Monitoring Natural Pitting Progress on Helical Gear Mesh Using Acoustic Emission and Vibration

Eftekharnejad, Babak

doi:10.1111/j.1475-1305.2010.00769.x

Cited by 6 publications

(5 citation statements)

References 26 publications

(33 reference statements)

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“…When a crack occurs in the material, it results in a rapid release of energy, transmitting in the form of an elastic wave, namely acoustic emission (AE). The AE-based detection method has been intensively used also in non-destructive assessments of cracks in papers [23,24,25]. Additionally, Rabiei and Modarres revealed a log-linear relationship between the AE features and crack growth rate, and presented an end-to-end approach for structural health management [10].…”

Section: Introductionmentioning

confidence: 99%

“…This method is capable of continuous monitoring, which is not the case with most traditional methods. In addition, AE sensors are very sensitive and can capture signals due to micro-scale defect formations coming from the internal regions of structures rather than only those at the surface [18,23]. An unsupervised and supervised pattern recognition algorithm was employed to classify the AE signals.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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The article presents the application of the acoustic emission (AE) technique for detecting crack initiation and examining the crack growth process in steel used in engineering structures. The tests were carried out on 40CrMo steel specimens with a single edge notch in bending (SENB). In the tests crack opening displacement, force parameter, and potential drop signal were measured. The fracture mechanism under loading was classified as brittle. Accurate AE investigations of the cracking process and SEM observations of the fracture surfaces helped to determine that the cracking process is a more complex phenomenon than the commonly understood brittle fracture. The AE signals showed that the frequency range in the initial stage of crack development and in the further crack growth stages vary. Based on the analysis of parameters and frequencies of AE signals, it was found that the process of apparently brittle fracture begins and ends according to the mechanisms characteristic of ductile crack growth. The work focuses on the comparison of selected parameters of AE signals recorded in the pre-initiation phase and during the growth of brittle fracture cracking.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…Debris monitoring in an oil-wetted environment has had some success. Research indicates acoustic emission (e.g., Eftekharnejad and Mba 2011, Eftekharnejad et al 2012) and vibration signals (e.g., Yesilyurt et al 2003) have better potential to detect spline damage if routine, automated inspections are performed. Acoustic emission inspections could relieve maintainers from the scrutinizing and subjective safety inspection requirements.…”

Section: Introductionmentioning

confidence: 99%

Fatigue Crack Detection at Gearbox Spline Component using Acoustic Emission Method

et al. 2014

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The spline section of helicopter gearbox structure is susceptible to fatigue crack, and non-redundant characteristic leads to the need for early flaw detection strategies. Acoustic Emission (AE) method relies on propagating elastic waves due to release of energy from active flaws. The initiation of damage is identified using the features of AE waveforms such as energy, amplitude and frequency centroid. The characteristics of the AE features are influenced by sensor type, sensor location and gearbox operational conditions. In this study, the AE data was collected from a helicopter gearbox with a notched spline section and realistic operational conditions using two different AE sensors located at two different positions. The data collection was conducted over one year under various operational conditions. The AE features were extracted from long duration waveforms (100 milliseconds) at every pre- defined time step (every 5 seconds). The frequency domain features of frequency centroid and energy distribution in various frequency bands were compared with gearbox operational conditions such as torque, lift, gyroscopic moment, and temperature. The influences of sensor location, sensor type and operational conditions on the AE features are presented in order to decouple their influences from the AE features due to damage. The comparison between the predicted crack growth time using the AE data and the observed crack initiation shows that the AE method using frequency domain features of streamed waveforms has great potential to identify the crack initiation when the sensor type and location are preserved.

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“…In the last two decades, vibrations analysis has often been used in order to monitor gear wear. Some studies have been dedicated to finding cracks while others were more focused on surface defects, such as pitting [5][6][7]. In particular, Choy et al [8] demonstrated that natural pitting has an influence on meshing stiffness amplitude and phase and, thus, on the vibrations of a gear transmission.…”

Section: Introductionmentioning

confidence: 99%

Gear noise behavior induced by their topological quality

Jolivet

Mezghani

Mansori

et al. 2013

Surf. Topogr.: Metrol. Prop.

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The upcoming fuel economy standards will result in the rapid development of electric or hybrid vehicles. Such regulatory demands will affect transmission design, which is currently driving changes in the number, type, size and quality levels of gears. Thus, gear manufacturers need to create high quality gear flanks with special topological modifications. The most important objectives are to increase the load-carrying capacity of gears and also to reduce the gear noise behavior. The teeth surface is at the heart of gear meshing mechanics and is one of the main elements in the generation of noise. The most common gear wear mechanisms are micro-pitting, pitting and spalling, which often occur on teeth surface during the early stage of failure. This study aims to identify the scale effect of pitting defects of gear teeth surface on the acoustics response of a spur gear pair. Consequently, we have developed a two-dimensional finite-element simulation model of a one-stage gear system. The transmission system was composed of two identical spur gears with one degree of freedom. Pitting defects versus topological features at different scales were modeled on the gear tooth flanks. To quantify their impact on gear noises and vibrations, we used contact stiffness as our criteria because it is directly linked to gear noise. The prevalence of the gear quality and its topological features on power density and sound issues are computed and discussed in this paper.

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Monitoring Natural Pitting Progress on Helical Gear Mesh Using Acoustic Emission and Vibration

Cited by 6 publications

References 26 publications

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

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

Fatigue Crack Detection at Gearbox Spline Component using Acoustic Emission Method

Gear noise behavior induced by their topological quality

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