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Vibration State Identification of Hydraulic Units Based on Improved Artificial Rabbits Optimization Algorithm.

Qingjiao Cao^1,2, Liying Wang^1,2, Weiguo Zhao^1,2

¹School of Water Conservancy and Hydropower, Hebei University of Engineering, Handan 056038, China.

Biomimetics (Basel, Switzerland)

|June 27, 2023

Summary

An improved artificial rabbits optimization algorithm (IARO) enhances support vector machine (SVM) accuracy for identifying hydraulic unit vibration states. This novel IARO-SVM model achieves 97.78% accuracy, outperforming existing methods.

Keywords:

artificial rabbits optimization signal identification support vector machine variational mode decomposition vibration state of hydraulic units

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Area of Science:

Mechanical Engineering
Signal Processing
Artificial Intelligence

Background:

Hydraulic units are critical components in many industrial systems.
Accurate identification of vibration states is essential for predictive maintenance and operational efficiency.
Existing methods for vibration analysis often face challenges in accuracy and stability.

Purpose of the Study:

To develop a highly accurate and stable model for identifying the vibration states of hydraulic units.
To optimize the Support Vector Machine (SVM) classifier using an advanced metaheuristic algorithm.
To compare the performance of the proposed model against other established optimization algorithms.

Main Methods:

Utilized Variational Mode Decomposition (VMD) for effective decomposition of complex vibration signals.
Extracted multi-dimensional time-domain feature vectors from the decomposed signals.
Developed an Improved Artificial Rabbits Optimization (IARO) algorithm with adaptive weight adjustment to optimize SVM parameters.
Implemented and compared the IARO-SVM model with ARO-SVM, ASO-SVM, PSO-SVM, and WOA-SVM models.

Main Results:

The proposed IARO-SVM model achieved an average identification accuracy of 97.78%.
This represents a significant improvement of 3.34% over the nearest competitor, the ARO-SVM model.
The IARO-SVM model demonstrated superior identification accuracy and enhanced stability compared to other evaluated models.

Conclusions:

The IARO-SVM model offers a robust and accurate solution for the vibration state identification of hydraulic units.
The adaptive weight adjustment strategy in IARO effectively optimizes SVM parameters for improved performance.
This research provides a valuable theoretical foundation for advancing vibration diagnostics in hydraulic systems.