scholarly journals Nondestructive Damage Testing of Beam Structure Based on Vibration Response Signal Analysis

Materials ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3301
Author(s):  
Xiaohao Li ◽  
Deyu Shi ◽  
Zihang Yu
Keyword(s):  
Nondestructive Testing ◽  
Signal Analysis ◽  
Unscented Kalman Filter ◽  
High Sensitivity ◽  
Vibration Signal ◽  
Structural Defects ◽  
Response Analysis ◽  
Identification Algorithm ◽  
Testing Technology ◽  
Vibration Signal Analysis

Nondestructive damage-testing technology based on vibration signal analysis makes full use of the response characteristics of wave and energy. With the advantages of wide bandwidths of response frequency and high sensitivity, the nondestructive testing technology based on vibration signal analysis has a superiority in the application for the detection and characterization of structural defects, and has become one of the important methods for the nondestructive testing of structural material defects and damage. This paper presents a novel method of detection localization and quantitative analysis for local damage in beam structures, based on the response analysis of vibration signals. A damage-detection and -identification algorithm based on a unscented Kalman filter (UKF) was designed, which greatly reduces the computational workload in the process of damage identification over that in conventional methods. The method presented in this paper has significances to widen the application scope of the nondestructive testing method, and increase the recognition efficiency and effectiveness of this kind of method in engineering.

Wavelet Selection for Bearing Defect Diagnosis

2009 ◽  
Vol 413-414 ◽  
pp. 575-582
Author(s):  
Ru Qiang Yan ◽  
Robert X. Gao
Keyword(s):  
Shannon Entropy ◽  
Signal Analysis ◽  
Ball Bearing ◽  
Quantitative Measure ◽  
Vibration Signal ◽  
Structural Defects ◽  
Bearing Defect ◽  
Selection For ◽  
Wavelet Selection ◽  
Vibration Signal Analysis

This paper introduces a quantitative measure based on the energy-to-Shannon entropy ratio for base wavelet selection in vibration signal analysis. The Gaussian-modulated sinusoidal signal and a realistic vibration signal measured from a ball bearing have been used to evaluate the effectiveness of the measure. Experimental results demonstrate that the wavelet selected using the developed measure is effective in diagnosing structural defects in the bearing and the method developed provides systematic guidance in wavelet selection.

scholarly journals Research on transformer condition evaluation technology based on vibration signal analysis

2021 ◽  
Vol 645 ◽  
pp. 012036
Author(s):  
Ma Hao ◽  
Yao Chuang ◽  
Duan Minghui ◽  
Wei Jufang ◽  
Zhang Xin ◽  
...  
Keyword(s):  
Signal Analysis ◽  
Vibration Signal ◽  
Condition Evaluation ◽  
Vibration Signal Analysis

Local Geometric Projection-Based Noise Reduction for Vibration Signal Analysis in Rolling Bearings

2008 ◽  
Author(s):  
Ruqiang Yan ◽  
Robert X. Gao ◽  
Kang B. Lee ◽  
Steven E. Fick
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Noise Reduction ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Multifractal Spectrum ◽  
Vibration Signal ◽  
Rolling Bearings ◽  
One Dimensional ◽  
Vibration Signal Analysis

This paper presents a noise reduction technique for vibration signal analysis in rolling bearings, based on local geometric projection (LGP). LGP is a non-linear filtering technique that reconstructs one dimensional time series in a high-dimensional phase space using time-delayed coordinates, based on the Takens embedding theorem. From the neighborhood of each point in the phase space, where a neighbor is defined as a local subspace of the whole phase space, the best subspace to which the point will be orthogonally projected is identified. Since the signal subspace is formed by the most significant eigen-directions of the neighborhood, while the less significant ones define the noise subspace, the noise can be reduced by converting the points onto the subspace spanned by those significant eigen-directions back to a new, one-dimensional time series. Improvement on signal-to-noise ratio enabled by LGP is first evaluated using a chaotic system and an analytically formulated synthetic signal. Then analysis of bearing vibration signals is carried out as a case study. The LGP-based technique is shown to be effective in reducing noise and enhancing extraction of weak, defect-related features, as manifested by the multifractal spectrum from the signal.

scholarly journals Vibration analysis based on the spectrum kurtosis for adjustment and monitoring of ball bearing radial clearance

2018 ◽  
Vol 211 ◽  
pp. 06006 ◽  
Author(s):  
Anthimos Georgiadis ◽  
Xiaoyun Gong ◽  
Nicolas Meier
Keyword(s):  
Signal Analysis ◽  
Vibration Signal ◽  
Radial Clearance ◽  
Spectral Kurtosis ◽  
Vibration Data ◽  
Bearing Clearance ◽  
Bearing Condition Monitoring ◽  
Bearing Vibration ◽  
Vibration Signal Analysis

Vibration signal analysis is a common tool to detect bearing condition. Effective methods of vibration signal analysis should extract useful information for bearing condition monitoring and fault diagnosis. Spectral kurtosis (SK) represents one valuable tool for these purposes. The aim of this paper is to study the relationship between bearing clearance and bearing vibration frequencies based on SK method. It also reveals the effect of the bearing clearance on the bearing vibration characteristic frequencies This enables adjustment of bearing clearance in situ, which could significantly affect the performance of the bearings. Furthermore, the application of the proposed method using SK on the measured data offers useful information for predicting bearing clearance change. Bearing vibration data recorded at various clearance settings on a floating and a fixed bearing mounted on a shaft are the basis of this study

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