A Comprehensive Study of Vibration Signals for a Thin Shell Structure Using Enhanced Independent Component Analysis and Experimental Validation

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Wei Cheng ◽  
Zhengjia He ◽  
Zhousuo Zhang
Keyword(s):  
Independent Component Analysis ◽  
Thin Shell ◽  
Component Analysis ◽  
Independent Component ◽  
Vibration Monitoring ◽  
Monitoring And Control ◽  
Source Information ◽  
Vibration Signals ◽  
Machinery Condition Monitoring ◽  
Source Number

Vibration source information (source number, source waveforms, and source contributions) of gears, bearings, motors, and shafts is very important for machinery condition monitoring, fault diagnosis, and especially vibration monitoring and control. However, it has been a challenging to effectively extract the source information from the measured mixed vibration signals without a priori knowledge of the mixing mode and sources. In this paper, we propose source number estimation, source separation, and source contribution evaluation methods based on an enhanced independent component analysis (EICA). The effects of nonlinear mixing mode and different source number on source separation are studied with typical vibration signals, and the effectiveness of the proposed methods is validated by numerical case studies and experimental studies on a thin shell test bed. The conclusions show that the proposed methods have a high accuracy for thin shell structures. This research benefits for application of independent component analysis (ICA) to solve the vibration monitoring and control problems for thin shell structures and provides important references for machinery condition monitoring and fault diagnosis.

Source Contribution Evaluation of Mechanical Vibration Signals via Enhanced Independent Component Analysis

2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Wei Cheng ◽  
Zhousuo Zhang ◽  
Seungchul Lee ◽  
Zhengjia He
Keyword(s):  
Independent Component Analysis ◽  
Evaluation Method ◽  
Component Analysis ◽  
Independent Component ◽  
Separation Performance ◽  
Complex Signals ◽  
Vibration Signals ◽  
Source Contribution ◽  
Machinery Condition Monitoring ◽  
Ica Algorithm

Extraction of effective information from measured vibration signals is a fundamental task for the machinery condition monitoring and fault diagnosis. As a typical blind source separation (BSS) method, independent component analysis (ICA) is known to be able to effectively extract the latent information in complex signals even when the mixing mode and sources are unknown. In this paper, we propose a novel approach to overcome two major drawbacks of the traditional ICA algorithm: lack of robustness and source contribution evaluation. The enhanced ICA algorithm is established to escalate the separation performance and robustness of ICA algorithm. This algorithm repeatedly separates the mixed signals multiple times with different initial parameters and evaluates the optimal separated components by the clustering evaluation method. Furthermore, the source contributions to the mixed signals can also be evaluated. The effectiveness of the proposed method is validated through the numerical simulation and experiment studies.

Denoising in Steel Structural Impulsive Vibration Signal Based on Independent Component Analysis

2011 ◽  
Vol 219-220 ◽  
pp. 1337-1341 ◽  
Author(s):  
Jun Hong Cao ◽  
Zhuo Bin Wei
Keyword(s):  
Independent Component Analysis ◽  
Steel Structure ◽  
Vibration Signal ◽  
Component Analysis ◽  
Independent Component ◽  
Structural Vibration ◽  
Vibration Signals ◽  
Structure Vibration

The analysis of structure vibration signals is influenced by noise mixed in the signals. Independent component analysis (ICA) method is introduced to denoise the vibration signals in this paper. The representative algorithms: FastICA and JADE are told in detail. The algorithms are applied to separate steel structural vibration signals. The denoising performances in impulsive vibration signals generated by steel structure demonstrate the effectiveness and good robustness of ICA method.

Research on Fault Diagnosis for Petrochemical Running Equipments Based on ICA

2011 ◽  
Vol 48-49 ◽  
pp. 950-953
Author(s):  
Zhi Gang Chen ◽  
Xiao Jiao Lian ◽  
Ming Zhou
Keyword(s):  
Fault Diagnosis ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
New Method ◽  
Signal Separation ◽  
Signal Extraction ◽  
Vibration Signals ◽  
Signal Preprocessing

For solving the difficulty of feature signal extraction from vibration signals, a new method based on Independent Component Analysis (ICA) is proposed to realize separation and filtering for multi-source vibration signals. Firstly, the principal and algorithm of ICA used to separate mixed signals is introduced. Secondly, application in signal separation and filtering with ICA is studied in diagnosis. In addition, imitation and field examples are given. The experiments show it is feasible to separate and extract feature signal from multi-source vibration signals and it is an effective method in signal preprocessing in fault diagnosis.

