Bearing health assessment based on chaotic characteristics
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In this paper, the authors proposed to utilize the chaotic characteristics of vibration signal for health assessment of a bearing by using self-organizing map (SOM) and Grassberger-Procaccia algorithm.Abstract:
Vibration signals extracted from rotating parts of machinery carry a lot of useful information about the condition of operating machine. Due to the strong non-linear, complex and non-stationary characteristics of vibration signals from working bearings, an accurate and reliable health assessment method for bearing is necessary. This paper proposes to utilize the se- lected chaotic characteristics of vibration signal for health assessment of a bearing by using self-organizing map (SOM). Both Grassberger-Procaccia algorithm and Takens' theory are employed to calculate the characteristic vector which includes three chaotic characteristics, such as correlation dimension, largest Lyapunov exponent and Kolmogorov entropy. After that, SOM is used to map the three corresponding characteristics into a confidence value (CV) which represents the health state of the bearing. Finally, a case study based on vibration datasets of a group of testing bearings was conducted to demonstrate that the proposed method can reliably assess the health state of bearing.read more
Citations
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Journal ArticleDOI
A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing
TL;DR: In this paper, an empirical study of feature extraction methods for the application of low-speed bearing condition monitoring is presented, where the selected features such as impulse factor, margin factor, approximate entropy and largest Lyapunov exponent (LLE) show obvious changes in bearing condition from normal condition to final failure.
Journal ArticleDOI
Acoustic emission-based condition monitoring methods: Review and application for low speed slew bearing
Wahyu Caesarendra,Wahyu Caesarendra,Buyung Kosasih,Anh Kiet Tieu,Hongtao Zhu,Craig A.S. Moodie,Qiang Zhu +6 more
TL;DR: In this paper, an acoustic emission-based method for the condition monitoring of low speed reversible slew bearings is presented, and several acoustic emission (AE) hit parameters as the monitoring parameters for the detection of impending failure of slew bearings are reviewed first.
Journal ArticleDOI
Experimental Investigation of the Diagnosis of Angular Contact Ball Bearings Using Acoustic Emission Method and Empirical Mode Decomposition
TL;DR: In this paper, the acoustic emission (AE) method was applied to an experimental case with defects on angular contact bearing and the results show promise in detecting small size defects in REBs.
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Fault diagnosis for multivariable non-linear systems based on non-linear spectrum feature:
TL;DR: A multi-fault classifier based on the fusion of a support vector machine (SVM) is designed according to different frequency domain scales, and a fusion method by using sub-classifier classification reliability is proposed.
Journal ArticleDOI
Phase-space topography characterization of nonlinear ultrasound waveforms.
Ehsan Dehghan-Niri,Helem Al-Beer +1 more
TL;DR: To analyze the acoustic field nonlinearities due to defects with closed interfaces, the use of a common technique in nonlinear physics, based on a phase‐space topography construction of ultrasound waveforms is proposed.
References
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TL;DR: In this article, the authors present the first algorithms that allow the estimation of non-negative Lyapunov exponents from an experimental time series, which provide a qualitative and quantitative characterization of dynamical behavior.
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Nonlinear time series analysis
Holger Kantz,Thomas Schreiber +1 more
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