By George Vachtsevanos, Frank L. Lewis, Michael Roemer, Andrew Hess, Biqing Wu
ISBN-10: 0047172991
ISBN-13: 9780047172999
ISBN-10: 047172999X
ISBN-13: 9780471729990
Professional tips on concept and perform in condition-based clever laptop fault prognosis and failure analysis clever Fault analysis and diagnosis for Engineering platforms offers a whole presentation of easy necessities of fault analysis and failure diagnosis, and takes a glance on the state-of-the-art self-discipline of clever fault prognosis and failure analysis applied sciences for condition-based upkeep. It completely information the interdisciplinary equipment required to appreciate the physics of failure mechanisms in fabrics, constructions, and rotating gear, and in addition provides techniques to realize faults or incipient mess ups and are expecting the rest worthy lifetime of failing elements. Case reviews are used in the course of the booklet to demonstrate allowing applied sciences. clever Fault analysis and diagnosis for Engineering structures deals fabric in a holistic and built-in process that addresses a few of the interdisciplinary elements of the field--from electric, mechanical, business, and laptop engineering to company administration. This invaluably useful ebook: * contains state of the art algorithms, methodologies, and contributions from prime specialists, together with cost-benefit research instruments and function evaluation options * Covers concept and perform in a manner that's rooted in examine and adventure * offers the one systematic, holistic method of a strongly interdisciplinary subject
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Additional info for Intelligent Fault Diagnosis and Prognosis for Engineering Systems
Sample text
Choose a set of quality attributes with which to assess the architectures (pick success criteria). Choose a set of concrete tasks that test the desired quality attributes. Evaluate the degree to which each architecture provides support for each task. 2 PERFORMANCE ASSESSMENT 35 Performance Metrics Metrics for fault diagnosis and prognosis functional elements form the core performance assessment of a deployed CBM/PHM system. Simulation results and real fault data are employed to project the fielded performance of the system.
Eventually, moving up the hierarchy, the ‘‘system of systems’’ considers the component couplings or interactions. Verification is much less involved than the more complex statistical nature of the validation process, and software quality assurance tools are used to address programming errors and to verify the correctness of the software. A combination of white and black box testing strategies is necessary to carry out the verification as completely as possible. The statistical approach to system validation is depicted schematically in Fig.
13 shows one example of the classification results from application of the diagnostic modules, which is based on the tank 1 level data shown in Fig. 12. 13 show the diagnostic results of the tank 1 leakage fault from three classifiers implemented in the PEDS and the overall results combined using Dempster’s rule of combination. Details of the DempsterShafer evidential theory are discussed in Sec. 4 of Chap. 5. The falsepositive rate (FPR), false-negative rate (FNR), and the fault accuracy rate (FAR) are calculated for each data set.
Intelligent Fault Diagnosis and Prognosis for Engineering Systems by George Vachtsevanos, Frank L. Lewis, Michael Roemer, Andrew Hess, Biqing Wu
by John
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