Research on compound fault diagnosis of rolling bearing based on intrinsic time scale decomposition and information fusion

2021 ◽  
pp. 095745652110557
Author(s):  
Mingyue Yu ◽  
Guihong Guo
Keyword(s):  
Correlation Function ◽  
Information Fusion ◽  
Time Scale ◽  
Cross Correlation ◽  
Rolling Bearing ◽  
Cross Correlation Function ◽  
Intrinsic Time ◽  
Cross Correlation Analysis ◽  
Reconstructed Signal ◽  
Time Scale Decomposition

In view of the difficulty to effectively extract compound faults of rolling bearing from aero-engine and precisely identify their types, the paper has proposed a method integrating signal separation algorithm and information fusion. Firstly, the method decomposes the vibration acceleration signals collected by sensors from different positions at the same moment based on intrinsic time scale decomposition algorithm. Secondly, cross correlation analysis is given to the proper rotation component (PRC) of the same layer, which are obtained after decomposition and correspond to the sensors from different positions and cross-correlation function is introduced to embody information fusion. Thirdly, signals are reconstructed according to cross-correlation function of each PRC. Finally, based on the frequency spectrum of reconstructed signal, extract the characteristics of rolling bearing and identify the type of faults under different sensor combinations and multiple compound fault types. The result shows, the proposed method can effectively extract the characteristics of compound faults of bearing and precisely identify the type of faults under different sensor combinations and multiple compound fault types of rolling bearing.

Lagrangian characteristics of turbulence and scalar transport in direct numerical simulations

2001 ◽  
Vol 427 ◽  
pp. 241-274 ◽  
Author(s):  
P. K. YEUNG
Keyword(s):  
Reynolds Number ◽  
Correlation Function ◽  
Numerical Simulations ◽  
Time Scales ◽  
Time Scale ◽  
Cross Correlation ◽  
Scalar Fields ◽  
Direct Numerical Simulations ◽  
Cross Correlation Function ◽  
Differential Diffusion

A study of the Lagrangian statistical properties of velocity and passive scalar fields using direct numerical simulations is presented, for the case of stationary isotropic turbulence with uniform mean scalar gradients. Data at higher grid resolutions (up to 5123 and Taylor-scale Reynolds number 234) allow an update of previous velocity results at lower Reynolds number, including intermittency and dimensionality effects on vorticity time scales. The emphasis is on Lagrangian scalar time series which are new to the literature and important for stochastic mixing models. The variance of the ‘total’ Lagrangian scalar value (ϕ˜+, combining contributions from both mean and fluctuations) grows with time, with the velocity–scalar cross-correlation function and fluid particle displacements playing major roles. The Lagrangian increment of ϕ˜+ conditioned upon velocity and scalar fluctuations is well represented by a linear regression model whose parameters depend on both Reynolds number and Schmidt number. The Lagrangian scalar fluctuation is non-Markovian and has a longer time scale than the velocity, which is due to the strong role of advective transport, and is in contrast to results in an Eulerian frame where the scalars have shorter time scales. The scalar dissipation is highly intermittent and becomes de-correlated in time more rapidly than the energy dissipation. Differential diffusion for scalars with Schmidt numbers between 1/8 and 1 is characterized by asymmetry in the two-scalar cross-correlation function, a shorter time scale for the difference between two scalars, as well as a systematic decrease in the Lagrangian coherency spectrum up to at least the Kolmogorov frequency. These observations are consistent with recent work suggesting that differential diffusion remains important in the small scales at high Reynolds number.

scholarly journals Rolling bearing fault diagnosis based on adaptive smooth ITD and MF-DFA method

2019 ◽  
Vol 39 (4) ◽  
pp. 968-986
Author(s):  
Zhe Yuan ◽  
Tingting Peng ◽  
Dong An ◽  
Daniel Cristea ◽  
Mihai Alin Pop
Keyword(s):  
Fault Diagnosis ◽  
Time Scale ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Identification Accuracy ◽  
Threshold Function ◽  
Fluctuation Analysis ◽  
Intrinsic Time ◽  
Time Scale Decomposition ◽  
Proper Rotation

