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.

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

Research on the Gear Box Fault Diagnosis of Self-Propelled Artillery Based on Vibration Signal Analysis

2014 ◽  
Vol 940 ◽  
pp. 136-139
Author(s):  
Ren Bin Zhou ◽  
Yong Feng Zhang ◽  
Jie Min Yang ◽  
Feng Ling
Keyword(s):  
Fault Diagnosis ◽  
Data Acquisition ◽  
Real Time ◽  
Signal Analysis ◽  
Great Influence ◽  
Vibration Signal ◽  
Vibration Data ◽  
Gearbox Vibration ◽  
Gear Box ◽  
Vibration Signal Analysis

As a universal component connection and power transmission gear box, is widely used in the modern industrial equipment, but also an easy failure parts, has a great influence on the running state of the working performance of the whole machine. This paper first analyzes the gear box fault form and characteristics, the gear box fault diagnosis method based on vibration signal analysis, and analysis of the vibration signal processing method for gear vibration signal analysis in time domain, including parameters, resonance demodulation method and cepstrum analysis method. Then using Visual C + + language and data acquisition card for real-time acquisition of gearbox vibration data software, including parameter setting, data acquisition module, signal real-time display module and data storage module. The data acquisition program is developed, the actual acquisition of gearbox vibration data of gear fault and bearing fault, and analyzed.

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.

scholarly journals A New Demodulation Method for Mechanical Fault Feature Extraction based on LOD and IEE

2021 ◽  
Vol 21 (3) ◽  
pp. 67-75
Author(s):  
Kang Zhang ◽  
Xiaorui Niu ◽  
Yunjiao Ma ◽  
Xiangmin Chen ◽  
Lida Liao ◽  
...  
Keyword(s):  
Mechanical Vibration ◽  
Energy Operator ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Gear Fault ◽  
Signal Demodulation ◽  
Simulation Signal ◽  
Adaptive Signal Decomposition ◽  
Adaptive Signal ◽  
Demodulation Method

Abstract The rolling bearing and gear fault features are generally shown as modulation characteristics of their vibration signals. The empirical envelope (EE) method is an accordingly common demodulation method. However, the EE method has the defects of over- and undershoot, which may lead to demodulation error. According to this, an envelope optimization algorithm -- empirical optimal envelope (EOE) is introduced into the EE method, and an improved empirical envelope (IEE) method is obtained to calculate the instantaneous amplitude and instantaneous frequency of mono-component modulation signal. Furthermore, aiming at the actual measured mechanical vibration signal has multi-component modulation feature, the IEE method is combined with an adaptive signal decomposition method -- local oscillatory characteristic decomposition (LOD) proposed by the author, thereby a new multi-component signal demodulation method based on LOD and IEE is proposed. The proposed method is compared with Hilbert transform (HT) and Teager energy operator (TEO) demodulation methods by the simulation signal and actual measured mechanical vibration signal. The results show that the demodulation effects including edge effects, negative frequency, over- and undershoot of the proposed method are significantly improved and can extract the rolling bearing and gear fault feature information clearly.

scholarly journals Adaptive Asymmetric Real Laplace Wavelet Filtering and Its Application on Rolling Bearing Early Fault Diagnosis

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Shuting Wan ◽  
Bo Peng
Keyword(s):  
Signal Analysis ◽  
Water Cycle ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Center Frequency ◽  
Asymmetric Structure ◽  
Bearing Fault ◽  
Narrowband Signal ◽  
Envelope Spectrum ◽  
Fault Characteristic

The early fault of rolling bearing is weak and may not be readily detected. To overcome this issue, the present paper comes up with a rolling bearing fault-diagnosing approach based on adaptive asymmetric real Laplace wavelet (ARLW) filtering, which is on the strength of water cycle optimization algorithm (WCA). Firstly, ARLW is introduced to filter the initial vibration signal since its waveform has the same asymmetric structure as the fault impact. Secondly, the optimum center frequency and bandwidth of ARLW is found out adaptively by applying the WCA through the proposed square envelope fault energy ratio (SEFER). Finally, envelope analysis is conducted to the narrowband signal obtained by the optimum ARLW filtering, and its envelope spectrum presents the rolling bearing fault characteristic frequency apparently. The proposed approach and two existing approaches are all tested in four signal analysis cases. The results are analyzed, and the conclusion is that the approach proposed by the present paper can detect the early fault of rolling bearing more accurately. The present research is valuable for diagnosing the early fault of rolling bearing.

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