{
  "@type": "dcat:Dataset",
  "accessLevel": "public",
  "accrualPeriodicity": "irregular",
  "bureauCode": [
    "026:00"
  ],
  "contactPoint": {
    "@type": "vcard:Contact",
    "fn": "Miryam Strautkalns",
    "hasEmail": "mailto:miryam.strautkalns@nasa.gov"
  },
  "description": "Complex engineering systems require efficient fault diagnosis methodologies, but centralized ap- proaches do not scale well, and this motivates the development of distributed solutions. This work presents an event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, by using the structural model decompo- sition capabilities provided by Possible Conflicts. We develop a distributed diagnosis algorithm that uses residuals, computed by extending Possible Conflicts, to build local event-based diagnosers based on global diagnosability analysis that gen- erate globally correct local diagnosis results. The proposed approach is applied to a multi-tank sys- tem, and results demonstrate an improvement in the design of local diagnosers. Since local diag- nosers use only a subset of the residuals, and use subsystem models to compute residuals (instead of the global system model), the local diagnosers are more efficient than previously developed dis- tributed approaches.",
  "distribution": [
    {
      "@type": "dcat:Distribution",
      "description": "12-BregonEtAl-ImprovingDistribDiagnosis-DX2011.pdf",
      "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/12-BregonEtAl-ImprovingDistribDiagnosis-DX2011.pdf",
      "format": "PDF",
      "mediaType": "application/pdf",
      "title": "12-BregonEtAl-ImprovingDistribDiagnosis-DX2011.pdf"
    }
  ],
  "identifier": "DASHLINK_816",
  "issued": "2013-07-29",
  "keyword": [
    "ames",
    "dashlink",
    "nasa"
  ],
  "landingPage": "https://c3.nasa.gov/dashlink/resources/816/",
  "modified": "2025-03-31",
  "programCode": [
    "026:029"
  ],
  "publisher": {
    "@type": "org:Organization",
    "name": "Dashlink"
  },
  "title": "Improving Distributed Diagnosis Through Structural Model Decomposition"
}