Underdetermined Blind Separation for Bearings Faults Based on the Improved Morphological Filter

2014 ◽  
Vol 670-671 ◽  
pp. 1200-1204
Author(s):  
Zhou Jun ◽  
Wu Xing ◽  
Yi Lin Chi ◽  
Pan Nan
Keyword(s):  
Background Noise ◽  
Clustering Algorithm ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Blind Separation ◽  
Sparse Component Analysis ◽  
Simulated Annealing Genetic Algorithm ◽  
Actual Environment ◽  
Mixing Matrix ◽  
Characteristic Signal

Aiming at the condition of the strong background noise, many interference sources and unknown the number of sources in the actual industrial field, a method based on multi-scale multi-structure close-open average combination morphological filtering(C-OACMF) and sparse component analysis (SCA) was proposed to deal with the blind source separation problem of rotation machines. First, the C-OACMF was used to filter out background noise signals and extract the characteristic signal of observation signals, then using the simulated annealing genetic algorithm of fuzzy C-average clustering algorithm estimates the mixing matrix, the linear programming is finally used to estimate the source signals. Through the actual environment composite rolling bearing fault vibration signal extraction experiments verify the effectiveness and accuracy of the proposed algorithm.

Rolling bearing degradation condition clustering using multidimensional degradation feature and Gath–Geva fuzzy clustering algorithm

2020 ◽  
pp. 107754632095495
Author(s):  
Bing Wang ◽  
Xiong Hu ◽  
Tao X Mei ◽  
Sun D Jian ◽  
Wang Wei
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Chaotic Map ◽  
Vibration Signal ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Performance Degradation ◽  
Time Parameter ◽  
Mechanical Equipment ◽  
Time Dimension

In allusion to the issue of rolling bearing degradation feature extraction and degradation condition clustering, a logistic chaotic map is introduced to analyze the advantages of C0 complexity and a technique based on a multidimensional degradation feature and Gath–Geva fuzzy clustering algorithmic is proposed. The multidimensional degradation feature includes C0 complexity, root mean square, and curved time parameter which is more in line with the performance degradation process. Gath–Geva fuzzy clustering is introduced to divide different conditions during the degradation process. A rolling bearing lifetime vibration signal from intelligent maintenance system bearing test center was introduced for instance analysis. The results show that C0 complexity is able to describe the degradation process and has advantages in sensitivity and calculation speed. The introduced degradation indicator curved time parameter can reflect the agglomeration character of the degradation condition at time dimension, which is more in line with the performance degradation pattern of mechanical equipment. The Gath–Geva fuzzy clustering algorithmic is able to cluster degradation condition of mechanical equipment such as bearings accurately.

Research on Fault Diagnosis for Roller Bearings Based on MED and Slice Bi-Spectrum

2014 ◽  
Vol 898 ◽  
pp. 961-964 ◽  
Author(s):  
Shang Kun Liu ◽  
Bin Pang ◽  
Gui Ji Tang
Keyword(s):  
Background Noise ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Measured Signal ◽  
Minimum Entropy ◽  
Roller Bearings ◽  
Fault Features ◽  
Envelope Signal ◽  
Envelope Spectrum ◽  
Environment Noise

The rolling bearings fault feature under strong background noise is very weak for reasons of environment noise impact and signal attenuation. To extract the fault features of roller bearings effectively, a new method based on Minimum entropy deconvolution (MED) and slice bi-spectrum is proposed. The paper Firstly decreases the strong background noise of rolling bearing by the MED method, and then calculates the envelope signal of the de-noised signal. Finally, analyzes the envelope signal with slice bi-spectrum and extracted the fault characteristic frequency. The effectiveness of the proposed method was validated by analysis of both a simulated faulty bearing vibration signal and the experiment measured signal of rolling bearing, and it was also compared with the method of envelope spectrum.

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 A Novel Intelligent Method for Bearing Fault Diagnosis Based on EEMD Permutation Entropy and GG Clustering

2020 ◽  
Vol 10 (1) ◽  
pp. 386 ◽  
Author(s):  
Jingbao Hou ◽  
Yunxin Wu ◽  
Hai Gong ◽  
A. S. Ahmad ◽  
Lei Liu
Keyword(s):  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Feature Vector ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Permutation Entropy ◽  
Linear Discriminant ◽  
Bearing Fault ◽  
Combination Methods ◽  
Diagnosis Method

For a rolling bearing fault that has nonlinearity and nonstationary characteristics, it is difficult to identify the fault category. A rolling bearing clustering fault diagnosis method based on ensemble empirical mode decomposition (EEMD), permutation entropy (PE), linear discriminant analysis (LDA), and the Gath–Geva (GG) clustering algorithm is proposed. Firstly, we decompose the vibration signal using EEMD, and several inherent modal components are obtained. Then, the permutation entropy values of each modal component are calculated to get the entropy feature vector, and the entropy feature vector is reduced by the LDA method to be used as the input of the clustering algorithm. The data experiments show that the proposed fault diagnosis method can obtain satisfactory clustering indicators. It implies that compared with other mode combination methods, the fault identification method proposed in this study has the advantage of better intra-class compactness of clustering results.

scholarly journals Fault Diagnosis of a Helical Gearbox Based on an Adaptive Empirical Wavelet Transform in Combination with a Spectral Subtraction Method

2019 ◽  
Vol 9 (8) ◽  
pp. 1696 ◽  
Author(s):  
Wang ◽  
Lee
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Background Noise ◽  
Vibration Signal ◽  
Spectral Subtraction ◽  
Accurate Identification ◽  
Side Band ◽  
Empirical Wavelet Transform ◽  
Gear Fault ◽  
Fault Characteristic

