Fault diagnosis of high-speed rotating machines using MATLAB
| 1 | Research Scholar, Mechanical Engineering Department, KLS’s Vishwanathrao Deshpande Institute of Technology, Haliyal, Karnataka, India 581329. |
| 2 | Mechanical Engineering Department, KLS’s Vishwanathrao Deshpande Institute of Technology, Haliyal, Karnataka, India 581329. |
CORRESPONDING AUTHOR
Mahesh B. Joshi
Research Scholar, Mechanical Engineering Department, KLS’s Vishwanathrao Deshpande Institute of Technology, Haliyal, Karnataka, India 581329.
Research Scholar, Mechanical Engineering Department, KLS’s Vishwanathrao Deshpande Institute of Technology, Haliyal, Karnataka, India 581329.
Submission date: 2022-12-10
Final revision date: 2023-03-23
Acceptance date: 2023-04-03
Online publication date: 2023-04-24
Publication date: 2023-04-24
Diagnostyka 2023;24(2):2023208
KEYWORDS
TOPICS
ABSTRACT
Industrial high-speed rotating machines entail constant and consistent monitoring to prevent downtime, affecting quantity and quality. Complex machines need advanced intelligent fault diagnosis showing minimal errors. This work offers a MATLAB-based fault diagnosis for sugar industry machines. The preliminary survey for sugar factories is conducted to gain critical vibration problems in sugar industries. The vibration behaviour of physical industrial machines is obtained, and the signals are provided to a MATLAB program to identify the fault. The information helps to suggest remedies to include in the maintenance schedule. The ease and comprehensible nature of the method reduce time and enhance the reliability of condition monitoring for industrial machines.
REFERENCES (21)
1.
Jong-Ho S, Hong-Bae J. On condition-based maintenance policy. Journal of Computational Design and Engineering 2015; 2(2): 119-127. https://doi.org/10.1016/j.jcde....
2.
Joshi MB, Nadakatti MM, Chavan SP. Implementation Of Diagnostic Technique To Solve Vibration Problems In Sugar Industry: Some Case Studies. International Review of Mechanical Engineering 2019;13(6):318-325. http://dx.doi.org/10.15866/ire....
3.
Bourdim M, Bourdim A, Kerrouz S, Benamar A. Statistical Approach of Bearing Degradation. International Review of Mechanical Engineering 2016; 10(7): 496-500. https://doi.org/10.15866/IREME....
4.
Khadersab A Shivkumar S. Vibration analysis techniques for rotating machinery and its effect on bearing fault. 2nd International Conference on Material Manufacturing and Design Engineering 2018; 20: 247-252. https://doi.org/10.1016/j.prom....
5.
Tandon N, Choudhury A. A theoretical model to predict the vibration response of rolling bearings in a rotor bearing system to distributed defects under radial load. Journal of Tribology 2000; 122: 609-915. https://doi.org/10.1115/1.5554....
6.
Dolenc B, Boskoski P, Juricic D. Distributed bearing faults diagnosis based on vibration analysis. Journal of Mechanical Systems and Signal Processing 2016; 66-67:521-532. https://doi.org/10.1016/j.ymss....
7.
Desavale RG, Mali AR. Detection of damage of rotor-bearing systems using experimental data analysis. Procedia Engineering 2016; 144: 195-201. https://doi.org/10.1016/j.proe....
8.
Shah DS, Patel VN. Study on excitation forces generated by defective races of rolling bearing. procedia technology 2016; 23: 209-216. https://doi.org/10.1016/j.prot....
9.
Tiwari M, Gupta K, Prakash O. Dynamic response of an unbalance rotor supported on ball bearings. Journal of Sound and Vibration 2000; 238(5), 757-779. https://doi.org/10.1006/jsvi.1....
10.
Kumbhar SG, Sudhagar EP, Desavale RG. Theoretical and experimental studies to predict vibration responses of defects in spherical roller bearings using dimension theory. Measurement 2020; 161(1), 107846. https://doi.org/10.1016/j.meas....
11.
Kumbhar SG, Sudhagar PE. Fault diagnostics of roller bearings using dimension theory. ASME J Nondestructive Evaluation 2020; 4(1): 011001. https://doi.org/10.1115/1.4047....
12.
Kumbhar SG, Sudhagar EP. An integrated approach of adaptive neuro-fuzzy inference system and dimension theory for diagnosis of rolling element bearing. Measurement 2020; 166(1): 108266. https://doi.org/10.1016/j.meas....
13.
Salunkhe VG, Desavale RG, Jagadeesha T. Experimental frequency-domain vibration based fault diagnosis of roller element bearings using support vector machine. ASME J. Risk Uncertainty Part B 2021;7(2):021001. https://doi.org/10.1115/1.4048....
14.
Salunkhe VG, Desavale RG. An intelligent prediction for detecting bearing vibration characteristics using a machine learning model. ASME J Nondestructive Evaluation 2021;4(3):031004. https://doi.org/10.1115/1.4049....
15.
Howard I. Vibration Signal Processing using MATLAB. Acoustic Australia 2017; 23: 1-9.
16.
Sarje SH, Lathkar GS, Basu SK. CBM policy for an online continuously monitored deteriorating system with random change mode. Journal of The Institute of Engineers Series C 2012;93(1):27-32. https://doi.org/10.1007/s40032....
17.
Gopinath K, Periyasamy S. Vibration analysis of rotating shaft using MATLAB. International Journal of Science Technology and Engineering 2016; 3(06): 248-252.
18.
Thakur NS, Kachhawaha A. Vibration analysis on the production line through MATLAB. International Journal For Technological Research In Engineering 2018; 5(11): 4331-4334.
19.
Huang H, Baddour N, Liang M. Multiple time-frequency curve extraction Matlab code and its application to automatic bearing fault diagnosis under time-varying speed conditions. MethodsX 2019; 6: 1415-1432. https://doi.org/10.1016/j.mex.....
20.
Sujata C. Vibration and acoustics measurement and signal analysis. McGraw Hill Education (India) Private Limited 2014, New Delhi.
21.
Purohit RK, Purohit K. Dynamic analysis of ball bearings with the effect of preload and number of balls. International journal of applied mechanics and engineering 2006; 11(1): 77-91.
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