scholarly journals Phase Synchrony Analysis of Rolling Bearing Vibrations and Its Application to Failure Identification

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2964 ◽  
Author(s):  
Qing Zhang ◽  
Tingting Jiang ◽  
Joseph D. Yan
Keyword(s):  
Phase Synchronization ◽  
Chaotic Behavior ◽  
Rolling Bearing ◽  
Bearing Failure ◽  
Phase Synchrony ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Initial Stage ◽  
Failure Evolution ◽  
Band Limited

As the failure-induced component (FIC) in the vibration signals of bearings transmits through housings and shafts, potential phase synchronization is excited among multichannel signals. As phase synchrony analysis (PSA) does not involve the chaotic behavior of signals, it is suitable for characterizing the operating state of bearings considering complicated vibration signals. Therefore, a novel PSA method was developed to identify and track the failure evolution of bearings. First, resonance demodulation and variational mode decomposition (VMD) were combined to extract the mono-component or band-limited FIC from signals. Then, the instantaneous phase of the FIC was analytically solved using Hilbert transformation. The generalized phase difference (GPD) was used to quantify the relationship between FICs extracted from different vibration signals. The entropy of the GPD was regarded as the indicator for quantifying failure evolution. The proposed method was applied to the vibration signals obtained from an accelerated failure experiment and a natural failure experiment. Results showed that phase synchronization in bearing failure evolution was detected and evaluated effectively. Despite the chaotic behavior of the signals, the phase synchronization indicator could identify bearing failure during the initial stage in a robust manner.

A Modified EEMD Decomposition for the Detection of Rolling Bearing Faults

2014 ◽  
Vol 548-549 ◽  
pp. 369-373
Author(s):  
Yuan Cheng Shi ◽  
Yong Ying Jiang ◽  
Hai Feng Gao ◽  
Jia Wei Xiang
Keyword(s):  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Hilbert Spectrum ◽  
Bearing Faults ◽  
Mode Decomposition ◽  
Fault Features ◽  
Rolling Element ◽  
Inner Race

The vibration signals of rolling element bearings are non-linear and non-stationary and the corresponding fault features are difficult to be extracted. EEMD (Ensemble empirical mode decomposition) is effective to detect bearing faults. In the present investigation, MEEMD (Modified EEMD) is presented to diagnose the outer and inner race faults of bearings. The original vibration signals are analyzed using IMFs (intrinsic mode functions) extracted by MEEMD decomposition and Hilbert spectrum in the proposed method. The numerical and experimental results of the comparison between MEEMD and EEMD indicate that the proposed method is more effective to extract the fault features of outer and inner race of bearings than EEMD.

scholarly journals Multiple Fault Detection of Rolling Bearing through Ensemble Empirical Mode Decomposition of Vibration Signal

2019 ◽  
Vol 9 (2) ◽  
pp. 2724-2726
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Data Driven ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Multiple Faults ◽  
Mode Decomposition

Generally, two or more faults occur simultaneously in the bearings. These Compound Faults (CF) in bearing, are most difficult type of faults to detect, by any data-driven method including machine learning. Hence, it is a primary requirement to decompose the fault vibration signals logically, so that frequencies can be grouped in parts. Empirical Mode Decomposition (EMD) is one of the simplest techniques of decomposition of signals. In this paper we have used Ensemble Empirical Mode Decomposition (EEMD) technique for compound fault detection/identification. Ensembled Empirical Mode Decomposition is found useful, where a white noise helps to detect the bearing frequencies. The graphs show clearly the capability of EEMD to detect the multiple faults in rolling bearings.

scholarly journals Fault Diagnosis of Rolling Bearing based on an Improved Denoising Technique using Complete Ensemble Empirical Mode Decomposition and Adaptive Thresholding Method

2021 ◽  
Author(s):  
Prashant Kumar Sahu ◽  
Rajiv Nandan Rai
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal Reconstruction ◽  
Low Frequency ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Adaptive Thresholding ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Envelope Spectrum

Abstract The vibration signals for rotating machines are generally polluted by excessive noise and can lose the fault information at the early development phase. In this paper, an improved denoising technique is proposed for early faults diagnosis of rolling bearing based on the complete ensemble empirical mode decomposition (CEEMD) and adaptive thresholding (ATD) method. Firstly, the bearing vibration signals are decomposed into a set of various intrinsic mode functions (IMFs) using CEEMD algorithm. The IMFs grouping and selection are formed based upon the correlation coefficient value. The noise-predominant IMFs are subjected to adaptive thresholding for denoising and then added to the low-frequency IMFs for signal reconstruction. The effectiveness of the proposed method denoised signals are measured based on kurtosis value and the envelope spectrum analysis. The presented method results on experimental datasets illustrate that the proposed approach is an effective denoising technique for early fault detection in the rolling bearing.

