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Vibration Signature Analysis of Two Stage Gearbox using Kurtosis, Skewness and Crest Factor


Affiliations
1 Mechanical Engineering Department, Marathwada Mitra Mandal’s College of Engineering, Pune, Maharashtra, India
2 Production Engineering and Industrial Management. Department, College of Engineering, Pune, Maharashtra, India
     

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In recent years, innovations and methodologies have been developed in mechanical equipment, mainly in rotating equipment, to increase the reliability of fault finding. World wide vibration indexes such as RMS, Kurtosis, etc. are widely accepted in industry and are also suggested by international standards. Even so, these parameters do not allow reliable diagnosis of the condition of the machinery. Their apparent simplicity of interpretation makes them more attractive. This work presents a discussion based on these traditional parameters about the diagnostic possibilities. The database used includes vibration signals of gears, taking into account different conditions of speeds and load. The results obtained show that these global parameters of vibration are limited in the exact diagnosis of fault, especially in the condition of initial faults. This method tries to acquire a baseline parameter that improves characterization of fault condition. The outcomes of velocity magnitude are compared with RMS, skewness and Kurtosis.

Keywords

RMS, Kurtosis, Crest Factor, Condition Monitoring.
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  • Obuchowski, Jakub; Zimroz, Radoslaw; Wylomanska, Agnieszka; Blind equalization using combined skewness-kurtosis criterion for gearbox vibration enhancement, Measurement, 2016, Doi: http://dx.doi.org/10.1016/j.measurement.2016.03.034.

  • Praveen kumar, T; Saimurugan, M; Krishnakumar, P; Ramachandran, KI: Fault diagnosis of automobile gearbox based on machine learning techniques, '12th Global congress on manufacturing and management, GCMM 2014, 'Procedia Engineering', vol. 97, 2014, 2092 – 2098.

  • Wang, Tianyang; Chu, Fulei; Han, Qinkai; Kong, Yun: Compound faults detection in gearbox via meshing resonance and spectral kurtosis methods, 'Journal of Sound and Vibration', 2017, http://dx.doi.org/10.1016/j.jsv.2016.12.041.

  • Klinchaeam, Songpon; Ajavakom, Nopdanai; Yongchareon, Withaya: Fault detection of a spur gear using vibration signal with multivariable statistical parameters, 'Songklanakarin J. Sci. Technol'. vol. 36, no. 5, 2014 563-568, Sep. - Oct. 2014.

  • Shukla, Shrikant; Karma, Vijay Kumar: Fault Detection of Two Stage Spur Gearbox using Time Domain Technique: Effect of Tooth Breakage and Improper Chamfering, 'IJISET -International Journal of Innovative Science, Engineering & Technology', vol. 1, no. 4, 2014, 184-189.

  • Zhi Qiang Chen, Chuan Li and Rene-Vinicio Sanchez (2015), Gearbox Fault Identification and Classification with Convolutional Neural Networks, 'Hindawi Publishing Corporation, Shock and Vibration', Article ID 390134.

  • David G, Lewicki; Kelsen E LaBerge, Ryan T: Ehinger and Jason Fetty (2011), 'Planetary Gearbox Fault Detection Using Vibration Separation Techniques', NASA/TM—2011-217127


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  • Vibration Signature Analysis of Two Stage Gearbox using Kurtosis, Skewness and Crest Factor

Abstract Views: 206  |  PDF Views: 0

Authors

A. B. Gholap
Mechanical Engineering Department, Marathwada Mitra Mandal’s College of Engineering, Pune, Maharashtra, India
M. D. Jaybhaye
Production Engineering and Industrial Management. Department, College of Engineering, Pune, Maharashtra, India

Abstract


In recent years, innovations and methodologies have been developed in mechanical equipment, mainly in rotating equipment, to increase the reliability of fault finding. World wide vibration indexes such as RMS, Kurtosis, etc. are widely accepted in industry and are also suggested by international standards. Even so, these parameters do not allow reliable diagnosis of the condition of the machinery. Their apparent simplicity of interpretation makes them more attractive. This work presents a discussion based on these traditional parameters about the diagnostic possibilities. The database used includes vibration signals of gears, taking into account different conditions of speeds and load. The results obtained show that these global parameters of vibration are limited in the exact diagnosis of fault, especially in the condition of initial faults. This method tries to acquire a baseline parameter that improves characterization of fault condition. The outcomes of velocity magnitude are compared with RMS, skewness and Kurtosis.

Keywords


RMS, Kurtosis, Crest Factor, Condition Monitoring.

References