Study on Fault Diagnosis of Rotating Machinery Based on Lyapunov Dimension

Bing Cheng Wang; Zhao Hui Ren; Bang Chun Wen

doi:10.4028/www.scientific.net/AMR.199-200.905

Paper Titles

Investigation of Self-adaptive Vibration Suppression for a Flexible Plate with Imbedded Fluid
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Decoupling Free Vibration System of Bridge Section Model
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Application of Acoustic Emission on Fault Diagnosis of Rolling Element Bearing
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Gear Local Fault Diagnosis with Empirical Mode Decomposition and Hilbert Huang Transformation
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Study on Fault Diagnosis of Rotating Machinery Based on Lyapunov Dimension
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A Method of Signal-Noise Separation from Measured Vibration Response Signals
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Estimation of Random Sonic Fatigue Life Based on Peak Probability Density of Von Mises Stress
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Amplitude Incremental Complementary Energy Principle for Nonlinear Vibration of Elastic System
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Multichannel Vibration Fault Diagnosis for Rolling Bearings Based on QPCA and SVM
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Study on Fault Diagnosis of Rotating Machinery Based on Lyapunov Dimension

Abstract:

Based on the study and analysis that Lyapunov exponent can be used to characterize the motion state of system, in connection with the nonlinear dynamic characteristics shown from the performance of fault rotating mechanical system, the authors proposed the analysis method of Lyapunov dimension on the signal feature of mechanical fault. Using theory of phase space reconstruction, simulating fault signal of rotating machine was reconstructed. In order to let the phase space reconstruction adequately reflect the movement characteristics of the system, the time delay and embedding dimension are discussed emphatically. Based on what mentioned above, the authors calculated the Lyapunov exponent and the Lyapunov dimension. From the analysis and calculation on simulation of different fault signals, it showed that under different rotating machinery fault conditions, its Lyapunov dimension were significantly different, which verified that this nonlinear feature quantities were effective parameters for fault information and they were excellent parameters in terms of extraction and recognition of fault features.