Rolling Bearing Vibration Signal Analysis Based on Dual-Entropy, Holder Coefficient and Gray Relation Theory

2017 ◽  
Author(s):  
Yulong Ying ◽  
Jingchao Li ◽  
Zhimin Chen ◽  
Jing Li
Keyword(s):  
Signal Analysis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Relation Theory ◽  
Bearing Vibration ◽  
Vibration Signal Analysis

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

Novel predictive features using a wrapper model for rolling bearing fault diagnosis based on vibration signal analysis

2020 ◽  
Vol 106 (7-8) ◽  
pp. 3409-3435 ◽  
Author(s):  
Issam Attoui ◽  
Brahim Oudjani ◽  
Nadir Boutasseta ◽  
Nadir Fergani ◽  
Mohammed-Salah Bouakkaz ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Vibration Signal Analysis

Vibration Signal Analysis of Bearing Based on EMD and Resonance Demodulation

2014 ◽  
Vol 556-562 ◽  
pp. 1286-1289 ◽  
Author(s):  
Jie Shi ◽  
Xing Wu ◽  
Nan Pan ◽  
Sen Wang ◽  
Jun Zhou
Keyword(s):  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Experimental Station ◽  
Fault Type ◽  
Mode Function ◽  
Vibration Signal Analysis

In order to monitor the operation state and implement fault diagnosis of rolling bearing in rotating machinery, this paper presents a method of fault diagnosis of rolling bearing, which is based on EMD and resonance demodulation. Using EMD to decompose the signal, which comes from QPZZ-II experimental station, the components of intrinsic mode function (IMF) will be obtained. Then, calculating the correlation coefficient of each IMF component, the highest correlation coefficient of IMF component will be analyzed by resonance demodulation. Finally, the experimental results show that the method can accurately identify and diagnose the running state and bearing fault type.

Vibration signal demodulation and bearing fault detection: A clustering-based segmentation method

2013 ◽  
Vol 228 (11) ◽  
pp. 1888-1899 ◽  
Author(s):  
Shumin Hou ◽  
Ming Liang ◽  
Yi Zhang ◽  
Chuan Li
Keyword(s):  
Background Noise ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Center Frequency ◽  
Segmentation Method ◽  
Vibration Data ◽  
Signal Demodulation ◽  
Vibration Signal Analysis ◽  
Demodulation Method

The resonance demodulation technique has been widely employed in vibration signal analysis. In order to construct a proper bandpass filter, the prior knowledge, i.e. the resonance frequency band of the mechanical system is required in the traditional demodulation method. However, as the collected vibration signal is often tainted by the background noise and interferences often with unknown frequency contents, it is difficult to identify the center frequency and the bandwidth of the filter. This paper introduces a clustering-based segmentation method to determine these parameters automatically. Envelope analysis is then applied to demodulating the vibration data. According to the simulated cases, the proposed approach is robust to Gaussian noise and interferences. Its effectiveness is further validated by applying it to detect rolling bearing faults based on experimental data.

A Bearing Fault Diagnosis Using Wavelet Envelope Spectrum Based on Full Vector Spectrum Technology

2012 ◽  
Vol 190-191 ◽  
pp. 873-879 ◽  
Author(s):  
Xiao Yun Gong ◽  
Jie Han ◽  
Hong Chen ◽  
Wen Ping Lei
Keyword(s):  
Signal Analysis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Analysis Method ◽  
Bearing Fault Diagnosis ◽  
Bearing Vibration ◽  
Vibration Signal Analysis ◽  
Envelope Spectrum ◽  
Signal Analysis Method ◽  
Full Vector

Wavelet envelope demodulation method can distinguish the fault information from complex bearing vibration signal. However, traditional signal analysis method, which is solely based on a single source data, is imperfect. In this paper, an approach to wavelet packet and envelope analysis based on full vector spectrum technology was proposed. Firstly, two different data from the same source were respectively decomposed and recomposed by wavelet packet transform. Then, in order to improve the accuracy of detecting fault, the recomposed signals were merged by using the full vector spectrum method. Compared to the traditional signal analysis method, the advantage of the new method is presented by showing their application to bearings. Finally, results from the bearing vibration signal analysis show that the new approach is more effective because of its inheritance and all-sided feature.

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