Acoustic Emission Signatures of Fatigue Damage in Idealized Bevel Gear Spline for Localized Sensing

Abstract

In many rotating machinery applications, such as helicopters, the splines of an externally-splined steel shaft that emerges from the gearbox engage with the reverse geometry of an internally splined driven shaft for the delivery of power. The splined section of the shaft is a critical and non-redundant element which is prone to cracking due to complex loading conditions. Thus, early detection of flaws is required to prevent catastrophic failures. The acoustic emission (AE) method is a direct way of detecting such active flaws, but its application to detect flaws in a splined shaft in a gearbox is difficult due to the interference of background noise and uncertainty about the effects of the wave propagation path on the received AE signature. Here, to model how AE may detect fault propagation in a hollow cylindrical splined shaft, the splined section is essentially unrolled into a metal plate of the same thickness as the cylinder wall. Spline ridges are cut into this plate, a through-notch is cut perpendicular to the spline to model fatigue crack initiation, and tensile cyclic loading is applied parallel to the spline to propagate the crack. In this paper, the new piezoelectric sensor array is introduced with the purpose of placing them within the gearbox to minimize the wave propagation path. The fatigue crack growth of a notched and flattened gearbox spline component is monitored using a new piezoelectric sensor array and conventional sensors in a laboratory environment with the purpose of developing source models and testing the new sensor performance. The AE data is continuously collected together with strain gauges strategically positioned on the structure. A significant amount of continuous emission due to the plastic deformation accompanied with the crack growth is observed. The frequency spectra of continuous emissions and burst emissions are compared to understand the differences of plastic deformation and sudden crack jump. The correlation of the cumulative AE events at the notch tip and the strain data is used to predict crack growth. The performance of the new sensor array is compared with the conventional AE sensors in terms of signal to noise ratio and the ability to detect fatigue cracking.

Analytical and Experimental Study of Fatigue-Crack-Growth AE Signals in Thin Sheet Metals

“…The AE due to fatigue crack growth, as well as wave scattering from fatigue cracks [ 33 , 34 , 35 , 36 , 37 , 38 ], has been studied by many researchers. The acoustic emission signatures of fatigue damages in an idealized bevel gear spline and two different AE signal signatures for plastic deformation and crack jump were identified by Zhang et al [ 39 ]. The AE signal signatures recorded by piezoelectric wafer active sensor (PWAS) transducers during a fatigue crack growth event in thin metallic plates were studied by Bhuiyan et al [ 40 , 41 , 42 ].…”

The Use of the Acoustic Emission Method to Identify Crack Growth in 40CrMo Steel

“…A more practical crack identification and detection method is still lacking. Aiming to improve recognition accuracy and generalization, in papers [29,30,31] a novel crack identification method based on acoustic emission and pattern recognition is proposed. In these methods, the sound pulses from cracks are firstly separated from the original signal by pre-processing.…”

“…AE and vibration methods are based on recording transient signals in two different frequency spectrums. While vibration method is based on features that are extracted from time and frequency domain signals recorded by low frequency accelerometers in order to assess the changes in vibrational properties as related to the damage [29,30,31], the AE method is based on detecting propagating elastic waves released from active flaws. Once transient signals are collected, signal processing methods, such as wavelet decomposition [11], empirical mode decomposition [11], and multivariate pattern recognition [20], are applied.…”

A Method to Decompose the Streamed Acoustic Emission Signals for Detecting Embedded Fatigue Crack Signals

“…The circular geometry of the spline section is built as a planar geometry in order to apply loading in conventional test equipment, while the other geometric details of the spline section (e.g., thickness, spline details) are preserved, as shown in Figure 2. A more comprehensive discussion of the laboratory testing is presented in [16]. Similar to the NAVAIR test rig, a notch is introduced to control the initiation of crack.…”

“…The AE data is collected using PCI-8 data acquisition board manufactured by Mistras Group Inc. Each AE waveform is recorded with 3 MHz sampling rate with the duration of 1 ms. The threshold is varied between 50 dB and 70 dB to control the hit rate [16]. In addition, strain data is collected throughout the fatigue test to capture the strain level change due to the crack growth.…”