  This paper addresses the detection of periodic transients in vibration
signals for detecting faults in rotating machines. For this purpose, we present
a method to estimate periodic-group-sparse signals in noise. The method is
based on the formulation of a convex optimization problem. A fast iterative
algorithm is given for its solution. A simulated signal is formulated to verify
the performance of the proposed approach for periodic feature extraction. The
detection performance of comparative methods is compared with that of the
proposed approach via RMSE values and receiver operating characteristic (ROC)
curves. Finally, the proposed approach is applied to compound faults diagnosis
of motor bearings. The non-stationary vibration data were acquired from a
SpectraQuest's machinery fault simulator. The processed results show the
proposed approach can effectively detect and extract the useful features of
bearing outer race and inner race defect.
