Improving mesh stiffness calculation of cracked gears for the purpose of vibration-based fault analysis

Mohammed, Omar D.; Rantatalo, Matti; Aidanpää, Jan-Olov

doi:10.1016/j.engfailanal.2013.08.008

Cited by 145 publications

(84 citation statements)

References 26 publications

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“…Statistical features which are commonly used to provide a measurement of the vibration level are widely used in mechanical fault detection [8,17,41]. The RMS value and Kurtosis are defined as follows [41]:…”

Section: Crack Fault Features Based On Frequency Features Rms and Kumentioning

confidence: 99%

“…The key for simulation analysis is to obtain the accurate timevarying mesh stiffness (TVMS), which has a great influence on the gear pair vibration. The finite element (FE) method [1][2][3][4][5][6][7], analytical method (AM) [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] and analytical-FE approach [23][24][25] have been used to calculate the TVMS of gears. For AM and analytical-FE approach, they can offer a simple and effective way to calculate the TVMS under most conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Conventional methods, such as RMS, kurtosis, and power spectrum, have proved to be effective in fault diagnosis [8,16,17]. However, their main drawback is that they are based on the assumption of stationarity and linearity of the signal generation process [33].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fault features analysis of cracked gear considering the effects of the extended tooth contact

Pang

Feng

et al. 2015

Engineering Failure Analysis

161

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Section: Crack Fault Features Based On Frequency Features Rms and Kumentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fault features analysis of cracked gear considering the effects of the extended tooth contact

Pang

Feng

et al. 2015

Engineering Failure Analysis

161

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“…Many mechanical faults will increase the RMS of the vibration of certain components eventually. [12] investigated the RMS and Crest Factor of the gear mesh stiffness for the vibration-based fault detection and presented a new method for a propagating crack in the tooth root.…”

Section: A Time Domain Methodsmentioning

confidence: 99%

A Review on Data-driven Predictive Maintenance Approach for Hydro Turbines/Generators

Wang¹,

Wang²,

Li³

2016

Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation

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Abstract-Hydroelectricity as a renewable energy to respond the increasing population and environment crisis is widely used in the world. With the Hydro Turbines/Generators (HTG) being more and more complicated, the maintenance play a more and more important role in the production management in the hydro power plant. Many researches had concentrated on the predictive maintenance for the HTG in recent years. From the perspective of data-driven, this paper reviews and summarizes the key techniques regarding data acquisition, data processing, data analysis and data mining for the predictive maintenance of HTG. Especially, it place emphasis on the data-driven models for the diagnostics and prognostics. Finally, the paper concludes the current practices and presents a future research work.

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“…The typical variation of gear mesh stiffness K m (t) depends on the mesh position of the teeth engagement to separation, in which the mesh stiffness of one tooth pair in mesh is calculated based on (Chen & Shao, 2011;Mohammed, Rantatalo, & Aidanpää, 2013;Wang et al, 2015) contact stiffness and k f is the tooth root fillet stiffness. The subscripts 1, 2 mean the pinion and gear, respectively.…”

Section: Varying Meshing Stiffnessmentioning

confidence: 99%

Frictional effects on the dynamic responses of gear systems and the diagnostics of tooth breakages

Brethee

Ball

2016

Systems Science & Control Engineering

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To develop accurate diagnostic techniques, this study examines the dynamic responses of spur gear transmission system with including frictional effects on a tooth mesh process. An 8-degreeof-freedom model is developed to include the effects of supporting bearings, a driving motor and a loading system. Moreover, it takes into account not only the time-varying stiffness, but also the time-varying forces and moments due to the frictional effect. The latter causes additional vibration responses in the direction of the off-line-of-action (OLOA). To show the quantitative effect of the friction, vibration responses are simulated under different friction coefficients. It shows that an increase in friction coefficient value causes a nearly linear increase in the vibration features of diagnostics. However, features from torsional responses and the principal responses in the line-of-action show less changes in the vibration level, whereas the most significant increasing is in the OLOA direction. Furthermore, the spectral peaks at the rotational and sideband frequencies are influenced significantly by small breakage defects, especially when the friction effect is taken into account. In addition, the second and third harmonics of the mesh frequency are more influenced than the first harmonic component for all motions, which can be effective features for both indicating lubrication deterioration and improving conventional diagnostic features. ARTICLE HISTORY

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Improving mesh stiffness calculation of cracked gears for the purpose of vibration-based fault analysis

Cited by 145 publications

References 26 publications

Fault features analysis of cracked gear considering the effects of the extended tooth contact

Fault features analysis of cracked gear considering the effects of the extended tooth contact

A Review on Data-driven Predictive Maintenance Approach for Hydro Turbines/Generators

Frictional effects on the dynamic responses of gear systems and the diagnostics of tooth breakages

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