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.

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.

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.

A source contribution quantitative calculation method for mechanical systems based on the simplified independent component analysis with reference

2016 ◽  
Vol 230 (18) ◽  
pp. 3222-3240
Author(s):  
Jie Zhang ◽  
Zhousuo Zhang ◽  
Wei Cheng ◽  
Guanwen Zhu ◽  
Zhengjia He
Keyword(s):  
Independent Component Analysis ◽  
Prior Knowledge ◽  
Complex Structure ◽  
Component Analysis ◽  
Independent Component ◽  
Vibration Measurement ◽  
Contrast Function ◽  
Source Contribution ◽  
Quantitative Calculation ◽  
Reference Signals

The quantitative calculation of the source contribution is very important and critical for the identification of the main vibration sources and the reduction of vibration and noise in submarine. It is difficult to calculate the source contribution because of the submarine’s complex structure and the large amount of vibration sources. As a typical blind source separation method, independent component analysis (ICA) has recently been proved to be an effective method to solve the source identification problem in which the source signals and mixing models are unknown. However, the outcomes of the ICA algorithm are affected by random sampling and random initialization of variables. In our study, the prior knowledge of the vibration sources can be obtained through the vibration measurement of submarine. Obviously, information in addition to mixed signals from sensors can lead to a more accurate separation. Therefore the contrast function of ICA can be enhanced by the reference signals obtained by the prior knowledge. In this paper, a closeness measurement between the independent components and the reference signals obtained by the prior knowledge is introduced, and the closeness measurement is constructed to have the same optimization direction with the traditional contrast function: negentropy. The closeness measurement is used to enhance the contrast function and then the enhanced contrast function is optimized by means of the Newton iteration and the deflation approach. Thus the simplified independent component analysis with reference (ICA-R) algorithm is obtained. After that a method to quantitatively calculate the source contribution is proposed based on the outcomes of the simplified ICA-R. Finally, the effectiveness of the proposed method is verified by the numerical simulation studies. The performance offered by the proposed method is also investigated by the experiment: it appear as a very appealing tool for the quantitative calculation of the source contribution.

scholarly journals Identification of Automotive Seat Rattle Noise Using an Independent Component Analysis-Based Coherence Analysis Technique

2020 ◽  
Vol 10 (20) ◽  
pp. 7027
Author(s):  
Kookhyun Yoo ◽  
Un-Chang Jeong
Keyword(s):  
Independent Component Analysis ◽  
Evaluation Method ◽  
Signal Amplitude ◽  
Component Analysis ◽  
Independent Component ◽  
Coherence Analysis ◽  
Evaluation Technique ◽  
Noise Sources ◽  
Original Source ◽  
Analysis Technique

This study proposed a contribution evaluation through the independent component analysis (ICA) method. The necessity of applying ICA to the evaluation of contribution was investigated through numerical simulation. Moreover, the estimation of the number of input sources, the labeling of signals, and the restoration of the signal amplitude were considered to perform the ICA-based coherence evaluation. The contribution evaluation was performed using the coherence evaluation method and by applying the established ICA-based coherence evaluation method to the seat rattle noise of the vehicle. According to the result of the evaluation, with the coherence evaluation technique it was difficult to calculate the contribution in identifying noise sources that overlap in both spatially and in frequency, because it was challenging to distinguish between the two measured signals. By contrast, the ICA-based coherence evaluation was able to restore the original source and investigate the contribution.

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.

Study of the synthesis mechanism of 4-amino-3,5-dimethyl pyrazole by fibre optic in-line FT-IR spectroscopy combined with independent component analysis

2014 ◽  
Vol 6 (12) ◽  
pp. 4305-4311 ◽  
Author(s):  
Jiguang Li ◽  
Jun Gao ◽  
Hua Li ◽  
Xiaofeng Yang ◽  
Yu Liu
Keyword(s):  
Ir Spectroscopy ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fibre Optic ◽  
Synthesis Mechanism ◽  
Ft Ir ◽  
Ica Algorithm ◽  
Ft Ir Spectroscopy

The synthesis mechanism of 4-amino-3,5-dimethyl pyrazole was investigated using in-line FT-IR spectroscopy combined with a Fast-ICA algorithm.

scholarly journals Detection of Indeterminacies in Corrected ECG Signals Using Parameterized Multidimensional Independent Component Analysis

2009 ◽  
Vol 10 (2) ◽  
pp. 85-115 ◽  
Author(s):  
M. P. S. Chawla
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Complex Signals ◽  
Data Set ◽  
Noise Component ◽  
Ecg Signals ◽  
Independent Components ◽  
Statistical Measures ◽  
Ecg Data

Independent component analysis (ICA) is a new technique suitable for separating independent components from electrocardiogram (ECG) complex signals. The basic idea of using multidimensional independent component analysis (MICA) is to find stable higher dimensional source signal subspaces and to decompose each rotation into elementary rotations within all two-dimensional planes spanned by the coordinate axes useful for diagnostic information of heart. In this paper, ability of ICA for parameterization of ECG signals was felt to reduce the amount of redundant ECG data. This work aims at finding an independent subspace analysis (ISA) model for ECG analysis that allows applicability to any random vectors available in an ECG data set. For the common standards for electrocardiography (CSE) based ECG data sets, joint approximate diagonalization of eigen matrices (Jade) algorithm is used to find smaller subspaces. The extracted independent components are further cleaned by statistical measures. In this study, it is also observed that the value of kurtosis coefficients for the independent components, which represents the noise component, can be further reduced using parameterized multidimensional ICA (PMICA) technique. The indeterminacies if available in the ECG data are to be analysed also using modified version of Jade algorithm to PMICA and parameterized standard ICA (PsICA) for comparative studies. The indeterminacies if available in the ECG data are reduced in PMICA better in comparison to the analysis done using PsICA. The simulation results obtained indicate that ICA definitely improves signal–noise ratio (SNR) like the other higher order digital filtering methods like Kalman, Butterworth etc. with minimum reconstruction errors. Here, it is also confirmed that re-parameterization of the standard ICA model results into a ‘component model’ using MICA technique, which is geometric in spirit and free of indeterminacies existing in sICA model.

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