scholarly journals Acoustical Source Tracing Using Independent Component Analysis and Correlation Analysis

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Wei Cheng ◽  
Zhousuo Zhang ◽  
Jie Zhang ◽  
Jiantao Lu
Keyword(s):  
Fault Diagnosis ◽  
Correlation Analysis ◽  
Independent Component Analysis ◽  
Condition Monitoring ◽  
Component Analysis ◽  
Independent Component ◽  
Source Information ◽  
Independent Components ◽  
Source Tracing ◽  
Acoustical Signals

Acoustical signals from mechanical systems reveal the operating conditions of mechanical components and thus benefit for machinery condition monitoring and fault diagnosis. However, the acoustical signals directly measured by the sensors in essential are the mixed signals of all the sources, and normally it is very difficult to be used for source identification or operating feature extraction. Therefore, this paper studies the acoustical source tracing problem using independent component analysis (ICA) and identifies the sources using correlation analysis: the measured acoustical signals are separated into independent components by independent component analysis method, and thus all the independent information of all the sources is obtained; these independent components are identified based on the prior information of the sources and correlation analysis. Therefore, all the source information contained in the measured acoustical signals can be independently separated and traced, which can provide more purer source information for condition monitoring and fault diagnosis.

An Enhanced CICA Method and Its Application to Multistage Gearbox Low-frequency Fault Feature Extraction

2020 ◽  
Vol 13 (2) ◽  
pp. 285-294
Author(s):  
Junfa Leng ◽  
Penghui Shi ◽  
Shuangxi Jing ◽  
Chenxu Luo
Keyword(s):  
Feature Extraction ◽  
Independent Component Analysis ◽  
Low Frequency ◽  
Component Analysis ◽  
Independent Component ◽  
Vibration Signals ◽  
Gear Fault ◽  
Weak Fault ◽  
Constrained Independent Component Analysis ◽  
Ica Algorithm

Background: The vibration signals acquired from multistage gearbox’s slow-speed gear with localized fault may be directly mixed with source noise and measured noise. In addition, Constrained Independent Component Analysis (CICA) method has strong immunity to the measured noise but not to the source noise. These questions cause the difficulty for applying CICA method to directly extract lowfrequency and weak fault characteristic from the gear vibration signals with source noise. Methods: In order to extract the low-frequency and weak fault feature from the multistage gearbox, the source noise and measured noise are introduced into the independent component analysis (ICA) algorithm model, and then an enhanced Constrained Independent Component Analysis (CICA) method is proposed. The proposed method is implemented by combining the traditional Wavelet Transform (WT) with Constrained Independent Component Analysis (CICA). Results: In this method, the role of a supplementary step of WT before CICA analysis is explored to effectively reduce the influence of strong noise. Conclusion: Through the simulations and experiments, the results show that the proposed method can effectively decrease noise and enhance feature extraction effect of CICA method, and extract the desired gear fault feature, especially the low-frequency and weak fault feature.

scholarly journals Assessing material densities by vibration analysis and independent component analysis

2014 ◽  
Vol 9 (3) ◽  
Author(s):  
Hajar Razaghi ◽  
Reza Saatchi ◽  
Amaka Offiah ◽  
Derek Burke ◽  
Nick Bishop ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Physical Properties ◽  
Vibration Analysis ◽  
Electronic System ◽  
Component Analysis ◽  
Test Tube ◽  
Independent Component ◽  
Vibration Signals ◽  
Vibration Sensors ◽  
Multiple Materials

The aim of this study was to investigate vibration analysis and independent component analysis (ICA) to assess the density of multiple materials making up a single structure. Density is important as it reveals information about physical properties of materials. The density of a single material can be determined from the relationship between its mass and volume. However, when a structure consists of multiple materials, identification of their individual densities from the structure is complicated. Vibration analysis is a technique that reveals information about an object’s physical properties such as its density. The investigation was carried out using a plastic test tube filled separately with three liquids of known densities; water, Chloroform and Methanol. Vibration was inducted into the tube, through an electronic system that produced a single impact at a predefined location on the tube. The resulting vibration signals were recorded using two vibration sensors placed on the tube. A signal source separation technique called ICA was used to obtain the vibration effects of the liquid and the tube. The power spectral densities (PSD) of ICA extracted vibration signals were examined. The frequency of the largest peak in the PSD was related to the liquid’s density under test. The study indicated that vibration analysis may be effective in assessing materials’ densities in a structure that contains multiple materials, however a larger study is needed to explore the findings.

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