Sparsity-based Algorithm for Detecting Faults in Rotating Machines
This work addresses fault detection in rotating machinery for maintenance and safety applications, but it is incremental as it builds on existing sparsity-based methods.
The paper tackles the problem of detecting periodic transients in vibration signals to identify faults in rotating machines, presenting a convex optimization-based method that effectively extracts features for bearing defects, as validated through simulations and real data from a machinery fault simulator.
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.