Current issue
Archive
About the Journal
About
Editorial Board
Abstract & Indexing
Contact
Editorial Policies
Authorship & COI
Ethical principles
Principles of Transparent Checklist
Open access
For Authors
Instructions to Authors
Detailing Guidelines for Authors
Review process
Review sheet
How to submit
Open Access license
ORIGINAL PAPER
Investigation of parameters for fault detection of worm gear box using denoise vibration signature
1
Department of Mechanical Engineering, MET BKC Institute of Engineering, Nashik, SPPU Pune, INDIA
2
Department of Mechanical Engineering, Sandip Institute of Technology and Research Center, Nashik, SPPU Pune, INDIA
3
Department of Mechanical Engineering, Bramha Valley College of Engineering and Research Institute, Anjaneri Village, Trimbakeshwar, SPPU Pune, INDIA
4
Department of Computer Engineering, Bramha Valley College of Engineering and Research Institute, Anjaneri Village, Trimbakeshwar, SPPU Pune, INDIA
Online publication date: 2023-12-19
Publication date: 2023-12-23
International Journal of Applied Mechanics and Engineering 2023;28(4):43-53
KEYWORDS
ABSTRACT
In industrial applications, worm gearboxes are a key element. In a worm gearbox, as the material of a worm wheel is softer than that of a worm screw, the worm wheel gear is vulnerable to failure through various modes like pitting, wearing out, or tooth breakage during the sliding process. Due to this, it is essential to monitor the failure of the worm wheel gear of the worm gearbox, and it has gained importance for the diagnosis of faults in gearboxes. The present work focuses on the investigation of the effect of worm wheel tooth breakage, worm wheel bearing outer race, and varying load on vibration signature amplitude and frequency domain statistical features such as root mean square (RMS), crest factor, kurtosis, mean, peak to peak, skewness, sample variance, and standard deviation. The experimental setup is fabricated to conduct the experimental trials. An OR34 FFT analyzer with NVGate software is used to acquire the frequency domain vibration signature. Experimental results show that captured vibration signature amplitude for healthy worm wheel and bearing increased as fault occurred on the worm wheel, and bearing and frequency domain statistical features value changed with the change in fault location in the worm gearbox.
ACKNOWLEDGEMENTS
This work is supported by the METs Institute of Engineering Management Nashik and Savitribai Phule Pune University, India.
REFERENCES (14)
1.
Karpat F., Kalay O.C., Dirik A.E. and Karpat E. (2022): Fault classification of wind turbine gearbox bearings based on convolutional neural networks.– Transdisciplinary Journal of Engineering & Science, vol.13, No.2, pp.71-83, doi: 10.22545/2022/00190.
2.
Umutlu R.C., Hizarci B., Kiral Z. and Ozturk H. (2020): Classification of pitting fault levels in a worm gearbox using vibration visualization and ANN.– Sadhana Academy Proceedings in Engineering Sciences, vol.45, No.22, pp.1-13, doi.org/10.1007/s12046-019-1263.
3.
Babu T.N., Patel D., Tharnari D. and Bhatt A. (2019): Temperature behavior-based monitoring of worm gears under different working conditions.– Innovative Design, Analysis and Development Practices in Aerospace and Automotive Engineering, vol.2, No.1089, pp.257-265, DOI:10.1007/978-981-13-2718-6_24.
4.
Elasha F., Carcel C.R., Mba D., Kiat G., Nze I. and Yebra G. (2014): Pitting detection in worm gearboxes with vibration analysis.– Engineering Failure Analysis, vol.42, No.1350, pp.366-376, DOI:10.1016/j.engfailanal.2014.04.028.
5.
Waqar T. and Demetgul M. (2016): Thermal analysis MLP neural network based fault diagnosis on worm gears.– Measurement, vol.86, No.1016, pp.56-66, http://dx.doi.org/10.1016/j.me....
6.
Lu N., Zhang G., Cheng Y. and Chen D. (2016): Signal denoising method based on adaptive redundant second generation wavelet for rotating machinery fault diagnosis.– Mathematical Problems in Engineering, vol.2016, No.1155, pp.1-11, https://doi.org/10.1155/2016/2....
7.
Dhamande L.S. and Chaudhari M.B. (2018): Compound gear-bearing fault feature extraction using statistical features based on time-frequency method.– Measurement, vol.125, No.1016, pp.63-77, https://doi.org/10.1016/ j.measurement.2018.04.059.
8.
Hizarci B., Umutlu R.C., Kiral Z. and Ozturk, H. (2019): Vibration region analysis for condition monitoring of gearboxes using image processing and neural networks.– Experimental Techniques, vol.43, No.483, pp.739-755, doi.org/10.1007/s40799-019-00329-9.
9.
Elasha F., Mba D. and Carcel C.R. (2016): A comparative study of adaptive filters in detecting a naturally degraded bearing within a gearbox.– Mechanical Systems and Signal Processing, vol.3, No.1016, pp.1-8, https://doi.org/10.1016/j.csms....
10.
Miao F., Zhao R. and Wang X. (2020): A new method of denoising of vibration signal and its application.– Shock and Vibration, vol.2, No.10, pp.1-8, DOI:10.1155/2020/7587840.
11.
Miao F. and Zhao R. (2020): A new fault diagnosis method for rotating machinery based on SCA-FastICA.– Shock and Vibration, vol.2, No.10, pp.1-12. DOI:10.1155/2020/6576915.
12.
He C., Xing J., Li J., Yang Q. and Wang R. (2015): A new wavelet threshold determination method considering interscale correlation in signal denoising.– Mathematical Problems in Engineering. Vol.5, No.1155, pp. 1-9. DOI:10.1155/2015/280251.
13.
He C., Xing J., Li J., Yang, Q. and Wang R. (2015): A new wavelet thresholding function based on hyperbolic tangent function.– Mathematical Problems in Engineering. vol.1, No.1155, DOI:10.1155/2015/528656.
14.
Reeves C.W. (1998): The Vibration Monitoring Handbook Basic Concepts and Theory.– United Kingdom, Coxmoor Publisher.
| eISSN: | 2353-9003 |
| ISSN: | 1734-4492 |
Task: edition of a scientific journal, its internationalization and ensuring open access to the journal via the Internet
© 2006-2024 Journal hosting platform by Bentus
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
