scholarly journals A Novel Underdetermined Blind Source Separation Method and Its Application to Source Contribution Quantitative Estimation

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1413 ◽  
Author(s):  
Jiantao Lu ◽  
Wei Cheng ◽  
Yanyang Zi
Keyword(s):  
Blind Source Separation ◽  
Quantitative Estimation ◽  
Source Separation ◽  
Estimation Method ◽  
Least Square ◽  
Underdetermined Blind Source Separation ◽  
Source Contribution ◽  
Time Frequency ◽  
Source Contributions ◽  
Source Signals

To identify the major vibration and radiation noise, a source contribution quantitative estimation method is proposed based on underdetermined blind source separation. First, the single source points (SSPs) are identified by directly searching the identical normalized time-frequency vectors of mixed signals, which can improve the efficiency and accuracy in identifying SSPs. Then, the mixing matrix is obtained by hierarchical clustering, and source signals can also be recovered by the least square method. Second, the optimal combination coefficients between source signals and mixed signals can be calculated based on minimum redundant error energy. Therefore, mixed signals can be optimally linearly combined by source signals via the coefficients. Third, the energy elimination method is used to quantitatively estimate source contributions. Finally, the effectiveness of the proposed method is verified via numerical case studies and experiments with a cylindrical structure, and the results show that source signals can be effectively recovered, and source contributions can be quantitatively estimated by the proposed method.

scholarly journals A Novel Underdetermined Blind Source Separation Method Based on OPTICS and Subspace Projection

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1677
Author(s):  
Qingyi Wang ◽  
Yiqiong Zhang ◽  
Shuai Yin ◽  
Yuduo Wang ◽  
Genping Wu
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Estimation Method ◽  
Signal Recovery ◽  
Subspace Projection ◽  
Underdetermined Blind Source Separation ◽  
Matrix Estimation ◽  
Time Frequency ◽  
Source Signal ◽  
Mixing Matrix

In recent years, the problem of underdetermined blind source separation (UBSS) has become a research hotspot due to its practical potential. This paper presents a novel method to solve the problem of UBSS, which mainly includes the following three steps: Single source points (SSPs) are first screened out using the principal component analysis (PCA) approach, which is based on the statistical features of signal time-frequency (TF) points. Second, a mixing matrix estimation method is proposed that combines Ordering Points To Identify the Clustering Structure (OPTICS) with an improved potential function to directly detect the number of source signals, remove noise points, and accurately calculate the mixing matrix vector; it is independent of the input parameters and offers great accuracy and robustness. Finally, an improved subspace projection method is used for source signal recovery, and the upper limit for the number of active sources at each mixed signal is increased from m−1 to m. The unmixing process of the proposed algorithm is symmetrical to the actual signal mixing process, allowing it to accurately estimate the mixing matrix and perform well in noisy environments. When compared to previous methods, the source signal recovery accuracy is improved. The method’s effectiveness is demonstrated by both theoretical and experimental results.

scholarly journals Fault Feature Extraction for Reciprocating Compressors Based on Underdetermined Blind Source Separation

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1217
Author(s):  
Jindong Wang ◽  
Xin Chen ◽  
Haiyang Zhao ◽  
Yanyang Li ◽  
Zujian Liu
Keyword(s):  
Blind Source Separation ◽  
Hierarchical Clustering ◽  
Source Separation ◽  
Least Square Method ◽  
Least Square ◽  
Underdetermined Blind Source Separation ◽  
Matrix Estimation ◽  
Initial Cluster ◽  
Mixing Matrix ◽  
Source Signals

In practical engineering applications, the vibration signals collected by sensors often contain outliers, resulting in the separation accuracy of source signals from the observed signals being seriously affected. The mixing matrix estimation is crucial to the underdetermined blind source separation (UBSS), determining the accuracy level of the source signals recovery. Therefore, a two-stage clustering method is proposed by combining hierarchical clustering and K-means to improve the reliability of the estimated mixing matrix in this paper. The proposed method is used to solve the two major problems in the K-means algorithm: the random selection of initial cluster centers and the sensitivity of the algorithm to outliers. Firstly, the observed signals are clustered by hierarchical clustering to get the cluster centers. Secondly, the cosine distance is used to eliminate the outliers deviating from cluster centers. Then, the initial cluster centers are obtained by calculating the mean value of each remaining cluster. Finally, the mixing matrix is estimated with the improved K-means, and the sources are recovered using the least square method. Simulation and the reciprocating compressor fault experiments demonstrate the effectiveness of the proposed method.

scholarly journals An Underdetermined Blind Source Separation Method with Application to Modal Identification

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Gang Yu
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Modal Identification ◽  
Modal Parameters ◽  
Underdetermined Blind Source Separation ◽  
Structural Dynamic ◽  
Time Frequency ◽  
Modal Response ◽  
Experimental Validations ◽  
Straight Lines

