Damage detection and isolation (DDI) is a task that can be divided into two major portions. Damage detection constitutes the first part of the problem, where the goal is to determine when a structural anomaly has occurred. Once an anomaly has been detected, algorithms must be employed to determine if the anomaly is a structural damage, or is due to system noise, sensor failure, or another non-structural event. Having confirmed the presence of a structural anomaly, the second part of the problem, damage isolation, is to determine where that anomaly has occurred.,/p>
The principles of DDI have been used for decades on critical infrastructures, but the advancements in computational power, instrumentation, and wireless communication and high bandwidth have enabled evolutionary development in structural health monitoring. Structural health monitoring (SHM), in its infancy, meant implementation of a suite of instruments and a data acquisition system. The data acquisition system sampled less data and required manual downloading of the acquired data before any analysis or processing could begin. In the second generation of systems, the manual downloading could be eliminated by using remote access and data transfer. This enabled convenient and frequent data analysis and synthesis. With the recent developments in data acquisition systems, more on-site data analysis and synthesis can be performed, allowing more rapid responses when anomalies are detected.
The specific aim of this research is to develop, calibrate, and validate sophisticated damage detection and isolation procedures that can be implemented on the next generation of structural health monitoring systems.
","abstract_html":"<p>Damage detection and isolation (DDI) is a task that can be divided into two major portions. Damage detection constitutes the first part of the problem, where the goal is to determine when a structural anomaly has occurred. Once an anomaly has been detected, algorithms must be employed to determine if the anomaly is a structural damage, or is due to system noise, sensor failure, or another non-structural event. Having confirmed the presence of a structural anomaly, the second part of the problem, damage isolation, is to determine where that anomaly has occurred.,/p><p>The principles of DDI have been used for decades on critical infrastructures, but the advancements in computational power, instrumentation, and wireless communication and high bandwidth have enabled evolutionary development in structural health monitoring. Structural health monitoring (SHM), in its infancy, meant implementation of a suite of instruments and a data acquisition system. The data acquisition system sampled less data and required manual downloading of the acquired data before any analysis or processing could begin. In the second generation of systems, the manual downloading could be eliminated by using remote access and data transfer. This enabled convenient and frequent data analysis and synthesis. With the recent developments in data acquisition systems, more on-site data analysis and synthesis can be performed, allowing more rapid responses when anomalies are detected.</p><p>The specific aim of this research is to develop, calibrate, and validate sophisticated damage detection and isolation procedures that can be implemented on the next generation of structural health monitoring systems.</p>","abstract_has_math":false,"creators":["Mohan, Rathish"],"institution":"University of Cincinnati","degree_name":"MS","degree_level":"masters","degree_discipline":"Engineering and Applied Science: Electrical Engineering","degree_department":null,"school":null,"contributors":["Helmicki, Arthur"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012","date_published":"2012","updated_at":"2026-07-24T03:36:23Z","subjects":["Engineering","Modal Assurance Criterion","Fault Detection and Isolation","Statistical Fault Detection and Isolation","Fault Detection and Isolation Algorithms","Combinatorial Optimization","Optimized Sensor Placement"],"languages":["English"],"rights":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353156107","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Helmicki, Arthur"]},{"key":"dc:creator","label":"Author","values":["Mohan, Rathish"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2012"]},{"key":"dc:publisher","label":"Institution","values":["University of Cincinnati / OhioLINK"]},{"key":"dc:type","label":"Dc Type","values":["Electronic Thesis or Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering and Applied Science: Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Cincinnati"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Engineering","Modal Assurance Criterion","Fault Detection and Isolation","Statistical Fault Detection and Isolation","Fault Detection and Isolation Algorithms","Combinatorial Optimization","Optimized Sensor Placement"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:rights","label":"Dc Rights","values":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353156107"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Damage detection and isolation (DDI) is a task that can be divided into two major portions. Damage detection constitutes the first part of the problem, where the goal is to determine when a structural anomaly has occurred. Once an anomaly has been detected, algorithms must be employed to determine if the anomaly is a structural damage, or is due to system noise, sensor failure, or another non-structural event. Having confirmed the presence of a structural anomaly, the second part of the problem, damage isolation, is to determine where that anomaly has occurred.,/p>
The principles of DDI have been used for decades on critical infrastructures, but the advancements in computational power, instrumentation, and wireless communication and high bandwidth have enabled evolutionary development in structural health monitoring. Structural health monitoring (SHM), in its infancy, meant implementation of a suite of instruments and a data acquisition system. The data acquisition system sampled less data and required manual downloading of the acquired data before any analysis or processing could begin. In the second generation of systems, the manual downloading could be eliminated by using remote access and data transfer. This enabled convenient and frequent data analysis and synthesis. With the recent developments in data acquisition systems, more on-site data analysis and synthesis can be performed, allowing more rapid responses when anomalies are detected.
The specific aim of this research is to develop, calibrate, and validate sophisticated damage detection and isolation procedures that can be implemented on the next generation of structural health monitoring systems.
"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf","p.127","4.93 MB"]},{"key":"dc:title","label":"Title","values":["Algorithmic Optimization of Sensor Placement on Civil Structures for Fault Detection and Isolation"]}]}],"canonical_facts":{"dc:contributor":["Helmicki, Arthur"],"dc:creator":["Mohan, Rathish"],"dc:date":["2012"],"dc:description":["Damage detection and isolation (DDI) is a task that can be divided into two major portions. Damage detection constitutes the first part of the problem, where the goal is to determine when a structural anomaly has occurred. Once an anomaly has been detected, algorithms must be employed to determine if the anomaly is a structural damage, or is due to system noise, sensor failure, or another non-structural event. Having confirmed the presence of a structural anomaly, the second part of the problem, damage isolation, is to determine where that anomaly has occurred.,/p>
The principles of DDI have been used for decades on critical infrastructures, but the advancements in computational power, instrumentation, and wireless communication and high bandwidth have enabled evolutionary development in structural health monitoring. Structural health monitoring (SHM), in its infancy, meant implementation of a suite of instruments and a data acquisition system. The data acquisition system sampled less data and required manual downloading of the acquired data before any analysis or processing could begin. In the second generation of systems, the manual downloading could be eliminated by using remote access and data transfer. This enabled convenient and frequent data analysis and synthesis. With the recent developments in data acquisition systems, more on-site data analysis and synthesis can be performed, allowing more rapid responses when anomalies are detected.
The specific aim of this research is to develop, calibrate, and validate sophisticated damage detection and isolation procedures that can be implemented on the next generation of structural health monitoring systems.
"],"dc:format":["application/pdf","p.127","4.93 MB"],"dc:identifier":["http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353156107"],"dc:language":["English"],"dc:publisher":["University of Cincinnati / OhioLINK"],"dc:rights":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."],"dc:subject":["Engineering","Modal Assurance Criterion","Fault Detection and Isolation","Statistical Fault Detection and Isolation","Fault Detection and Isolation Algorithms","Combinatorial Optimization","Optimized Sensor Placement"],"dc:title":["Algorithmic Optimization of Sensor Placement on Civil Structures for Fault Detection and Isolation"],"dc:type":["Electronic Thesis or Dissertation"],"thesis:degree_discipline":["Engineering and Applied Science: Electrical Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["MS"],"thesis:institution_name":["University of Cincinnati"]},"updated_at":"2026-07-24T03:36:23Z"}