Frequency Estimation of Vibration Signals: A Subspace Approach for Bearing Fault Diagnosis

This article investigates the fault characteristic frequency extraction from the noisy vibration signal for bearing fault diagnosis. Although sparse representation (SR) approaches are widely utilized and can achieve exceptional frequency extraction performance, they often encounter issues, such as error accumulation or limited frequency resolution. State-of-the-art Bayesian learning methods can address these shortcomings, but they come with high-computational complexity. Consequently, in this article, we introduce a new low-complexity subspace-based approach for detecting fault characteristic frequencies, offering a more accurate and practical solution to bearing fault diagnosis. The novelties of the proposed method are twofold: 1) present a subspace-based frequency extraction formulation by adopting multiple-level Hilbert transformation and spatial smoothing, which paves the way to extract the fault characteristic frequencies within frequency domain directly and efficiently, and 2) utilize a special unitary matrix to construct a real-valued signal model and exploit the special rotation invariance under such a new real-valued model to facilitate noise suppression, computational complexity reduction, and recovery performance enhancement. Results on both simulated and real datasets demonstrate the superiority of the proposed method.