- All Journals
- Al-Qadisiyah University
- Basrah University
- Kirkuk University
- Mosul University
- The Iraqi Borad for Medical Specialization
- University of Anbar
- University of Technology
- Al Mustafa University
- Al-Zahraa University for Women
- Alnoor University
- Kunooz University
- Misan University
- Ninevah University
- Tikrit University
- University of Al-Hamdaniya
- University of Kerbala
Bearings Health Monitoring Based on Frequency-Domain Vibration Signals Analysis | ||
| Engineering and Technology Journal | ||
| Article 8, Volume 41, Issue 1, 2023, Pages 86-95 PDF (1.74 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.30684/etj.2022.131581.1043 | ||
| Authors | ||
| Saja M. Jawad* 1; Alaa A. Jaber2 | ||
| 1mechanical Department | ||
| 2University of Technology-Iraq | ||
| Abstract | ||
| Rotating machine health monitoring is critical for system safety, cost savings, and increased reliability. The need for a simple and accurate fault diagnosis method has led to the development of various monitoring techniques. They incorporate vibration, motor’s current signature, and acoustic emission signals analysis in condition monitoring. So, based on using vibration signal analysis, a test rig was built for bearing fault identification. The test rig replicates and investigates various bearing problems, such as those found in the inner and outer races. An accelerometer, type ADXL335, was interfaced to a data acquisition device (DAQ USB-6215) for collecting vibration signals under various operating circumstances. In addition, a load cell was embedded with the test rig, interfaced with a digital panel meter, and used for recording the applied load on the bearings. The time-domain signal analysis technique was used after acquiring vibration signals at various bearing health states. Then, the time-domain signal was converted to the frequency domain using the fast Fourier transform, and the result was analyzed to investigate the generated fault frequencies. Finally, the obtained frequencies were compared with the theoretical values extracted from the theoretical equations, and the method proved its effectiveness in detecting the fault generated. | ||
Highlights | ||
| ||
| Keywords | ||
| Vibration Signal Analysis; Bearing fault detection; Time-domain signal analysis; Frequency-domain signal analysis; Fault frequencies | ||
| References | ||
|
[1] X. Jin, M. Zhao, T. W. Chow, and M. Pecht, Motor bearing fault diagnosis using trace ratio linear discriminant analysis, IEEE Trans. Ind. Electron. 61 (2013) 2441-2451. [2] D. S. Chandra and Y. S. Rao, Fault Diagnosis of a Double-Row Spherical Roller Bearing for Induction Motor Using Vibration Monitoring Technique, J. Fail. Anal. Prev. 19 (2019) 1144-1152. [3] A. Boudiaf, A. Moussaoui, A. Dahane, and I. Atoui, A comparative study of various methods of bearing faults diagnosis using the case Western Reserve University data, J. Fail. Anal. Prev. 16 (2016) 271-284. [4] S. Fu, K. Liu, Y. Xu, and Y. Liu, Rolling bearing diagnosing method based on time domain analysis and adaptive fuzzy-means clustering, Shock Vib. 2016 (2016). [5] J. J. Saucedo-Dorantes, M. Delgado-Prieto, J. A. Ortega-Redondo, R. A. Osornio-Rios, and R. d. J. Romero-Troncoso, Multiple-fault detection methodology based on vibration and current analysis applied to bearings in induction motors and gearboxes on the kinematic chain, Shock Vib. 2016 (2016). [6] B. Fang, J. Zhang, K. Yan, J. Hong, and M. Y. Wang, A comprehensive study on the speed-varying stiffness of ball bearing under different load conditions, Mech. Mach. Theory, 136 (2019) 1-13. [7] M. Vishwakarma, R. Purohit, V. Harshlata, and P. Rajput, Vibration analysis & condition monitoring for rotating machines: a review, Mater. Today:. Proc. 4 (2017) 2659-2664. [8] S. M. Jawad and A. A. Jaber, Rolling Bearing Fault Detection Based on Vibration Signal Analysis and Cumulative Sum Control Chart, FME Transactions, 49 (2021) 684-695. [9] J. Chebil, M. Hrairi, and N. Abushikhah, Signal analysis of vibration measurements for condition monitoring of bearings, Aust. J. Basic Appl. Sci. 5 (2011) 70-78. [10] S. Patidar and P. K. Soni, An overview on vibration analysis techniques for the diagnosis of rolling element bearing faults, International Journal of Engineering Trends and Technology (IJETT), 4 (2013) 1804-1809. [11] P. R. Manve and R. S., CONDITION MONITORING AND FAULT IDENTIFICATION OF NEW DEEP GROOVE BALL BEARING USING LABVIEW, Journal of Emerging Technologies and Innovative Research (JETIR), 4 (2017). [12] H. Musbah and M. El-Hawary, SARIMA Model Forecasting of Short-Term Electrical Load Data Augmented by Fast Fourier Transform Seasonality Detection, in IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), 2019. [13] P. Gupta and M. Pradhan, Fault detection analysis in rolling element bearing: A review, Materials Today: Proceedings, 4 (2017) 2085-2094. [14] Z. Xia, S. Xia, L. Wan, and S. Cai, Spectral regression based fault feature extraction for bearing accelerometer sensor signals, Sensors, 12 (2012) 13694-13719. [15] M. M. Tahir, S. Badshah, A. Hussain, and M. A. Khattak, Extracting accurate time domain features from vibration signals for reliable classification of bearing faults, Int. J. Adv. Appl. Sci, 5 (2017) 156-163. [16] D. S. J. Lacey, An Overview of Bearing Vibration Analysis, InA&FAG. [17] J. S. Mohammed and J. A. Abdulhady, Rolling bearing fault detection based on vibration signal analysis and cumulative sum control chart, FME Transactions, 49 (2021) 684-695. [18] C. J. Yuan, A. S. H. A. Bakar, M. N. Roslan, C. W. Cheng, M. N. S. M. Rosekhizam, J. A. Ghani, et al., Electrochemical machining (ECM) and its recent development, Jurnal Tribologi, 28 (2021) 20-31. | ||
|
Statistics Article View: 1,027 PDF Download: 600 |
||