Degradation Feature Extraction of Rolling Bearings Based on Optimal Ensemble Empirical Mode Decomposition and Improved Composite Spectrum Analysis

2018 ◽  
Author(s):  
Fengli Wang ◽  
Hua Chen
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Single Point ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Significant Feature ◽  
Vibration Signals ◽  
Composite Spectrum ◽  
Optimal Ensemble ◽  
Mode Decomposition

Rolling bearing is a key part of turbomachinery. The performance and reliability of the bearing is vital to the safe operation of turbomachinery. Therefore, degradation feature extraction of rolling bearing is important to prevent it from failure. During rolling bearing degradation, machine vibration can increase, and this may be used to predict the degradation. The vibration signals are however complicated and nonlinear, making it difficult to extract degradation features effectively. Here, a novel degradation feature extraction method based on optimal ensemble empirical mode decomposition (EEMD) and improved composite spectrum (CS) analysis is proposed. Firstly, because only a few IMFs are expected to contain the information related to bearing fault, EEMD is utilized to pre-process the vibration signals. An optimization method is designed for adaptively determining the appropriate EEMD parameters for the signal, so that the significant feature components of the faulty bearing can be extracted from the signal and separated from background noise and other irrelevant components to bearing faults. Then, Bayesian information criterion (BIC) and correlation kurtosis (CK) are employed to select the sensitive intrinsic mode function (IMF) components and obtain fault information effectively. Finally, an improved CS analysis algorithm is used to fuse the selected sensitive IMF components, and the CS entropy (CSE) is extracted as degradation feature. Experimental data on the test bearings with single point faults separately at the inner race and rolling element were studied to demonstrate the capabilities of the proposed method. The results show that it can assess the bearing degradation status and has good sensitivity and good consistency to the process of bearing degradation.

scholarly journals A Novel Method for Identifying Crack and Shaft Misalignment Faults in Rotor Systems under Noisy Environments Based on CNN

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5158 ◽  
Author(s):  
Wang Zhao ◽  
Chunrong Hua ◽  
Dawei Dong ◽  
Huajiang Ouyang
Keyword(s):  
Signal To Noise Ratio ◽  
Principal Component ◽  
Rotor System ◽  
Fault Classification ◽  
Vibration Signals ◽  
Rotor Systems ◽  
Response Characteristics ◽  
Mode Decomposition ◽  
Shaft Misalignment ◽  
Band Limited

Crack and shaft misalignment are two common types of fault in a rotor system, both of which have very similar dynamic response characteristics, and the vibration signals are vulnerable to noise contamination because of the interaction among different components of rotating machinery in the actual industrial environment, resulting in great difficulties in fault identification of a rotor system based on vibration signals. A method for identification of faults in the form of crack and shaft misalignments is proposed in this paper, which combines variational mode decomposition (VMD) and probabilistic principal component analysis (PPCA) to denoise the collected vibration signals from a test rig and then achieve signal feature extraction and fault classification with convolutional artificial neural network (CNN). The key parameters of the CNN are optimized and determined by genetic algorithm (GA) firstly, and the domain adaptability of the trained network is verified by the signals with different signal-to-noise ratio (SNR) values; then, the noisy vibration signals are decomposed into multiple band-limited intrinsic modal functions by VMD, and further data dimension reduction is performed by PPCA to realize the separation of the useful signals from noise; finally, the crack and shaft misalignment of the rotor system are identified by the optimized CNN. The results show that the proposed method can effectively remove the interference noise and extract the intrinsic features of the vibration signals, and the recognition rates of crack and shaft misalignment faults for the rotor system with different SNR values are more than 99%, which is considered to be very effective and useful.

Intelligent fault diagnosis of rolling bearings using variational mode decomposition and self-organizing feature map

2020 ◽  
Vol 26 (21-22) ◽  
pp. 1886-1897 ◽  
Author(s):  
Jialing Zhang ◽  
Jimei Wu ◽  
Bingbing Hu ◽  
Jiahui Tang
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
The Self ◽  
Permutation Entropy ◽  
Variational Mode Decomposition ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Feature Map ◽  
Mode Decomposition ◽  
Self Organizing

Rotating machinery contains numerous rolling bearings, which are critical for ensuring the normal working position and accurate operation of individual shaft systems. However, damage to rolling bearings can change their damping, stiffness, and elastic force. As a result, fault signals appear nonlinear and nonstationary. Vibration signals thus become difficult to diagnose clearly, especially in the incipient fault stage. To solve this problem, this article proposes an intelligent approach based on variational mode decomposition and the self-organizing feature map for rolling bearing fault diagnosis. First, the intrinsic mode function components of rolling bearing vibration signals are effectively separated by variational mode decomposition. Then, permutation entropy is used to extract feature vectors, which are used as training and testing data for the self-organizing feature map network. Finally, the various fault types of states are clustered on an intuitive visualization map. Clustering results of the experimental signal and the measured signal prove that the proposed method can successfully extract and cluster the rolling bearing faults in engineering applications. The proposed method improves the fault recognition rate to some extent over traditional methods.

