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SVM Algorithm for Vibration Fault Diagnosis in Centrifugal Pump

Nabanita Dutta1, Palanisamy Kaliannan1,*, Paramasivam Shanmugam2

1 School of Electrical Engineering, Vellore Institute of Technology, Vellore, TamilNadu, India
2 ESAB India Limited, Chennai, India

* Corresponding Author: Palanisamy Kaliannan. Email: email

Intelligent Automation & Soft Computing 2023, 35(3), 2997-3020. https://doi.org/10.32604/iasc.2023.028704

Abstract

Vibration failure in the pumping system is a significant issue for industries that rely on the pump as a critical device which requires regular maintenance. To save energy and money, a new automated system must be developed that can detect anomalies at an early stage. This paper presents a case study of a machine learning (ML)-based computational technique for automatic fault detection in a cascade pumping system based on variable frequency drive (VFD). Since the intensity of the vibrational effect depends on which axis has the most significant effect, a three-axis accelerometer is used to measure it in the pumping system. The emphasis is on determining the vibration effect on different axes. For experiment, various ML algorithms are investigated on collected vibratory data through Matlab software in x, y, z axes and performances of the algorithms are compared based on accuracy rate, prediction speed and training time. Based on the proposed research results, the multiclass support vector machine (MSVM) is found to be the best suitable algorithm compared to other algorithms. It has been demonstrated that ML algorithms can detect faults automatically rather than conventional methods. MSVM is used for the proposed work because it is less complex and produces better results with a limited data set.

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Cite This Article

N. Dutta, P. Kaliannan and P. Shanmugam, "Svm algorithm for vibration fault diagnosis in centrifugal pump," Intelligent Automation & Soft Computing, vol. 35, no.3, pp. 2997–3020, 2023.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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