Abstract
As important equipment for natural gas transmission stations, electric drive centrifugal compressor units are generally in continuous high-speed operation. Long-term high-load operation is easy to induce drive motor failure, resulting in huge economic losses and casualties. Compressor group condition monitoring, maintenance and repair are all closely related to motor fault diagnosis, making compressor group fault diagnosis very important. Therefore, the study of motor abnormal fault warning technology is of great significance for the prevention of centrifugal compressor accidents. In this paper, the motor fault warning model is established based on the multi-sensor data and second-order Volterra series. The obtained prediction data is compared with the fusion data collected by the sensor to obtain a range under the normal operation of the motor, that is, the numerical set [66, 67]. Predicting the development trend of motor running based on motor monitoring data. The model is used to predict the simulation data and motor data, and the results verify the correctness of the model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Tavner, P.J., Hammond, P., Penman, J.: Contribution to the study of leakage fields at the ends of rotating electrical machines. Proc. IEE 125(125), 1339–1349 (1978)
Schoen, R.R., Habetler, T.G., Kamran, F., et al. Motor bearing damage detection using stator current monitoring. IEEE Trans. Ind. Appl. 31(6), 1274–1279 (2002)
Han, L., Hong, J., Wang, D.: Fault diagnosis of aero-engine bearings based on wavelet package analysis. J. Propuls. Technol. 30(3), 327–328 (2009)
Cheang, T.S., Zhang, L.A: New prototype of diagnosis system of inner-faults for three-phase induction motors developed by expert system. In: International Conference on Electrical Machines & Systems, IEEE (2001)
Jafari, H., Poshtan, J.: Fault isolation and diagnosis of induction motor based on multi-sensor data fusion. In: Power Electronics, Drives Systems & Technologies Conference (2015)
Wang, G.W., Zhuang, J., Yu, D.H.: Research and application of manifold learning to fault diagnosis of reciprocating compressor. In: Seventh International Conference on Fuzzy Systems & Knowledge Discovery (2010)
Smeeton, P., Bousbaine, A.: fault diagnostic testing using partial discharge measurements on high voltage rotating machines. In: Universities Power Engineering Conference (2009)
Banerjee, T.P., Das, S.: Multi-sensor data fusion using support vector machine for motor fault detection. Inf. Sci. 217(24), 96–107 (2012)
Jafari, H., Poshtan, J.: Fault isolation and diagnosis of induction motor based on multi-sensor data fusion. In: Power Electronics, Drives Systems & Technologies Conference (2015)
Frenay, B., Verleysen, M.: Classification in the presence of label noise: a survey. IEEE Trans. Neural Netw. Learn. Syst. 25(5), 845–869 (2014)
Honda, K., Shrestha, A., Chinnachodteeranun, R., et al. Landslide early warning system for rural community as an application of sensor Asia. In: World Conference on Agricultural Information & It (2008)
Ghimire, R., Zhang, C., Pattipati, K.: A rough set theory-based fault diagnosis method for an electric power steering system. In: IEEE/ASME Trans. Mechatron. 1–1 (2018)
Acknowledgements
This paper is supported by SINOPEC Gas Company (No. 35150014-15-ZC0607-0002).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, Y., Ding, N., Qiu, Z., Yang, S., Wang, Y. (2020). Research on Motor Fault Warning Technology Based on Second-Order Volterra Series. In: Wahab, M. (eds) Proceedings of the 13th International Conference on Damage Assessment of Structures. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-8331-1_48
Download citation
DOI: https://doi.org/10.1007/978-981-13-8331-1_48
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-8330-4
Online ISBN: 978-981-13-8331-1
eBook Packages: EngineeringEngineering (R0)