To effectively utilize a feature set to further improve fault diagnosis of a rolling bearing vibration signal, a method based on multi-fractal detrended fluctuation analysis (MF-DFA) and smooth intrinsic time-scale decomposition (SITD) was proposed. The vibration signal was decomposed into several proper rotation components by applying this new SITD method to overcome noise effects, preserve the effective signal, and improve the signal-to-noise ratio. Wavelet analysis was embedded in iteration procedures of intrinsic time-scale decomposition (ITD). For better results, an adaptive threshold function was used for signal recovery from noisy proper rotation components in the wavelet domain. Additionally, MF-DFA was used to reveal the multi-fractality present in the instantaneous amplitude of the proper rotation components. Finally, linear local tangent space alignment was applied for feature dimension reduction and to obtain fault characteristics of different types, further improving identification accuracy. The performance of the proposed method is determined to be superior to that of the ITD-MF-DFA method.

scholarly journals Autocorrelation and cross-correlation flow analysis along the confluence of the Kupa and Sava Rivers

2021 ◽  
Vol 36 (5) ◽  
pp. 67-77
Author(s):  
Marta Caren ◽  
Krešimir Pavlić
Keyword(s):  
Correlation Function ◽  
Cross Correlation ◽  
Time Lag ◽  
Flow Analysis ◽  
Meteorological Station ◽  
Cross Correlation Function ◽  
Maximum Value ◽  
Time Period ◽  
Cross Correlation Analysis ◽  
The Cross

In this paper, an autocorrelation and cross-correlation analysis of the flow of the Kupa and Sava rivers was performed. The analysis was performed at hydrological stations close to the confluence of these two rivers near the city of Sisak, based on data of mean daily flows and daily precipitation. The analysed time period is from 2008 to 2017, with the series being divided into two parts of five years each, from 2008 to 2012 and 2013 to 2017. Daily flow data were measured at the hydrological stations Farkašić on the Kupa River and Crnac on the Sava River, and data on precipitation at the main meteorological station and the automatic meteorological station Sisak. The maximum value of the cross-correlation function between the hydrological stations at the Kupa and Sava rivers is very high, but at a time lag of zero days. The value of the cross-correlation function remains high, up to 0.6 and up to a 4 day lag. The cross-correlation function between precipitation and hydrological data has a very low maximum value.

A new method to identify rotor–stator rubbing positions based on intrinsic time scale decomposition

2021 ◽  
pp. 095745652110557
Author(s):  
Mingyue Yu ◽  
Wangying Chen ◽  
Jinglin Wang ◽  
Haonan Cong
Keyword(s):  
Time Scale ◽  
Classification Algorithm ◽  
Noise Component ◽  
Identification Rate ◽  
Intrinsic Time ◽  
Reconstructed Signal ◽  
Time Scale Decomposition ◽  
The Difference ◽  
Rotation Component ◽  
Proper Rotation

To effectively identify the rotor–stator rubbing positions in aero-engine, the paper has proposed the combination of intrinsic time-scale decomposition (ITD) and classification algorithm. Regarding that with larger noise component in proper rotation component (PRC) signals after ITD, it will be more difficult to extract the characteristic information of rubbing faults, the PRC correspondings to the largest noise was eliminated. Meanwhile, signals were reconstructed based on residual proper rotation components, and positions of rubbing faults were identified according to the reconstructed signal. As rubbing extent and other factors cannot be completely the same in each rubbing, energy of reconstructed signal has been normalized to reduce the difference. Normalized energy indexes were inputted into classification algorithm as feature vectors to identify the positions of rubbing faults. To identify the superiority of approach, a comparison has been made between the proposed approach and the method of directly extracting normalized energy indexes of acceleration signals. The result of comparison shows that the two methods both work well in the identification rate of training and test samples; as for the identification rate for an unknown sample, the proposed method is superior to the other, with identification rate increasing by 17% and 9.4%.

