Acoustic emission monitoring of naturally developed damage in large-scale low-speed roller bearings

Abstract

This article presents an approach to identify naturally developed damage in low-speed bearings using waveform-similarity-based clustering of acoustic emissions (AEs) under fatigue loading. The approach is motivated by the notation that each recorded AE signal from a particular damage is defined by the convolution of the source signal, transfer function of the propagation path and transfer function of the utilised sensor, and may thusly be used to identify consistent AE sources, for example due to crack growth. A sequential clustering procedure is proposed, that is based on waveform cross-correlation. The supporting theoretical background of waveform similarity, rooted in an analytical formulation of waveform propagation and transmission in complex structures, is discussed. The presented methodology is evaluated through application to AE data obtained in a low-speed run-to-failure experiment utilising a densely instrumented purpose-built linear bearing segment. The implemented sensor system comprises arrays of three types of AE transducers, that is relatively low - (40–100 kHz), mid - (95–180 kHz) and high-frequency (180–580 kHz), that are situated on both the raceways and supporting substructures of either side of the bearing. Over the course of 225,000 cycles of extension and retraction, wear has been developed. A total of about ∼2,300,000 AE signals have been recorded. Analysis of the recorded data suggests the rate of degradation increases from around 70,000 cycles onwards. Highly consistent structures of clusters indicative of a localised defect in the raceway have been identified from around 170,000 cycles onwards. These clusters are characterised by hit-rates in the range of 1–2 hits per cycle and an average similarity of 93%, they comprise about half the AE activity for the periods they have been identified for. These results highlight that the proposed cross-correlation-based clustering of AE waveforms and identification of multi-channel formations in said clusters compose a suitable methodology for assessment of damage in low-speed roller bearings.

Acoustic emission monitoring of composite marine propellers in submerged conditions using embedded piezoelectric sensors

“…These waves appear in a multitude of through-thickness deformation (wave) modes and propagate in in-plane direction. In a mathematical sense, the measurement P (x R , x S , ω) can be written as the convolution of AE source signal S i (x S , ω) with wave propagation transfer function W i (x R , x S , ω) and sensor transfer function D i (x R , ω), and the addition of noise P N (x R , ω) [33,34]. When propagating in the form of guided waves, each component can be dependent on the wave mode i = [1..n] for n modes, the source location x S , sensor location x R and frequency ω.…”