Unsupervised fault detection using semidefinite programming

Autor:	José Andrés González López, Mario Sznaier, Octavia Camps
Rok vydání:	2015
Předmět:	Semidefinite programming Kernel (linear algebra) Dynamical systems theory Noise measurement business.industry Feature extraction Pattern recognition Artificial intelligence Method of moments (statistics) business Fault detection and isolation Mathematics Data-driven
Zdroj:	CDC
DOI:	10.1109/cdc.2015.7402809
Popis:	We present an unsupervised, data driven method for detecting faults in dynamical systems based upon recent results in polynomial optimization. The proposed technique only requires information about the statistical moments of the normal-state distribution of the system. We demonstrate our method by detecting damage in a bearing test rig.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::59a01c9e6f7f0ddd65692a44f69c1023 https://doi.org/10.1109/cdc.2015.7402809 Zobrazit plný text záznamu