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| Research on Wear State Identification of Cylinder Liner–Piston Ring Based on Friction Vibration Signal |
| DOI：10.13949/j.cnki.nrjgc.2022.03.008 |
| Key Words:multi-fractal detrended fluctuation analysis（MF–DFA） support vector machine（SVM） ensemble empirical mode decomposition（EEMD） differential evolution（DE） friction vibration |
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| Abstract:Based on the multifractal characteristics of friction vibration signals， a method combining multi-fractal detrended fluctuation analysis（MF–DFA） and support vector machine（SVM） was proposed to identify the wear states of cylinder liner and piston ring. The simulation tests were carried out on the Bruker UMT—3 friction and wear testing machine. The vibration signals were denoised by the ensemble empirical mode decomposition（EEMD） method to obtain the friction vibration signal which could characterize the contact characteristics of the friction pairs. The multifractal spectra under different wear states were calculated by MF–DFA method. The feature vectors of different wear states were constructed from the multifractal spectrum， and the parameters of SVM were optimized by differential evolution（DE） algorithm to identify different wear states. The test results showed that the recognition accuracy of normal wear state was 100%， and there was slight confusion between the recognition results of running-in wear state and sharp wear state. Therefore， the proposed method can identify the different wear states of cylinder liner and piston ring. |
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