Research on Fault Identification Methods Based on MRMR and MKELM

Jingzong  Yang

doi:10.6180/jase.202503_28(3).0013

Research on Fault Identification Methods Based on MRMR and MKELM

Computer Science and Information Engineering

Jingzong YangThis email address is being protected from spambots. You need JavaScript enabled to view it.

School of Big Data, Baoshan University, BaoShan, Yunnan, 678000, P.R.China

Received: September 29, 2023
Accepted: March 4, 2024
Publication Date: May 22, 2024

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202503_28(3).0013

As an emerging detection technology, acoustic active detection has attracted considerable attention in recent years due to its advantages such as low cost, non-destructiveness, and ease of signal collection. To address the challenges of fault identification in acoustic detection of pipeline blockages, this paper proposes a fault recognition method that combines Minimum Redundancy Maximum Relevance (MRMR) and Multi-Kernel Extreme Learning Machine (MKELM). Firstly, statistical analysis and multi-resolution wavelet transform are employed to extract the acoustic pulse response signals of the blockage, thus constructing mixed-domain features. Subsequently, the MRMR algorithm is utilized for secondary feature extraction to eliminate redundant features. Finally, the optimized features are input into the MKELM classification model for identification. Experimental results demonstrate that mixed-domain features are more effective in characterizing blockage states compared to single-domain features, while the MRMR algorithm significantly reduces feature redundancy. Additionally, when comparing the recognition performance of different classification models, MKELM achieves higher accuracy than single-kernel models and traditional methods.

Keywords: Pipeline; MRMR; MKELM; Pattern recognition

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