This paper presents an empirical study of feature extraction methods for the application of low-speed slew bearing condition monitoring. The aim of the study is to find the proper features that represent the degradation condition of slew bearing rotating at very low speed (≈ 1 r/min) with naturally defect. The literature study of existing research, related to feature extraction methods or algorithms in a wide range of applications such as vibration analysis, time series analysis and bio-medical signal processing, is discussed. Some features are applied in vibration slew bearing data acquired from laboratory tests. 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.

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A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing

Acoustic emission-based condition monitoring methods: Review and application for low speed slew bearing

Early detection of angular contact bearings, one of the important subsets of rolling element bearings (REBs), is critical for applications of high accuracy and high speed performance. In this study, acoustic emission (AE) method was applied to an experimental case with defects on angular contact bearing. AE signals were collected by AE sensors in different operating conditions. Signal to noise ratio (SNR) was calculated by kurtosis to entropy ratio (KER), then acquired signals were denoised by empirical mode decomposition (EMD) method, and optimal intrinsic mode function (IMF) was selected by the proposed method. Finally, envelope spectrum was applied to the denoised signals, and frequencies of defects were obtained in different rotating speeds, loadings, and defect sizes. For the first time, a small defect with width of 0.3 mm and loading of 475 N was detected in early stage of 0.04 KHz. Moreover, a comparison between theoretical and extracted defect frequencies suggested that our method successfully detected localized defects in both inner and outer race. Our results show promise in detecting small size defects in REBs.

/pdf/experimental-investigation-of-the-diagnosis-of-angular-7ricnry82v.pdf

Experimental Investigation of the Diagnosis of Angular Contact Ball Bearings Using Acoustic Emission Method and Empirical Mode Decomposition

In this study, a novel fault diagnosis approach based on a non-linear spectrum feature is proposed for a multivariable non-linear system. The non-linear spectrum features are obtained using a non-l...

Fault diagnosis for multivariable non-linear systems based on non-linear spectrum feature:

Phase-space topography characterization of nonlinear ultrasound waveforms.

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.

/pdf/bearing-health-assessment-based-on-chaotic-characteristics-11t4rmjfy3.pdf

Bearing health assessment based on chaotic characteristics

De-noising and extraction of the weak signature are crucial to fault diagnostics in which case features are often very weak and masked by noise. The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability. In this paper, wavelet decomposition-based de-noising method is employed for fault feature extraction of hydraulic pump. The experiment result reveals that the wavelet decomposition de-noising method can achieve satisfactory results on weak signal detection of hydraulic pump.

Chuan Lu

Papers

Bearing health assessment based on chaotic characteristics

Fault diagnosis based on wavelet package for hydraulic pump