Gear tooth stiffness reduction measurement using modal analysis and its use in wear fault severity assessment of spur gears

Yesilyurt, Isa; Gu, Fengshou; Ball, Andrew

doi:10.1016/s0963-8695(03)00011-2

Cited by 84 publications

(52 citation statements)

References 13 publications

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“…Stiffness reduction is commonly used in dynamic gear mesh models to represent tooth surface defects [19,60,61]. Tooth surface wear can cause sliding and the normal load amplitudes to vary with the position of the contact on the tooth surfaces, in turn this can cause difficulties with the computation of load distributions for the gears [62].…”

Section: Modelling Tooth Wear In Helical Gearsmentioning

confidence: 99%

Helical gear wear monitoring: Modelling and experimental validation

Brethee

Zhen

et al. 2017

Mechanism and Machine Theory

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Gear tooth surface wear is a common failure mode. It occurs over relatively long periods of service nonetheless, it degrades operating efficiency and lead to other major failures such as excessive tooth removal and catastrophic breakage. To develop accurate wear detection and diagnosis approaches at the early phase of the wear, this paper examines the gear dynamic responses from both experimental and numerical studies with increasing extents of wear on tooth contact surfaces. An experimental test facility comprising of a back-to-back two-stage helical gearbox arrangement was used in a run-tofailure test, in which variable sinusoidal and step increment loads along with variable speeds were applied and gear wear was allowed to progress naturally. A comprehensive dynamic model was also developed to study the influence of surface wear on gear dynamic response, with the inclusion of time-varying stiffness and tooth friction based on elasto-hydrodynamic lubrication (EHL) principles.The model consists of an 18 degree of freedom (DOF) vibration system, which includes the effects of the supporting bearings, driving motor and loading system. It also couples the transverse and torsional motions resulting from time-varying friction forces, time varying mesh stiffness and the excitation of different wear severities. Vibration signatures due to tooth wear severity and frictional excitations were acquired for the parameter determination and the validation of the model with the experimental results. The experimental test and numerical model results show clearly correlated behaviour, over different gear sizes and geometries. The spectral peaks at the meshing frequency components along with their sidebands were used to examine the response patterns due to wear. The paper concludes that the mesh vibration amplitudes of the second and third harmonics as well as the sideband components increase considerably with the extent of wear and hence these can be used as effective features for fault detection and diagnosis.

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Section: Modelling Tooth Wear In Helical Gearsmentioning

confidence: 99%

Helical gear wear monitoring: Modelling and experimental validation

Brethee

Zhen

et al. 2017

Mechanism and Machine Theory

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“…Local damage in gear teeth generates a local decrease in mesh stiffness [17]; as a consequence the damage in gears produce modulation effects modifying either the magnitude or the phase of the vibration signal picked up by the transducers. These effects occur during the engagement of the faulted teeth, but are repeated once per revolution of the gear.…”

Section: Damage Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Many studies have been performed in the past to determine the influence of the aforementioned mesh compliance in gear dynamics [16,17]. This study aims to detect this torsional stiffness change on-line which implies an extra feature over such techniques [16,17]. In addition the evaluation on line of the change in stiffness allows to determine its influence over the gear operating point which can be critical in some applications due to the fact that changes in mesh stiffness associated with some failures can decrease the frequency value of some resonances, with the potential risk of interacting with another exciting frequencies in the rotary machine, like shaft unbalances, giving raise to unforeseen changes that should be evaluated either for maintenance purposes and to avoid unexpected failures.…”

Section: Introductionmentioning

confidence: 99%

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Gear dynamics monitoring using discrete wavelet transformation and multi-layer perceptron neural networks

Sanz

Perera

Huerta

2012

Applied Soft Computing

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This paper presents a multi-stage algorithm for the dynamic condition monitoring of a gear. The algorithm provides information referred to the gear status (fault or normal condition) and estimates the mesh stiffness per shaft revolution in case that any abnormality is detected. In the first stage, the analysis of coefficients generated through discrete wavelet transformation (DWT) is proposed as a fault detection and localization tool. The second stage consists in establishing the mesh stiffness reduction associated with local failures by applying a supervised learning mode and coupled with analytical models. To do this, a multi-layer perceptron neural network has been configured using as input features statistical parameters sensitive to torsional stiffness decrease and derived from wavelet transforms of the response signal. The proposed method is applied to the gear condition monitoring and results show that it can update the mesh dynamic properties of the gear on line.

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“…The common nondestructive evaluation (NDE) methods to monitor flaws in splines are debris monitoring, acoustic emission, and vibration. The methods can be applied individually [1][2][3][4][5][6] or concurrently [7,8]. While debris monitoring does not require any electronics, it is simple to interpret and has excellent sensitivity to wear-related failure; this method is insufficient to non-benign cracks as no debris is produced.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Ozevin

et al. 2017

Applied Sciences

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Abstract:The data collection of Acoustic Emission (AE) method is typically based on threshold-dependent approach, where the AE system acquires data when the output of AE sensor is above the pre-defined threshold. However, this approach fails to detect flaws in noisy environment, as the signal level of noise may overcome the signal level of AE from flaws, and saturate the AE system. Time-dependent approach is based on streaming waveforms and extracting features at every pre-defined time interval. It is hypothesized that the relevant AE signals representing active flaws are embedded into the streamed signals. In this study, a decomposition method of the streamed AE signals to separate noise signal and crack signal is demonstrated. The AE signals representing fatigue crack growth in steel are obtained from the laboratory scale testing. The streamed AE signals in a noisy operational condition are obtained from the gearbox testing at the Naval Air Systems Command (NAVAIR) facility. The signal addition and decomposition is achieved to determine the minimum detectable signal to noise ratio that is embedded into the streamed AE signals. The developed decomposition approach is demonstrated on detecting burst signals embedded into the streamed signals recorded in the spline testing of the helicopter gearbox test rig located at the NAVAIR facility.

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Gear tooth stiffness reduction measurement using modal analysis and its use in wear fault severity assessment of spur gears

Cited by 84 publications

References 13 publications

Helical gear wear monitoring: Modelling and experimental validation

Helical gear wear monitoring: Modelling and experimental validation

Gear dynamics monitoring using discrete wavelet transformation and multi-layer perceptron neural networks

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

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