Fault Detection via Sparsity-based Blind Filtering on Experimental Vibration Signals

Abstract

The detection of incipient rolling element bearing faults is a challenging task since the impulsive pattern of bearing faults often fades into the noise. Moreover, tracking the health conditions of rotating machinery generally requires the characteristic frequencies of the components of interest, which can be a cumbersome constraint for large industrial applications because of the extensive number of machine components. One recent method proposed in literature addresses these difficulties by aiming to increase the sparsity of the squared envelope spectrum of the vibration signal via blind filtering. As the name indicates, this method requires no prior knowledge about the machine. Sparsity measures of Hoyer index, l2 l1 - norm, and spectral negentropy are optimized in the blind filtering approach using generalized Rayleigh quotient iteration. Even though the proposed method has demonstrated a promising performance, it has only been applied to vibration signals of an academic experimental test rig. This paper focuses on the real-world performance of the sparsitybased blind filtering approach on a complex industrial machine. One of the challenges is to ensure the numerical stability and the convergence of the generalized Rayleigh quotient optimization. Enhancements are thus made by identifying a quasi-optimal filter parameter range within which blind filtering tackles these issues. Finally, filtering is applied to certain frequency ranges in order to prevent the blind filtering optimization from getting skewed by dominant deterministic healthy signal content. The outcomes prove that sparsitybased blind filters are effective in tracking rolling element bearing faults on real-world rotating machinery without any prior knowledge of characteristic frequencies.