scholarly journals Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery

2018 ◽  
Vol 8 (9) ◽  
pp. 1441 ◽  
Author(s):  
Liang Fang ◽  
Hongchun Sun
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Single Channel ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Fault Features ◽  
Kernel Independent Component Analysis

A method is proposed to improve the feature extraction of vibration signals of rotating machinery. Firstly, the single-channel vibration signal is decomposed with ensemble empirical mode decomposition (EEMD). Then, the number of fault signals can be estimated with singular-value decomposition (SVD). Finally, the fault signals can be extracted with kernel-independent component analysis (KICA). The advantage of this method is that it can estimate the number of fault signals of single-channel vibration signals and can extract the fault features clearly. Compared with wavelets, empirical mode decomposition (EMD), variational mode decomposition (VMD) and EEMD, the better performance of this method is proven with three experimental analyses of faulty gear, a faulty rolling bearing and a faulty shaft. The results demonstrate that the proposed method is efficient to extract the fault features of single-channel vibration signals of rotating machinery.

Analysis of hydropower unit vibration signals based on variational mode decomposition

2015 ◽  
Vol 23 (12) ◽  
pp. 1938-1953 ◽  
Author(s):  
Xueli An ◽  
Luoping Pan ◽  
Fei Zhang
Keyword(s):  
Hydraulic Turbine ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Accurate Analysis ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
The Face ◽  
Band Limited ◽  
Nonlinear Signal

The vibration signals of hydropower units are nonstationary when serious vortex occurs in the draft tube of the hydraulic turbine. The traditional signal analysis method based on Fourier transform is not suitable for the nonstationary signals. In the face of the nonstationarity of such signals and the limitation of the empirical mode decomposition method, a new nonstationary and nonlinear signal analyzing method based on variational mode decomposition (VMD) is introduced into hydropower unit vibration signals analysis. Firstly, VMD is used to decompose the signal into an ensemble of band-limited intrinsic mode functions components. Then, frequency spectrum analysis of these components is conducted to obtain the characteristic frequencies of the signal caused by the serious vortex of hydraulic turbine. Analysis of real test data shows that this proposed method can effectively suppress mode mixing. It can realize accurate analysis of nonstationary vibration signals. This provides a new way for analyzing hydropower unit vibration signals.

scholarly journals A Hybrid Approach for Fault Diagnosis of Railway Rolling Bearings Using STWD-EMD-GA-LSSVM

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Dechen Yao ◽  
Jianwei Yang ◽  
Xi Li ◽  
Chunqing Zhao
Keyword(s):  
Fault Diagnosis ◽  
Hybrid Approach ◽  
Rolling Bearing ◽  
Support Vector ◽  
The Novel ◽  
Intrinsic Mode Functions ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Novel Method

Vibration signals resulting from railway rolling bearings are nonstationary by nature; this paper proposes a hybrid approach for the fault diagnosis of railway rolling bearings using segment threshold wavelet denoising (STWD), empirical mode decomposition (EMD), genetic algorithm (GA), and least squares support vector machine (LSSVM). The original signal is first denoised using STWD as a prefilter, which improves the subsequent decomposition into a number of intrinsic mode functions (IMFs) using EMD. Secondly, the IMF energy-torques are extracted as feature parameters. Concurrently, a GA is employed to optimize the LSSVM to improve the classification accuracy. Finally, the extracted features are used as inputs for classification by the GA-LSSVM. Actual railway rolling bearing vibration signals are used to experimentally verify the effectiveness of the proposed method. The results show that the novel method is effective and accurate for fault diagnosis of railway rolling bearings.

scholarly journals Analysis method of the cavitation vibration signals in poppet valve based on EEMD

2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199811
Author(s):  
Beibei Li ◽  
Qiao Zhao ◽  
Huaiyi Li ◽  
Xiumei Liu ◽  
Jichao Ma ◽  
...  
Keyword(s):  
Ensemble Empirical Mode Decomposition ◽  
Center Frequency ◽  
Analysis Method ◽  
Vibration Signals ◽  
Poppet Valve ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Cavitation Intensity ◽  
Eemd Method ◽  
Peak Value

To study the vibration characteristics of the poppet valve induced by cavitation, the signal analysis method based on the ensemble empirical mode decomposition (EEMD) method was studied experimentally. The component induced by cavitation was separated from the vibration signals through the EEMD method. The results show that the IMF2 component has the largest amplitude and energy of all components. The root mean square (RMS) value, peak value of marginal spectrum, and center frequency of marginal spectrum of the IMF2 component were studied in detail. The RMS value and the peak value of the marginal spectrum decrease with a decrease of cavitation intensity. The center frequency of marginal spectrum is between 12 kHz and 20 kHz, and the center frequency first increases and then decreases with a decrease of cavitation intensity. The change rate of the center frequency also decreases with an increase of inlet pressure.

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