Abstract:
Diagnostics in low speed rolling element bearings is difficult. Not only are normal frequency domain diagnostics methods not appropriate for this application, but the bearing response signals are usually immersed in background
noise which make it difficult to detect these faults. Higher order statistics (HOS) techniques have been available for
decades but have not been widely applied to machine condition monitoring with the exceptions of skewness and
kurtosis. There is however reason to believe that these HOS techniques could play an important role in acoustic
emission (AE) based condition monitoring of rolling element bearings at low speeds provided appropriate care is
taken. To explore this hypothesis, AE signals at low bearing rotational speeds of 70, 80, 90 and 100 rpm respectively
were used in this work for the monitoring of tapered roller bearings. In addition to the well-established statistical
parameters such as mean, standard deviation, skewness and kurtosis, higher moments such as hyper flatness are
considered in this study. A novel diagnostic method is proposed for fault extraction based on hyperflatness,
combined with Kullback-Leibler divergence, and an indicator formula derived with the use of Lempel-Ziv Complexity is given. The Kullback-Leibler divergence is used together with the skewness and hyperflatness to obtain
the Kullback-Leibler information Wave (KLW) with which the analysis is performed, and better results obtained as
compared to conventional frequency domain analysis.
