Open Access

ARTICLE

Condition Monitoring of an Industrial Oil Pump Using a Learning Based Technique

Amin Ranjbar¹, Amir Abolzafl Suratgar^1,*, Saeed Shiry Ghidary², Jafar Milimonfared³

1 Distributed and Intelligent Optimization Research Laboratory, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, 1591634311, Iran
2 Department of Computer Engineering, Amirkabir University of Technology, Tehran, 1591634311, Iran
3 Department of Electrical Engineering, Amirkabir University of Technology, Tehran, 1591634311, Iran

* Corresponding Author: Amir Abolzafl Suratgar. Email:

Sound & Vibration 2020, 54(4), 257-267. https://doi.org/10.32604/sv.2020.05055

Received 19 December 2018; Accepted 18 July 2020; Issue published 25 November 2020

Abstract

This paper proposes an efficient learning based approach to detect the faults of an industrial oil pump. The proposed method uses the wavelet transform and genetic algorithm (GA) ensemble for an optimal feature extraction procedure. Optimal features, which are dominated through this method, can remarkably represent the mechanical faults in the damaged machine. For the aim of condition monitoring, we considered five common types of malfunctions such as casing distortion, cavitation, looseness, misalignment, and unbalanced mass that occur during the machine operation. The proposed technique can determine optimal wavelet parameters and suitable statistical functions to exploit excellent features via an appropriate distance criterion function. Moreover, our optimization algorithm chooses the most appropriate feature submatrix to improve the final accuracy in an iterative method. As a case study, the proposed algorithms are applied to experimental data gathered from an industrial heavy-duty oil pump installed in Arak Oil Refinery Company. The experimental results are very promising.

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Keywords

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