scholarly journals A New Method Based on Time-Varying Filtering Intrinsic Time-Scale Decomposition and General Refined Composite Multiscale Sample Entropy for Rolling-Bearing Feature Extraction

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 451
Author(s):  
Jianpeng Ma ◽  
Song Han ◽  
Chengwei Li ◽  
Liwei Zhan ◽  
Guang-zhu Zhang
Keyword(s):  
Fault Diagnosis ◽  
Time Scale ◽  
Rolling Bearing ◽  
Sample Entropy ◽  
New Method ◽  
Frequency Separation ◽  
Time Varying ◽  
Rolling Bearings ◽  
Intrinsic Time ◽  
Time Scale Decomposition

The early fault diagnosis of rolling bearings has always been a difficult problem due to the interference of strong noise. This paper proposes a new method of early fault diagnosis for rolling bearings with entropy participation. First, a new signal decomposition method is proposed in this paper: intrinsic time-scale decomposition based on time-varying filtering. It is introduced into the framework of complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN). Compared with traditional intrinsic time-scale decomposition, intrinsic time-scale decomposition based on time-varying filtering can improve frequency-separation performance. It has strong robustness in the presence of noise interference. However, decomposition parameters (the bandwidth threshold and B-spline order) have significant impacts on the decomposition results of this method, and they need to be artificially set. Aiming to address this problem, this paper proposes rolling-bearing fault diagnosis optimization based on an improved coyote optimization algorithm (COA). First, the minimal generalized refined composite multiscale sample entropy parameter was used as the objective function. Through the improved COA algorithm, optimal intrinsic time-scale decomposition parameters based on time-varying filtering that match the input signal are obtained. By analyzing generalized refined composite multiscale sample entropy (GRCMSE), whether the mode component is dominated by the fault signal is determined. The signal is reconstructed and decomposed again. Finally, the mode component with the highest energy in the central frequency band is selected for envelope spectrum variation for fault diagnosis. Lastly, simulated and experimental signals were used to verify the effectiveness of the proposed method.

scholarly journals A Fault Feature Extraction Method for Rolling Bearing Based on Intrinsic Time-Scale Decomposition and AR Minimum Entropy Deconvolution

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Jiakai Ding ◽  
Liangpei Huang ◽  
Dongming Xiao ◽  
Lingli Jiang
Keyword(s):  
Time Scale ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Sample Entropy ◽  
Minimum Entropy ◽  
Feature Extraction Method ◽  
Intrinsic Time ◽  
Time Scale Decomposition ◽  
Weak Fault ◽  
Feature Frequency

It is very difficult to extract the feature frequency of the vibration signal of the rolling bearing early weak fault and in order to extract its feature frequency quickly and accurately. A method of extracting early weak fault vibration signal feature frequency of the rolling bearing by intrinsic time-scale decomposition (ITD) and autoregression (AR) minimum entropy deconvolution (MED) is proposed in this paper. Firstly, the original early weak fault vibration signal of the rolling bearing is decomposed by the ITD algorithm to proper rotations (PRs) with fault feature frequency. Then, the sample entropy value of each PR is calculated to find the largest PRs of the sample entropy. Finally, the AR-MED filtering algorithm is utilized to filter and reduce the noise of the largest PRs of the sample entropy value, and the early weak fault vibration signal feature frequency of the rolling bearing is accurately extracted. The results show that the ITD-AR-MED method can extract the early weak fault vibration signal feature frequency of the rolling bearing accurately.

