Review of Rotating Machinery Fault Diagnosis with Vibration Analysis
Creators
- 1. Department of Mechanical Engineering, Walchand College of Engineering, Sangli, Maharashtra, India
Description
A fully automatic system that can identify internal defects and forecast their remaining usable life is needed for smart factories worldwide. Utilizing the “predictive maintenance” method is one way to do this. It allows for intervention before failure occurs and considerably raises the efficiency of engineering components. Condition monitoring of rotating machinery can be done by vibration analyses utilizing various characteristic frequencies. Common defects like shaft misalignment, unbalanced, bend shafts, bearing defects and gear defects in rotating machinery must be identified before failure occurs. With the aid of frequency analysis, these errors can be anticipated. This paper covers a brief review of different fault diagnosis techniques, vibration analysis for fault detection and diagnosis, signal processing techniques, sensor position, dominant frequencies and the vibrational plane of different faults in rotating machinery.
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Review of rotating machinery.pdf
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Additional details
References
- Nayak, C., Pathak, V. K., Kumar, S., & Athnekar, P. design and development of machine fault simulator (MFS) for fault diagnosis. International Journal of Recent advances in Mechanical Engineering (IJMECH) Vol, 4, 77-84.
- Vernekar, K., Kumar, H., & Gangadharan, K. V. (2014). Gear fault detection using vibration analysis and continuous wavelet transform. Procedia Materials Science, 5, 1846-1852.
- Azeem, N., Yuan, X., Raza, H., & Urooj, I. (2019). Experimental condition monitoring for the detection of misaligned and cracked shafts by order analysis. Advances in Mechanical Engineering, 11(5), 1687814019851307.
- Shah, M., Kitkaru, S., & Rajanarasimha, S. (2020). Detection and Analysis of Faults in Gears using Frequency Domain and Artificial Neural Network. International Research Journal of Engineering and Technology, 7(10).
- Mogal, S. P., & Lalwani, D. I. (2017). Fault diagnosis of bent shaft in rotor bearing system. Journal of Mechanical Science and Technology, 31, 1-4.
- Thirumalai, M., Kumar, P. A., Jayagopi, K., Prakash, V., Anandbabu, C., Kalyanasundaram, P., & Vaidyanathan, G. (2009, December). Vibration Diagnostics as NDT Tool for Condition Monitoring in Power Plants. In Proceedings of the National Seminar & Exhibition on Non-Destructive Evaluation (NDE 2009) (pp. 50-55).
- Vishwakarma, M., Purohit, R., Harshlata, V., & Rajput, P. (2017). Vibration analysis & condition monitoring for rotating machines: a review. Materials Today: Proceedings, 4(2), 2659-2664.
- Kumar, S., Lokesha, M., Kumar, K., & Srinivas, K. R. (2018, June). Vibration based fault diagnosis techniques for rotating mechanical components. In IOP Conference Series: Materials Science and Engineering (Vol. 376, No. 1, p. 012109). IOP Publishing.
- Chacon, J. L. F., Andicoberry, E. A., Kappatos, V., Asfis, G., Gan, T. H., & Balachandran, W. (2014). Shaft angular misalignment detection using acoustic emission. Applied acoustics, 85, 12-22.
- Collacott, R. A., & Collacott, R. A. (1977). Monitoring systems in operation. Mechanical Fault Diagnosis and condition monitoring, 367-403.