Blind filters based on envelope spectrum sparsity indicators for bearing and gear vibration-based condition monitoring

Abstract

This paper investigates a novel perspective on blind filtering of vibration signals with the purpose of fault detection in rotating machinery. Instead of maximizing a property of the time-domain signal such as kurtosis to find an optimal filter, the sparsity of its envelope spectrum is maximized. The underlying assumption for this approach is that faults of rotating components such as bearings introduce second-order cyclostationary content into the signal. This cyclostationary content manifests itself as discrete peaks in the envelope spectrum (given the speed is stationary). These peaks thus increase the sparsity of the envelope spectrum as a consequence. Therefore this paper derives blind filter formulations that try to filter out a signal with the most sparse envelope spectrum. Blind filters are derived using three different sparsity measures, i.c. l 2 l 1 -norm, Hoyer Index, and spectral negentropy. Key in the iterative optimization procedure is the usage of the Rayleigh quotient to update the filter coefficients. One major advantage of this approach is that no prior information about characteristic fault frequencies of the mechanical components of interest needs to be known. Inspection of simulation and experimental results shows that the proposed approach is a simple yet effective way of tracking faults with a cyclostationary signature.

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

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Signal Identification of Gear Vibration in Engine-Gearbox Systems Based on Auto-Regression and Optimized Resonance-Based Signal Sparse Decomposition

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Crack Size Identification for Bearings Using an Adaptive Digital Twin

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