Performance Analysis of Motor Vibration Based Condition Monitoring Using R-curve
Authors
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Mechanical Engineering Department, Ballari Institute of Technology and Management, Ballari - 583104, Karnataka ,IN
B. K. Pavan Kumar -
Mechanical Engineering Department, Ballari Institute of Technology and Management, Ballari - 583104, Karnataka ,INYadavalli Basavaraj
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Mechanical Engineering Department, Ballari Institute of Technology and Management, Ballari - 583104, Karnataka ,INSantosh V. Janamatti
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Al-Kitab University, Kirkuk - 36015 ,IQSameer Algburi
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Department of Chemical Engineering and Petroleum Industries, Al-Mustaqbal University, Babylon - 51001 ,IQHasan Sh. Majdi
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Civil Engineering Department, Dijlah University College, Baghdad ,IQSalah J. Mohammed
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Aircraft Research and Design Centre, HAL, Bangalore - 560037, Karnataka ,INMadeva Nagaral
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Mechanical Engineering Department, Ballari Institute of Technology and Management, Ballari - 583104, Karnataka ,INFazil Nalband
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Department of Mechanical Engineering, APS Polytechnic, Bangalore - 560082, Karnataka ,INNagaraj Namdev
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Department of Mechanical Engineering, Siddaganga Institute of Technology, Tumakuru - 572103, Karnataka ,INV. Auradi
DOI:
https://doi.org/10.18311/jmmf/2024/44562Keywords:
Condition monitoring, FFT Analyzer, Maintenance, Neural Network, VibrationAbstract
Traditional techniques of manually extracting characteristics from monitoring data need skill in signal processing and previous knowledge in failure detection, which is seldom possible on a machinery big data platform. As a result, a unique approach for automatically extracting adaptive fault characteristics from monitoring data and intelligently diagnosing fault patterns is projected to accomplish rotating equipment problem identification on a machinery big data platform. This study is focused on knowledge acquired from vibration analysis and applying towards condition monitoring techniques. Results showed 99.87% accuracy level of vibration that improves the performance of motor.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Accepted 2024-07-19
Published 2024-08-19
References
Tang B, Chen F, Yang Y. Performance degradation prediction of mechanical equipment based on optimized multi-kernel relevant vector machine and fuzzy information granulation. China: Laboratory of Hydroelectric Machinery Design and Maintenance; 2020. p. 1-12. https://doi.org/10.1016/j.measurement.2019.107116
Jayaswal P, Verma SN, Wadhwani AK. Application of ANN, fuzzy logic and wavelet transform in machine fault diagnosis using vibration signal analysis. Journal of Quality in Maintenance Engineering. 2010; 16(2). https://doi.org/10.1108/13552511011048922
Patel JP, Upadhyay SH. Comparison between Artificial Neural Network and Support Vector Method for a fault diagnostics in rolling element bearings. 12th International Conference on Vibration Problems, ICOVP 2015, Science Direct, Procedia Engineering 144; 2016. p. 390 - 397.https://doi.org/10.1016/j.proeng.2016.05.148
Gondal I, Kamruzzaman J, Md. Rashid M, Amar M. A data mining approach for machine fault diagnosis based on associated frequency patterns. Australia: Springer Science+Business Media New York; 2016. p. 638-651. https://doi.org/10.1007/s10489-016-0781-3
Callejo LH, Perez OD, Merizalde Y. Diagnosis of wind turbine faults using generator current signature analysis: A review. Journal of Quality in Maintenance Engineering. 2020; 26(3):431-458. https://doi.org/10.1108/JQME-02-2019-0020
Droder K, Hoffmeistera W, Luiga M, Tounsi T. Real-Time Monitoring of High-Speed Spindle Operations using Infrared Data Transmission. International Conference on High Performance Cutting, Germany; 2014. p. 488 – 493. https://doi.org/10.1016/j.procir.2014.03.058
Alrobaian A, Bellary SAI, Kanai RA, Jamadar M. Model-based condition monitoring for the detection of failure of a ball bearing in a centrifugal pump. Journal of Failure Analysis and Prevention, Qassim University. 2019. https://doi.org/10.1007/s11668-019-00792-x
Lokesha M, Majumder MC, Raheem KFA, Ramachandran KP. Fault detection and diagnosis ingears using wavelet enveloped power spectrum and ANN. International Journal of Research in Engineering and Technology, Caledonian College of Engineering. 2013; 02(09):146-158. https://doi.org/10.15623/ijret.2013.0209023
Chunzhi Wu, Ding C, Feng F, Jiang P, Chen T. Intelligent fault diagnosis of rotating machinery based on the one-dimensional convolutional neural network. China: Computers in Industry, Academy of Army Armored Forces Beijing; 2019 p. 53-61. https://doi.org/10.1016/j.compind.2018.12.001
Gupta P, Pradhan MK. Fault detection analysis in rolling element bearing: A review. International Conference of Materials Processing and Characterization (ICMPC 2016), Materials Today: Proceedings; 2017. p. 2085-2094. https://doi.org/10.1016/j.matpr.2017.02.054
Muniz AG, Cuadrado A, Diaz I. DCNN for condition monitoring and fault detection in rotating machines and its contribution to the understanding of machine nature. Heliyon Journal. 2020; 3395-3405. https://doi.org/10.1016/j.heliyon.2020.e03395
Pintelon L, Wakiru J. A data mining approach for lubricant-based fault diagnosis. Journal of Emerald Insights. 2020; 1355-1365.
Deng C, Wu J, Liang P. Compound fault diagnosis of gearboxes via multi-label convolutional neural network and wavelet transform. China: Computers in Industry. 2019. p. 1 to 10.
Boral S, Chaturvedi SK. A case-based reasoning system for fault detection and isolation, A case study on complex gearboxes. Journal of Quality in Maintenance Engineering. 2019; 25(2):213-235.https://doi.org/10.1108/JQME-05-2018-0039
Zhang K, Yi P, Li S, Qian W. A novel transfer learning method for robust fault diagnosis of rotating machines under variable working conditions. Journal of Measurement. 2019; 514-525. https://doi.org/10.1016/j.measurement.2019.02.073
