Health Assessment of Hydraulic Servo System Based on Fractal Analysis and Gaussian Mixture Model

Abstract:

A hydraulic servo system is a typical feedback control system. Health assessment of a hydraulic servo system is usually difficult to realize when traditional methods based on sensor signals are utilized. An approach for health assessment of hydraulic servo systems based on multi-fractal analysis and Gaussian mixture model (GMM) is proposed in this study. A GRNN neural network is employed to establish a fault observer for the hydraulic servo system. The observer is utilized to simulate the system output under normal state. The residue is then generated by subtracting the estimated output from the actual output. The residue’s feature is extracted by fractal analysis. After the feature extraction, the overlap between the current feature vectors and the normal feature vectors is obtained by applying GMM. The confidence value (CV) can be obtained in advance; this value is employed to characterize the health degree of the current state and consequently implement the health assessment of the hydraulic servo system. Lastly, two common types of fault, namely, burst and gradual, are applied to validate the effectiveness of the proposed method.

• Periodical: Applied Mechanics and Materials (Volumes 764-765)
• Pages: 703-707
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.764-765.703
• Online since: May 2015
• Authors: Xuan Wang, Hong Mei Liu*, Chen Lu
• Keywords: Fractal Analysis, Health Assessment, Hydraulic Servo System

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