A Methodology for Selection of Condition Monitoring Techniques for Rotating Machinery
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Published
Aug 29, 2022
Anuj Kumar Goel
Gurmeet Singh Vallayil Narayana Achutha Naikan
https://orcid.org/0000-0003-2438-104X
Gurmeet Singh Vallayil Narayana Achutha Naikan
Abstract
Rotating machinery generally consist of a driver machine such as a motor and a driven machine or load such as a compressor or pump. Several condition monitoring (CM) techniques have been developed over the years for the predictive maintenance of rotating machinery. An appropriate selection of these techniques needs to be established for maximizing the ROI (Return on investment) of such systems. This paper proposes a methodology for the proper selection of CM techniques based on factors such as fault detectability, fault severity, cost, ease of data collection, noise, and system criticality. Effective techniques are recommended based on applicability in the industrial scenario and research done till now. A careful scoring system was adopted and weightage was given to each factor by expert opinion depending on its importance in the industrial environment. Multi-criteria decision-making (MCDM) was used to obtain comparable technique combination scores. The effectiveness of a single technique was found limited in rotating machinery, effective combinations were made and scored according to important factors. Final scores were obtained and top combinations were chosen for non-critical, sub-critical, and critical systems. A possible way of implementation is also shown for remote monitoring through literature.
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Keywords
industrial asset condition monitoring, fault diagnosis, induction motor, condition monitoring techniques, industrial downtime, remote monitoring
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Davis, P., Sullivan, E., Marlow, D., & Marney, D. (2013). A selection framework for infrastructure condition monitoring technologies in water and wastewater networks. Expert Systems with Applications, 40(6), 1947–1958. https://doi.org/10.1016/j.eswa.2012.10.004
Djeddi, M., Granjon, P., & Leprettre, B. (2007). Bearing Fault Diagnosis in Induction Machine Based on Current Analysis Using High-Resolution Technique. 2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, 23–28. https://doi.org/10.1109/DEMPED.2007.4393066
Duan, Z., Wu, T., Guo, S., Shao, T., Malekian, R., & Li, Z. (2018). Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review. International Journal of Advanced Manufacturing Technology, 96(1–4), 803–819. https://doi.org/10.1007/s00170-017-1474-8
Dutta, N., Umashankar, S., Arun Shankar, V. K., Padmanaban, S., Leonowicz, Z., & Wheeler, P. (2018). Centrifugal Pump Cavitation Detection Using Machine Learning Algorithm Technique. Proceedings - 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2018. https://doi.org/10.1109/EEEIC.2018.8494594
Elliot, S. (2015). DevOps and the Cost of Downtime: Fortune 1000 Best Practice Metrics Quantified. IDC Insight, December.
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Gangsar, P., & Tiwari, R. (2020). Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review. In Mechanical Systems and Signal Processing (Vol. 144). Academic Press. https://doi.org/10.1016/j.ymssp.2020.106908
Glowacz, A. (2018). Acoustic based fault diagnosis of three-phase induction motor. Applied Acoustics, 137, 82–89. https://doi.org/10.1016/j.apacoust.2018.03.010
Glowacz, A., & Glowacz, Z. (2017). Diagnosis of the three-phase induction motor using thermal imaging. Infrared Physics and Technology, 81, 7–16. https://doi.org/10.1016/j.infrared.2016.12.003
Goktas, T., Arkan, M., Salih Mamis, M., & Akin, B. (2017, August 3). Broken rotor bar fault monitoring based on fluxgate sensor measurement of leakage flux. 2017 IEEE International Electric Machines and Drives Conference, IEMDC 2017. https://doi.org/10.1109/IEMDC.2017.8002342
Goman, V., Oshurbekov, S., Kazakbaev, V., Prakht, V., & Dmitrievskii, V. (2019). Energy efficiency analysis of fixed-speed pump drives with various types of motors. Applied Sciences (Switzerland), 9(24). https://doi.org/10.3390/app9245295
Gugaliya, A., Singh, G., & Naikan, V. N. A. (2018). Effective combination of motor fault diagnosis techniques. Proceedings of 2018 IEEE International Conference on Power, Instrumentation, Control and Computing, PICC 2018, 1–5. https://doi.org/10.1109/PICC.2018.8384812
Gundewar, S. K., & Kane, P. v. (2021). Condition Monitoring and Fault Diagnosis of Induction Motor. In Journal of Vibration Engineering and Technologies (Vol. 9, Issue 4, pp. 643–674). Springer. https://doi.org/10.1007/s42417-020-00253-y
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Hamid A. Toliyat, G. B. K. (2004). Handbook of Electrical Motors. In Electronic Product Design.
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