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Automated System Monitoring and Diagnosis via Singular Value Decomposition by John L. Maryak; Lawrence W. Hunter; Stanley Favin is a Engineering article available to read on EtoBox.

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## We present a new methodology for use in automated monitoring of a system to detect abnormal or degraded system behavior. The methodology uses the singular value decomposition technique of matrix algebra to discover relationships between the elements of data observed from a normally operating system. It then tests these relationships against newly acquired data to detect system malfunctions. Based on a theory for system identification in the presence of nuisance parameters, distribution theory developed specifically for this application allows the system monitor to properly set threshold levels so as to control the false alarm rate. The algorithm is numerically stable and relatively easy to apply, and was tested successfully on data from a motorized globe valve in a nuclear power plant. 0 1997 Elsevier Science Ltd. In practice, observations on a normally operating system are recorded, transformed into "feature vectors", and used for training the algorithm to recognize recurring relationships in the data. Then the monitor is put on line, and real-time observations (in feature-vector form) are tested against the relationships as \*

Who reads Automated System Monitoring and Diagnosis via Singular Value Decomposition?

Author

John L. Maryak; Lawrence W. Hunter; Stanley Favin

Publisher

Elsevier Science; Elsevier ; Elsevier Ltd.; Test accounts; Elsevier BV (ISSN 0005-1098)

Published

1997

Language

EN

Field

Engineering (Physical Sciences)