Sparsity-based Algorithm for Detecting Faults in Rotating Machines

TL;DR

This paper introduces a convex optimization-based method for detecting faults in rotating machines by extracting periodic transient features from vibration signals, demonstrating improved accuracy in simulated and real-world data.

Contribution

It presents a novel convex optimization approach for periodic-group-sparse signal estimation in noisy environments for fault detection in rotating machinery.

Findings

Effective detection of bearing faults in simulated data.

Successful application to real machinery fault data.

Outperforms comparative methods in accuracy.

Abstract

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.