Generalized likelihood ratio-based condition indicator maximization via Rayleigh quotient iteration

This study attempts to improve the performance of Generalized Likelihood Ratio Test-based indicators via
blind filtering the of vibration signals. The key point is the optimization of the filter coefficients to maximize
the indicator of interest. The filter coefficients are optimized through Rayleigh quotient iteration. The proposed method’s performance and applicability are demonstrated on both simulated and real vibration signals
measured on an experimental test rig. The outcome of the study shows that the Rayleigh quotient iteration
is a potent tool for maximizing such complex condition monitoring indicators. Inspections over the filtered
signals reveal that the optimal filters promote particular signal patterns linked to a bearing fault in vibration
signals. The indicator estimated over the filtered signals is able to detect the bearing fault more robustly when compared to the raw signals.