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Research on Twin Extreme Learning Fault Diagnosis Method Based on Multi-Scale Weighted Permutation Entropy.

Xuyi Yuan¹, Yugang Fan¹, Chengjiang Zhou²

¹Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.

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|September 23, 2022

Summary

This study introduces a new method for diagnosing check valve faults in high-pressure diaphragm pumps. The multi-scale weighted permutation entropy (MWPE) and twin extreme learning machine (TELM) model accurately identifies fault states, improving diagnostic reliability.

Keywords:

MWPE TELM check valve fault diagnosis

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Area of Science:

Mechanical Engineering
Signal Processing
Artificial Intelligence

Background:

Check valve fault diagnosis in high-pressure diaphragm pumps is challenging due to non-stationary and non-linear vibration signals.
Traditional methods like MPE and ELM struggle with accurate fault feature extraction and classification.
Existing methods lack reliability in identifying complex fault characteristics.

Purpose of the Study:

To develop an accurate and reliable fault diagnosis method for check valves in high-pressure diaphragm pumps.
To enhance fault feature extraction from complex vibration signals.
To improve the classification accuracy of fault diagnosis models.

Main Methods:

Utilized Multi-scale Weighted Permutation Entropy (MWPE) to extract enhanced multi-scale fault and arrangement pattern features from vibration signals.
Developed a fault diagnosis model using the Twin Extreme Learning Machine (TELM).
The TELM model was designed to find non-parallel classification hyperplanes for improved applicability.

Main Results:

The proposed MWPE method effectively enhances fault features by combining amplitude and arrangement pattern information.
The MWPE-based TELM model demonstrated superior performance in identifying check valve fault states.
Achieved a high fault diagnosis accuracy rate of 97.222%.

Conclusions:

The combined MWPE and TELM approach offers a robust solution for check valve fault diagnosis in high-pressure diaphragm pumps.
This method overcomes limitations of traditional techniques in characterizing complex vibration signal dynamics.
The study validates the effectiveness and high accuracy of the proposed diagnostic model.