A comparative study of the effectiveness of vibration and acoustic emission in diagnosing a defective bearing in a planetry gearbox

Elasha, Faris; Greaves, Matthew; Mba, David; Duan, Fang

doi:10.1016/j.apacoust.2016.07.026

Cited by 85 publications

(38 citation statements)

References 53 publications

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“…The gearbox transmission system is one of the important elements due to the fact that it contains mechanical elements that are commonly included in a wide variety of industrial applications; the reason for its common use is because it is needed in order to handle different rotating speeds and maintain specific torques between the electric machines and the load mechanics. Although these components are robust, efficient, reliable and have a low cost, their monitoring and continuous assessment are necessary [5]. Even so, the occurrence of unexpected faults in gearboxes can occur at any time, causing production losses, system malfunctions or unwanted downtime, with significant financial losses and human efforts [6].…”

Section: Introductionmentioning

confidence: 99%

Novel Methodology for Condition Monitoring of Gear Wear Using Supervised Learning and Infrared Thermography

et al. 2020

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In gearboxes, the occurrence of unexpected failures such as wear in the gears may occur, causing unwanted downtime with significant financial losses and human efforts. Nowadays, noninvasive sensing represents a suitable tool for carrying out the condition monitoring and fault assessment of industrial equipment in continuous operating conditions. Infrared thermography has the characteristic of being installed outside the machinery or the industrial process under assessment. Also, the amount of information that sensors can provide has become a challenge for data processing. Additionally, with the development of condition monitoring strategies based on supervised learning and artificial intelligence, the processing of signals with significant improvements during the classification of information has been facilitated. Thus, this paper proposes a novel noninvasive methodology for the diagnosis and classification of different levels of uniform wear in gears through thermal analysis with infrared imaging. The novelty of the proposed method includes the calculation of statistical time-domain features from infrared imaging, the consideration of a dimensionality reduction stage by means of Linear Discriminant Analysis, and automatic fault diagnosis performed by an artificial neural network. The proposed method is evaluated under an experimental laboratory data set, which is composed of the following conditions: healthy, and three severity degrees of uniform wear in gears, namely, 25%, 50%, and 75% of uniform wear. Finally, the obtained results are compared with classical condition monitoring approaches based on vibration analysis.

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Section: Introductionmentioning

confidence: 99%

Novel Methodology for Condition Monitoring of Gear Wear Using Supervised Learning and Infrared Thermography

et al. 2020

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“…Faris et al conducted a research on bearing defects with comparison of acoustic emission and vibration techniques. They declared that vibration technique is suitable for big defects on bearing and acoustic technique is successful for detecting both big and small defects on bearing [9]. Sun and others experimented wear properties of ball bearing steel on an experimental setup in different time intervals in three wear regimes.…”

Section: Introductionmentioning

confidence: 99%

Acoustic response of 100 Cr 6 (SAE 52100 chrome steel) material ball bearing in experimental oil starvation failure modes

Erol¹

2020

Sakarya University Journal of Science

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Materials which are used in rotating elements face with faster deterioration due to vibration, acoustic emissions and the most common rotating element in dynamic mechanical systems is the bearing element. Due to surface and micro-structure features, components of the bearings’ frictional effect vary in different characteristics such as acoustical response. Particulary, as the oil starvation occur in bearings by the time, the material of the bearing inner race, outer race, balls gain higher priority for preventing internal and external failures. In this study, some failure modes are experimented with ball bearings made of 100 Cr 6 (SAE 52100 chrome steel) and evaluated respect to material acoustic response. According to the conclusion, acoustic characteristic responses are achieved in time-domain and frequency-domain in different rotating speeds; and also found that overall RMS values of the acoustic signals are in increasing trend respect to the material deterioration response.

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“…This is particularly the case in vehicles due to extended service intervals and oil-for-life being implemented in hybrids. Acoustics emission (AE) is emerging as a promising means of on-line sensing, which has been proposed to monitor the operating status of bearings [3], cutting tools [4], head/disk interfaces [5], surface finishing processes [6], grinding wheels [7] and rotating machines [8,9]. This is largely because AE transducers can be located remotely from the interface being monitored, they require no optical window and can detect the transient elastic stress waves which result from elastic/plastic deformation and fracture accompanying friction and wear [10].…”

Section: Introductionmentioning

confidence: 99%

Using acoustic emission to characterize friction and wear in dry sliding steel contacts

Geng

Puhan

Reddyhoff

2019

Tribology International

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Acoustic emission (AE) was recorded during tribological tests on 52100 steel specimens under different loads. AE signals were transformed to the frequency domain using a Fast Fourier Transform and parameters such as power, RMS amplitude, mean frequency, and energy were analyzed and compared with friction coefficient and wear volume measurements. Results show that certain acoustic frequencies reflect friction while others reflect wear. If frequencies are chosen optimally, AE and friction signals are highly correlated (Pearson coefficients >0.8). SEM and Raman analysis reveal how plastic deformation and oxide formation affect friction, wear and AE simultaneously. AE recordings contains more information than conventional friction and wear volume measurements and are more sensitive to changes in wear mechanism. This all demonstrates AE's potential as a tool to monitor tribological behavior.waveform can correlate with the quantity of wear particles, as does the AE energy [14]. Moreover, the amplitude and length of a train of AE waves can be associated with individual slip events during the stick-slip [15]. In addition to this, the AE event location can determin mode I crack propagation in fretting fatigue [16]. The instantaneous RMS amplitude has been found to correlate with CoF [17][18][19], distinguish between different stages in CoF evolution [20] and even predict CoF based on a power law relation [18]. AE RMS can also be sensitive to applied load, velocity and mechanical properties of sliding components [20]. The integrated AE RMS can be related to frictional work and wear under different sliding speeds [18] and loads [21] and can detect the sliding speed threshold for accelerated wear [18]. The integrated AE RMS [22,23] may also have a direct correlation with the wear volume and wear rate [24].In the frequency domain, AE parameters such as energy and median frequency (MDF) can be used, with the former being correlated to CoF [25,26] and wear volume [27] and the latter being correlated to CoF [26,28] and frictional work [29]. The amplitude of individual AE frequency components can also be excited by specific wear mechanisms, which can be determined based on power spectral density (PSD) and auto-covariance techniques [30]. In addition to this, it has been found that low and high frequencies are associated with different lubricant conditions [28]. Peak frequencies can vary depending on wear mechanism, with adhesive wear emission suggested to occur around 1.1 MHz and abrasive wear between 0.25-1 MHz [31,32], while stick slip results in dominant AE frequencies around 10 kHz [20].Time-frequency analysis can also be applied to AE signals, with evolving PSD spectra indicating different stages wear [19] and wavelet analysis detecting the onset of scuffing [33,34] and other wear states [35] [36].Reviewing this literature it is apparent that the instantaneous RMS value of the acquired AE signal has been the most widely used parameter to correlate with friction and wear [17-19, 22, 23, 37, 38]. However, recently questions h...

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A comparative study of the effectiveness of vibration and acoustic emission in diagnosing a defective bearing in a planetry gearbox

Cited by 85 publications

References 53 publications

Novel Methodology for Condition Monitoring of Gear Wear Using Supervised Learning and Infrared Thermography

Novel Methodology for Condition Monitoring of Gear Wear Using Supervised Learning and Infrared Thermography

Acoustic response of 100 Cr 6 (SAE 52100 chrome steel) material ball bearing in experimental oil starvation failure modes

Using acoustic emission to characterize friction and wear in dry sliding steel contacts

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