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.

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 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 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.

scholarly journals An Enhanced Intrinsic Time-Scale Decomposition Method Based on Adaptive Lévy Noise and Its Application in Bearing Fault Diagnosis

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 617
Author(s):  
Jianpeng Ma ◽  
Shi Zhuo ◽  
Chengwei Li ◽  
Liwei Zhan ◽  
Guangzhu Zhang
Keyword(s):  
Fault Diagnosis ◽  
Time Scale ◽  
Lévy Noise ◽  
Model Parameters ◽  
Rolling Bearings ◽  
Intrinsic Time ◽  
Time Scale Decomposition ◽  
Weak Fault ◽  
Levy Noise ◽  
The Impact

When early failures in rolling bearings occur, we need to be able to extract weak fault characteristic frequencies under the influence of strong noise and then perform fault diagnosis. Therefore, a new method is proposed: complete ensemble intrinsic time-scale decomposition with adaptive Lévy noise (CEITDALN). This method solves the problem of the traditional complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN) method not being able to filter nonwhite noise in measured vibration signal noise. Therefore, in the method proposed in this paper, a noise model in the form of parameter-adjusted noise is used to replace traditional white noise. We used an optimization algorithm to adaptively adjust the model parameters, reducing the impact of nonwhite noise on the feature frequency extraction. The experimental results for the simulation and vibration signals of rolling bearings showed that the CEITDALN method could extract weak fault features more effectively than traditional methods.

scholarly journals A Feature Extraction Method of Ship-Radiated Noise Based on Fluctuation-Based Dispersion Entropy and Intrinsic Time-Scale Decomposition

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 693 ◽  
Author(s):  
Zhaoxi Li ◽  
Yaan Li ◽  
Kai Zhang
Keyword(s):  
Feature Extraction ◽  
Time Scale ◽  
Recognition Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Extraction Method ◽  
Radiated Noise ◽  
Intrinsic Time ◽  
Different Types ◽  
Time Scale Decomposition

To improve the feature extraction of ship-radiated noise in a complex ocean environment, fluctuation-based dispersion entropy is used to extract the features of ten types of ship-radiated noise. Since fluctuation-based dispersion entropy only analyzes the ship-radiated noise signal in single scale and it cannot distinguish different types of ship-radiated noise effectively, a new method of ship-radiated noise feature extraction is proposed based on fluctuation-based dispersion entropy (FDispEn) and intrinsic time-scale decomposition (ITD). Firstly, ten types of ship-radiated noise signals are decomposed into a series of proper rotation components (PRCs) by ITD, and the FDispEn of each PRC is calculated. Then, the correlation between each PRC and the original signal are calculated, and the FDispEn of each PRC is analyzed to select the Max-relative PRC fluctuation-based dispersion entropy as the feature parameter. Finally, by comparing the Max-relative PRC fluctuation-based dispersion entropy of a certain number of the above ten types of ship-radiated noise signals with FDispEn, it is discovered that the Max-relative PRC fluctuation-based dispersion entropy is at the same level for similar ship-radiated noise, but is distinct for different types of ship-radiated noise. The Max-relative PRC fluctuation-based dispersion entropy as the feature vector is sent into the support vector machine (SVM) classifier to classify and recognize ten types of ship-radiated noise. The experimental results demonstrate that the recognition rate of the proposed method reaches 95.8763%. Consequently, the proposed method can effectively achieve the classification of ship-radiated noise.

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.

A novel intrinsic time–scale decomposition–graph signal processing–based characteristic extraction method for rotor–stator rubbing of aeroengine

2021 ◽  
pp. 107754632098596
Author(s):  
Mingyue Yu
Keyword(s):  
Signal Processing ◽  
Autocorrelation Function ◽  
Time Scale ◽  
Energy Index ◽  
Signal Processing Algorithm ◽  
Intrinsic Time ◽  
Graph Signal Processing ◽  
Laplacian Energy ◽  
Rotational Components ◽  
Time Scale Decomposition

Intrinsic time-scale decomposition and graph signal processing are combined to effectively identify a rotor–stator rubbing fault. The vibration signal is decomposed into mutually independent rotational components, and then, the Laplacian energy index is obtained by the graph signal of the autocorrelation function of rotational components, and the signal is reconstructed by an autocorrelation function of each proper rotation (PR) component relative to smaller Laplacian energy index (less complexity). Finally, characteristics are extracted from rotor–stator rubbing faults in an aeroengine according to square demodulation spectrum of a reconstructed signal. To validate the effectiveness of the algorithm, a comparative analysis is made among traditional intrinsic time-scale decomposition algorithm, combination of intrinsic time-scale decomposition and autocorrelation function, and the proposed intrinsic time-scale decomposition–graph signal processing algorithm. Comparative result shows that the proposed intrinsic time-scale decomposition–graph signal processing algorithm is more precise and effective than the traditional intrinsic time-scale decomposition and intrinsic time-scale decomposition and autocorrelation function algorithms in extracting characteristic frequency and frequency multiplication of rotor–stator rubbing faults and can greatly reduce the number of noise components irrelevant to faults.

scholarly journals Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach

Algorithms ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 184 ◽  
Author(s):  
Qing Li ◽  
Steven Liang
Keyword(s):  
Vibration Signal ◽  
Singular Values ◽  
Rolling Bearing ◽  
L1 Norm ◽  
Detection Approach ◽  
The Matrix ◽  
Noisy Observation ◽  
Weak Fault ◽  
Convex Regularization ◽  
Fault Information

Aimed at the issue of estimating the fault component from a noisy observation, a novel detection approach based on augmented Huber non-convex penalty regularization (AHNPR) is proposed. The core objectives of the proposed method are that (1) it estimates non-zero singular values (i.e., fault component) accurately and (2) it maintains the convexity of the proposed objective cost function (OCF) by restricting the parameters of the non-convex regularization. Specifically, the AHNPR model is expressed as the L1-norm minus a generalized Huber function, which avoids the underestimation weakness of the L1-norm regularization. Furthermore, the convexity of the proposed OCF is proved via the non-diagonal characteristic of the matrix BTB, meanwhile, the non-zero singular values of the OCF is solved by the forward–backward splitting (FBS) algorithm. Last, the proposed method is validated by the simulated signal and vibration signals of tapered bearing. The results demonstrate that the proposed approach can identify weak fault information from the raw vibration signal under severe background noise, that the non-convex penalty regularization can induce sparsity of the singular values more effectively than the typical convex penalty (e.g., L1-norm fused lasso optimization (LFLO) method), and that the issue of underestimating sparse coefficients can be improved.

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