Gear tooth root fatigue test monitoring with continuous acoustic emission: Advanced signal processing techniques for detection of incipient failure

Abstract

The phenomenon of fatigue in gears at the tooth root can be a cause of catastrophic failure if not detected in time.Where traditional low-frequency vibration may help in detecting a well-developed crack or a completely failed tooth, a system for early detection of the nucleation and initial propagation of a fatigue crack can be of great use in condition monitoring. Acoustic emission is a potentially suitable technique, as it is sensitive to the higher frequencies generated by crack propagation and is not affected by low-frequency noise. In this article, a static gear pair is tested where a crack was initiated at a tooth root. Continuous acoustic emission was periodically recorded throughout the test. Data were processed in multiple ways to support the early detection of crack initiation. Initially, traditional feature-based acoustic emission was employed. This showed qualitative results indicating fracture initiation around 8000 cycles. A rolling crosscorrelation was then employed to compare two given system states, showing a sensitivity to large changes towards the final phases of crack propagation. A banded fast Fourier transform approach showed that the 110-to 120-kHz band was sensitive to the observed crack initiation at 8000 cycles, and to the later larger propagation events at 22,000 cycles. Two advanced data processing techniques were then used to further support these observations. First, a technique based on Chebyshev polynomial decomposition was used to reduce each wavestream data to a vector of 25 descriptors; these were used to track the system deviation from a baseline state and confirmed the previously observed deviations with a higher sensitivity. Further confirmation came from the analysis of wavestream entropy content, providing support from multiple data analysis techniques on the feasibility of system state tracking using continuous acoustic emission.

Optimisation of acoustic emission wavestreaming for structural health monitoring

“…Thus, the number of occurrences of time steps with peak frequency and frequency centroid values in 50 kHz frequency bands were summed for each wavestream and synthetic wavestream; this allowed the relative amount of activity in these bands to be tracked throughout the entirety of the test. Tracking the activity of different frequency bands is a popular method of attempting to distinguish between background noise, which is not expected to evolve with time, and AE from relevant sources relating to crack growth, which are expected to evolve with time [48]. The cumulative activity of all 50 kHz peak frequency and frequency centroid bands for the 1.5 second long synthetic wavestreams can be seen in Figure 12.…”

“…Tracking the activity of different frequency bands is a popular method of attempting to distinguish between background noise, which is not expected to evolve with time, and AE from relevant sources relating to crack growth, which are expected to evolve with time. 48 The cumulative activity of all 50 kHz peak frequency and frequency centroid bands for the 1.5-s-long synthetic wavestreams can be seen in Figure 12.…”

A novel acoustic emission sensor design and modeling method for monitoring the status of high-speed train bearings

“…37 Accordingly, the contact surface between the piezoelectric ceramics and the matching layer is analyzed. Assuming that the vibration form of the center of the circle at the bottom of the piezoelectric ceramics is a sine wave f t ð Þ = Asin(vt + u), the vibration velocity at the center can be calculated using Equation (5).…”

A new acoustic emission damage localization method using synchrosqueezed wavelet transforms picker and time-order method

“…[23][24][25][26] Compared with above non-destructive testing approaches, the AE technique has the advantages of being sensitive to materials damage and suitable for continuous health monitoring in real time. [27][28][29] Therefore, it has been applied widely in civil engineering, 30,31 mechanical engineering, 13,32 mine engineering, 33 1 and other fields. 28,34 Besides, since the AE signal comes directly from the damage location of specimen, it can be used to locate the damage location.…”