A simulation study to examine the use of cross-correlation as an estimate of surface EMG cross talk

2003 ◽  
Vol 94 (4) ◽  
pp. 1324-1334 ◽  
Author(s):  
Madeleine M. Lowery ◽  
Nikolay S. Stoykov ◽  
Todd A. Kuiken
Keyword(s):  
Correlation Function ◽  
Motor Unit ◽  
Cross Talk ◽  
Cross Correlation ◽  
Surface Emg ◽  
Cross Correlation Function ◽  
Cross Correlation Analysis ◽  
Motor Unit Synchronization ◽  
Common Drive ◽  
The Cross

Cross-correlation between surface electromyogram (EMG) signals is commonly used as a means of quantifying EMG cross talk during voluntary activation. To examine the reliability of this method, the relationship between cross talk and the cross-correlation between surface EMG signals was examined by using model simulation. The simulation results illustrate an increase in cross talk with increasing subcutaneous fat thickness. The results also indicate that the cross-correlation function decays more rapidly with increasing distance from the active fibers than cross talk, which was defined as the normalized EMG amplitude during activation of a single muscle. The influence of common drive and short-term motor unit synchronization on the cross-correlation between surface EMG signals was also examined. While common drive did not alter the maximum value of the cross-correlation function, the correlation increased with increasing motor unit synchronization. It is concluded that cross-correlation analysis is not a suitable means of quantifying cross talk or of distinguishing between cross talk and coactivation during voluntary contraction. Furthermore, it is possible that a high correlation between surface EMG signals may reflect an association between motor unit firing times, for example due to motor unit synchronization.

scholarly journals Cross-Correlation Analysis of Galaxies with Different Luminosity

1988 ◽  
Vol 130 ◽  
pp. 554-554
Author(s):  
X.-Y. Xia ◽  
Z.-G. Deng ◽  
Y.-Z. Liu
Keyword(s):  
Correlation Function ◽  
Linear Regression ◽  
Correlation Analysis ◽  
Cross Correlation ◽  
Absolute Magnitude ◽  
Cross Correlation Function ◽  
Cross Correlation Analysis ◽  
Luminous Galaxies ◽  
Point Correlation ◽  
Straight Lines

In the former work (Xia, Deng and Zhou, 1986), we have showed by two- point correlation analysis that more luminous galaxies cluster stronger. Now we present the result of cross-correlation analysis for galaxies with different luminosity. This analysis supplies information about the relations between the distributions of galaxies with different luminosity. The analyses are based on the data given by CfA survey and have made the same corrections as in the former work. The samples are divided into three subgroups in absolute magnitude ranges: a) −21–22, b) −20–21 and c)−19–20. We make the cross-correlation analysis for each two subgroups. Fig. 1 gives the obtained cross-correlation function ξc(r) and Fig. 2 shows the log ξc(r)-log r diagram, the straight lines in Fig. 2 are given by linear regression. These results show that the two brightest subgroups have the strongest correlation. Combining with the results of former work, it follows that the probability of two brighter galaxies being close to each other is larger than that of fainter galaxies.

scholarly journals Rolling Bearing Feature Extraction Method Based on Improved Intrinsic Time-Scale Decomposition and Mathematical Morphological Analysis

2021 ◽  
Vol 11 (6) ◽  
pp. 2719
Author(s):  
Jianpeng Ma ◽  
Guodong Chen ◽  
Chengwei Li ◽  
Liwei Zhan ◽  
Guang-Zhu Zhang
Keyword(s):  
Feature Extraction ◽  
Time Scale ◽  
Rolling Bearing ◽  
Feature Extraction Method ◽  
Intrinsic Time ◽  
Extraction Scheme ◽  
Time Scale Decomposition ◽  
Adaptive Feature Extraction ◽  
Envelope Spectrum ◽  
Fault Feature Extraction

To overcome the difficulty of extracting the feature frequency of early bearing faults, this paper proposes an adaptive feature extraction scheme. First, the improved intrinsic time-scale decomposition, proposed in this paper, is used as a noise reduction method. Then, we use the adaptive composite quantum morphology analysis method, also proposed in this paper, to perform an adaptive demodulation analysis on the signal, and finally, extract the fault characteristics in the envelope spectrum. The experimental results show that the scheme performs well in the early fault feature extraction of rolling bearings.

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