Fault characteristic extraction is attracting a great deal of attention from researchers for the fault diagnosis of rotating machinery. Generally, when a gearbox is damaged, accurate identification of the side-band features can be used to detect the condition of the machinery equipment to reduce financial losses. However, the side-band feature of damaged gears that are constantly disturbed by strong jamming is embedded in the background noise. In this paper, a hybrid signal-processing method is proposed based on a spectral subtraction (SS) denoising algorithm combined with an empirical wavelet transform (EWT) to extract the side-band feature of gear faults. Firstly, SS is used to estimate the real-time noise information, which is used to enhance the fault signal of the helical gearbox from a vibration signal with strong noise disturbance. The empirical wavelet transform can extract amplitude-modulated/frequency-modulated (AM-FM) components of a signal using different filter bands that are designed in accordance with the signal properties. The fault signal is obtained by building a flexible gear for a helical gearbox with ADAMS software. The experiment shows the feasibility and availability of the multi-body dynamics model. The spectral subtraction-based adaptive empirical wavelet transform (SS-AEWT) method was applied to estimate the gear side-band feature for different tooth breakages and the strong background noise. The verification results show that the proposed method gives a clearer indication of gear fault characteristics with different tooth breakages and the different signal-noise ratio (SNR) than the conventional EMD and LMD methods. Finally, the fault characteristic frequency of a damaged gear suggests that the proposed SS-AEWT method can accurately and reliably diagnose faults of a gearbox.

The Spectrum Segmentation Algorithm of Multimode Vibration Signal Based on Spectral Clustering

2011 ◽  
Vol 121-126 ◽  
pp. 2372-2376
Author(s):  
Dan Dan Wang ◽  
Yu Zhou ◽  
Qing Wei Ye ◽  
Xiao Dong Wang
Keyword(s):  
Wave Packet ◽  
Frequency Domain ◽  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Wave Packets ◽  
Vibration Signal ◽  
Segmentation Algorithm ◽  
Frequency Curve ◽  
Macroscopic Observation ◽  
Spectral Clustering Algorithm

The mode peaks in frequency domain of vibration signal are strongly interfered by strong noise, causing the inaccuracy mode parameters. According to this situation, this paper comes up with the thought of mode-peak segmentation based on the spectral clustering algorithm. First, according to the concept of wave packet, the amplitude-frequency of vibration signal is divided into wave packets. Taking each wave packet as a sample of clustering algorithm, the spectral clustering algorithm is used to classify these wave packets. The amplitude-frequency curve of a mode peak becomes a big wave packet in macroscopic. The experiment to simulation signals indicates that this spectral clustering algorithm could accord with the macroscopic observation of mode segmentation effectively, and has outstanding performance especially in strong noise.

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.

scholarly journals Two De-Noising Methods Based on Gabor Transform

2011 ◽  
Vol 2-3 ◽  
pp. 176-181
Author(s):  
Yong Jun Shen ◽  
Guang Ming Zhang ◽  
Shao Pu Yang ◽  
Hai Jun Xing
Keyword(s):  
Wavelet Transform ◽  
Adaptive Filtering ◽  
Time Domain ◽  
Background Noise ◽  
Averaging Method ◽  
Vibration Signal ◽  
Gabor Transform ◽  
Transform Domain ◽  
Computation Scheme ◽  
Source Signals

Two de-noising methods, named as the averaging method in Gabor transform domain (AMGTD) and the adaptive filtering method in Gabor transform domain (AFMGTD), are presented in this paper. These two methods are established based on the correlativity of the source signals and the background noise in time domain and Gabor transform domain, that is to say, the uncorrelated source signals and background noise in time domain would still be uncorrelated in Gabor transform domain. The construction and computation scheme of these two methods are investigated. The de-noising performances are illustrated by some simulation signals, and the wavelet transform is used to compare with these two new de-noising methods. The results show that these two methods have better de-noising performance than the wavelet transform, and could reduce the background noise in the vibration signal more effectively.

scholarly journals A fault diagnosis method based on Auxiliary Classifier Generative Adversarial Network for rolling bearing

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0246905
Author(s):  
Chunming Wu ◽  
Zhou Zeng
Keyword(s):  
Fault Diagnosis ◽  
Noise Pollution ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Generative Adversarial Network ◽  
High Background ◽  
Bearing Fault Diagnosis ◽  
Adversarial Network ◽  
Practical Engineering

Rolling bearing fault diagnosis is one of the challenging tasks and hot research topics in the condition monitoring and fault diagnosis of rotating machinery. However, in practical engineering applications, the working conditions of rotating machinery are various, and it is difficult to extract the effective features of early fault due to the vibration signal accompanied by high background noise pollution, and there are only a small number of fault samples for fault diagnosis, which leads to the significant decline of diagnostic performance. In order to solve above problems, by combining Auxiliary Classifier Generative Adversarial Network (ACGAN) and Stacked Denoising Auto Encoder (SDAE), a novel method is proposed for fault diagnosis. Among them, during the process of training the ACGAN-SDAE, the generator and discriminator are alternately optimized through the adversarial learning mechanism, which makes the model have significant diagnostic accuracy and generalization ability. The experimental results show that our proposed ACGAN-SDAE can maintain a high diagnosis accuracy under small fault samples, and have the best adaptation performance across different load domains and better anti-noise performance.

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