Unsupervised fault detection using semidefinite programming

Jose A. Lopez, Mario Sznaier, Octavia I. Camps

Abstract: 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.