INTELLIGENT CONDITION BASED MAINTENANCE - A SOFT COMPUTING APPROACH TO SYSTEM DIAGNOSIS AND PROGNOSIS
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University of Cincinnati / OhioLINK
2006
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| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=ucin1141339344 |
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ndltd-OhioLink-oai-etd.ohiolink.edu-ucin11413393442021-08-03T06:10:56Z INTELLIGENT CONDITION BASED MAINTENANCE - A SOFT COMPUTING APPROACH TO SYSTEM DIAGNOSIS AND PROGNOSIS KOTHAMASU, RANGANATH Condition Based Maintenance Model based maintenance Neuro-Fuzzy Backpropagation Information Entropy Model selection Maintenance is the set of activities performed on a system to sustain it in operable condition while Condition Based Maintenance (CBM) refers to the practice of triggering these activities as necessitated by the condition of the target system. CBM thus entails the process of diagnosis (of the target system) and timely identification of incipient or existing failures popularly known as Failure Detection and Identification (FDI). FDI has been given due research focus; however there is a dearth of autonomous yet interactive decision making tools that would perform diagnosis and prognosis under the precepts of CBM in a guided environment. The development of such an architecture along with the tools necessary for decision making in the realm of condition based maintenance constitute the focus of this research. The architecture and the tools developed in this research encompass the model based approach to FDI. These tools are built on Neuro-Fuzzy (NF) paradigms as they offer many advantages in the form of accuracy, adaptability and lucidity compared to other parametric and non-parametric approaches. Along with the development of a NF algorithm, suitable evaluation criteria are also explored and developed to gauge the applicability and efficiency of the developed models. Intelligent Condition Based Maintenance (ICBM) thus refers to the creation of adaptive and robust FDI models based on a model based architecture and their subsequent validation using suitable evaluation criteria. The efficiency and robustness of these ICBM tools are demonstrated by applying them in several scenarios – Simulated as well as real world. 2006-04-03 English text University of Cincinnati / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ucin1141339344 http://rave.ohiolink.edu/etdc/view?acc_num=ucin1141339344 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. |
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NDLTD |
| language |
English |
| sources |
NDLTD |
| topic |
Condition Based Maintenance Model based maintenance Neuro-Fuzzy Backpropagation Information Entropy Model selection |
| spellingShingle |
Condition Based Maintenance Model based maintenance Neuro-Fuzzy Backpropagation Information Entropy Model selection KOTHAMASU, RANGANATH INTELLIGENT CONDITION BASED MAINTENANCE - A SOFT COMPUTING APPROACH TO SYSTEM DIAGNOSIS AND PROGNOSIS |
| author |
KOTHAMASU, RANGANATH |
| author_facet |
KOTHAMASU, RANGANATH |
| author_sort |
KOTHAMASU, RANGANATH |
| title |
INTELLIGENT CONDITION BASED MAINTENANCE - A SOFT COMPUTING APPROACH TO SYSTEM DIAGNOSIS AND PROGNOSIS |
| title_short |
INTELLIGENT CONDITION BASED MAINTENANCE - A SOFT COMPUTING APPROACH TO SYSTEM DIAGNOSIS AND PROGNOSIS |
| title_full |
INTELLIGENT CONDITION BASED MAINTENANCE - A SOFT COMPUTING APPROACH TO SYSTEM DIAGNOSIS AND PROGNOSIS |
| title_fullStr |
INTELLIGENT CONDITION BASED MAINTENANCE - A SOFT COMPUTING APPROACH TO SYSTEM DIAGNOSIS AND PROGNOSIS |
| title_full_unstemmed |
INTELLIGENT CONDITION BASED MAINTENANCE - A SOFT COMPUTING APPROACH TO SYSTEM DIAGNOSIS AND PROGNOSIS |
| title_sort |
intelligent condition based maintenance - a soft computing approach to system diagnosis and prognosis |
| publisher |
University of Cincinnati / OhioLINK |
| publishDate |
2006 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1141339344 |
| work_keys_str_mv |
AT kothamasuranganath intelligentconditionbasedmaintenanceasoftcomputingapproachtosystemdiagnosisandprognosis |
| _version_ |
1719432274915622912 |