In structural dynamic analysis, the blind source separation (BSS) technique has been accepted as one of the most effective ways for modal identification, in which how to extract the modal parameters using very limited sensors is a highly challenging task in this field. In this paper, we first review the drawbacks of the conventional BSS methods and then propose a novel underdetermined BSS method for addressing the modal identification with limited sensors. The proposed method is established on the clustering features of time-frequency (TF) transform of modal response signals. This study finds that the TF energy belonging to different monotone modals can cluster into distinct straight lines. Meanwhile, we provide the detailed theorem to explain the clustering features. Moreover, the TF coefficients of each modal are employed to reconstruct all monotone signals, which can benefit to individually identify the modal parameters. In experimental validations, two experimental validations demonstrate the effectiveness of the proposed method.

Blind Source Separation for Under-Determined Mixtures Based on Time-Frequency Analysis

2016 ◽  
Vol 693 ◽  
pp. 1350-1356 ◽  
Author(s):  
Hong Kun Li ◽  
Hong Yi Liu ◽  
Chang Bo He
Keyword(s):  
Blind Source Separation ◽  
Frequency Analysis ◽  
Source Separation ◽  
Vibration Signal ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Diagnosis And Classification ◽  
Source Signals

Blind source separation (BSS) is an effective method for the fault diagnosis and classification of mixture signals with multiple vibration sources. The traditional BSS algorithm is applicable to the number of observed signals is no less to the source signals. But BSS performance is limit for the under-determined condition that the number of observed signals is less than source signals. In this research, we provide an under-determined BSS method based on the advantage of time-frequency analysis and empirical mode decomposition (EMD). It is suitable for weak feature extraction and pattern recognition. Firstly, vibration signal is decomposed by using EMD. The number of source signals are estimated and the optimal observed signals are selected according to the EMD. Then, the vibration signal and the optimal observed signals are used to construct the multi-channel observed signals. In the end, BSS based on time-frequency analysis are used to the constructed signals. Gearbox signals are used to verify the effectiveness of this method.

scholarly journals Under-Determined Blind Source Separation

2021 ◽  
Author(s):  
Mehrdad Kafaiezadtehrani
Keyword(s):  
Blind Source Separation ◽  
Clustering Algorithm ◽  
Matching Pursuit ◽  
Source Separation ◽  
Least Square ◽  
Separation Problem ◽  
Second Stage ◽  
Mixing Matrix ◽  
Matching Pursuit Algorithm ◽  
Source Signals

The Under-determined Blind Source Separation problem aims at estimating N source signals, with only a given set of M known mixtures, where M < N. The problem is solved by a two-stage approach. The rst stage is the estimation of the unknown mixing matrix. The contributions made unravel a more precise and accurate tool which directly relates to the initialization of the clustering algorithm. Di erent schemes such as segmentation, correlation and least square curve tting are used to take advantage of the sparsity of the sources. A signi cant addition involves applying linear transforms to produce a higher sparse domain. Further, the second stage is the sparse source recovery using a Matching Pursuit algorithm. The contributions involve a Matching Pursuit algorithm with di

scholarly journals Consistent Independent Low-Rank Matrix Analysis for Determined Blind Source Separation

2020 ◽  
Author(s):  
Daichi Kitamura ◽  
Kohei Yatabe
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Matrix Analysis ◽  
Low Rank ◽  
Separation Performance ◽  
Time Frequency ◽  
Rank Matrix ◽  
Permutation Problem ◽  
Low Rank Matrix ◽  
Source Signals

Abstract Independent low-rank matrix analysis (ILRMA) is the state-of-the-art algorithm for blind source separation (BSS) in the determined situation (the number of microphones is greater than or equal to that of source signals). ILRMA achieves a great separation performance by modeling the power spectrograms of the source signals via the nonnegative matrix factorization (NMF). Such highly developed source model can effectively solve the permutation problem of the frequency-domain BSS, which should be the reason of the excellence of ILRMA. In this paper, we further improve the separation performance of ILRMA by additionally considering the general structure of spectrogram called consistency, and hence we call the proposed method Consistent ILRMA. Since a spectrogram is calculated by an overlapping window (and a window function induces spectral smearing called main- and side-lobes), the time-frequency bins depend on each other. In other words, the time-frequency components are related each other via the uncertainty principle. Such co-occurrence among the spectral components can be an assistant for solving the permutation problem, which has been demonstrated by a recent study. Based on these facts, we propose an algorithm for realizing Consistent ILRMA by slightly modifying the original algorithm. Its performance was extensively studied through the experiments performed with various window lengths and shift lengths. The results indicated several tendencies of the original and proposed ILRMA which include some topics have not discussed well in the literature. For example, the proposed Consistent ILRMA tends to outperform the original ILRMA when the window length is sufficiently long compared to the reverberation time of the mixing system.

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