483
119 Nc56d5c59ae8149d9ab56efe623999e0e
120 schema:publisher N0f8a34d32c424149af5e0fb5bb287bae
121 schema:sameAs https://app.dimensions.ai/details/publication/pub.1107040386
122 https://doi.org/10.1007/978-3-319-99759-9_22
123 schema:sdDatePublished 2021-11-01T19:00
124 schema:sdLicense https://scigraph.springernature.com/explorer/license/
125 schema:sdPublisher Nfa91a6e716564332962c4ab6729c9f4a
126 schema:url https://doi.org/10.1007/978-3-319-99759-9_22
127 sgo:license sg:explorer/license/
128 sgo:sdDataset chapters
129 rdf:type schema:Chapter
130 N0f8a34d32c424149af5e0fb5bb287bae schema:name Springer Nature
131 rdf:type schema:Organisation
132 N16895397ee0a4892a05891a738e63bef rdf:first Nf2b122bb92084a07bc129189470cca10
133 rdf:rest Nd5af750db4f342d784b944fb5a4043fa
134 N23de3f6463e54967ac98121ee9ab4b45 schema:familyName Tarasova
135 schema:givenName Irina L.
136 rdf:type schema:Person
137 N2e76c80e175640bb8d6cd7167d7eb7b8 rdf:first sg:person.016015727221.88
138 rdf:rest Nd6032e86ac0445bd93ecbe6022b398c4
139 N69bad4473a4b49f7ad2e2aeab07ba483 schema:name dimensions_id
140 schema:value pub.1107040386
141 rdf:type schema:PropertyValue
142 Nc56d5c59ae8149d9ab56efe623999e0e schema:name doi
143 schema:value 10.1007/978-3-319-99759-9_22
144 rdf:type schema:PropertyValue
145 Nd5af750db4f342d784b944fb5a4043fa rdf:first N23de3f6463e54967ac98121ee9ab4b45
146 rdf:rest rdf:nil
147 Nd6032e86ac0445bd93ecbe6022b398c4 rdf:first sg:person.07525540060.35
148 rdf:rest rdf:nil
149 Ne24ae5289dcd45c6846ae30a82d9a46f schema:isbn 978-3-319-99758-2
150 978-3-319-99759-9
151 schema:name Smart Electromechanical Systems
152 rdf:type schema:Book
153 Nf2b122bb92084a07bc129189470cca10 schema:familyName Gorodetskiy
154 schema:givenName Andrey E.
155 rdf:type schema:Person
156 Nfa91a6e716564332962c4ab6729c9f4a schema:name Springer Nature - SN SciGraph project
157 rdf:type schema:Organization
158 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
159 schema:name Information and Computing Sciences
160 rdf:type schema:DefinedTerm
161 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
162 schema:name Artificial Intelligence and Image Processing
163 rdf:type schema:DefinedTerm
164 anzsrc-for:17 schema:inDefinedTermSet anzsrc-for:
165 schema:name Psychology and Cognitive Sciences
166 rdf:type schema:DefinedTerm
167 anzsrc-for:1701 schema:inDefinedTermSet anzsrc-for:
168 schema:name Psychology
169 rdf:type schema:DefinedTerm
170 sg:person.016015727221.88 schema:affiliation grid-institutes:None
171 schema:familyName Korobkin
172 schema:givenName Vladimir V.
173 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016015727221.88
174 rdf:type schema:Person
175 sg:person.07525540060.35 schema:affiliation grid-institutes:grid.445792.9
176 schema:familyName Kolodenkova
177 schema:givenName Anna E.
178 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07525540060.35
179 rdf:type schema:Person
180 grid-institutes:None schema:alternateName Acad. Kalyaev Scientific Research Institute of Multiprocessor Computer Systems, Taganrog, Russia
181 schema:name Acad. Kalyaev Scientific Research Institute of Multiprocessor Computer Systems, Taganrog, Russia
182 rdf:type schema:Organization
183 grid-institutes:grid.445792.9 schema:alternateName Department Chair «Information Technologies», Samara State Technical University, Samara, Russia
184 schema:name Department Chair «Information Technologies», Samara State Technical University, Samara, Russia
185 rdf:type schema:Organization
 




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


...