Mechanical Parts Defect Detection Method Based on Blind Source Separation Algorithm

2014 ◽  
Vol 1044-1045 ◽  
pp. 805-807
Author(s):  
Jing Nie ◽  
Hong Lei Jing ◽  
Nian Zhang
Keyword(s):  
Blind Source Separation ◽  
Defect Detection ◽  
Detection Method ◽  
Source Separation ◽  
Signal Separation ◽  
Blind Signal Separation ◽  
Video Communications ◽  
Separation Algorithm ◽  
Mechanical Parts ◽  
Blind Signal

A blind source separation technique is widely used in voice, video, communications, medical, mechanical failure signal processing, and data mining, and many other fields. Such a broad application prospects, making the blind signal separation problems on continuously were widespread concern experts and scholars at home and abroad. This paper describes the meaning of blind source separation techniques, a detailed description of the application of this technique to detect defects in terms of mechanical parts, due to the blind source separation algorithm is a regular in the development of the theory, and how to better integrate its application in the field of diagnosis defective parts, will be an important issue in the future is worth further exploration.

A Note on Stone's Conjecture of Blind Signal Separation

2005 ◽  
Vol 17 (2) ◽  
pp. 321-330 ◽  
Author(s):  
Shengli Xie ◽  
Zhaoshui He ◽  
Yuli Fu
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Signal Separation ◽  
Blind Signal Separation ◽  
The Novel ◽  
Blind Signal ◽  
Simple Simulation ◽  
Novel Approaches

Stone's method is one of the novel approaches to the blind source separation (BSS) problem and is based on Stone's conjecture. However, this conjecture has not been proved. We present a simple simulation to demonstrate that Stone's conjecture is incorrect. We then modify Stone's conjecture and prove this modified conjecture as a theorem, which can be used a basis for BSS algorithms.

scholarly journals Blind Source Separation based on Whale Optimization Algorithm

2018 ◽  
Vol 173 ◽  
pp. 03052
Author(s):  
CHU Ding-li ◽  
CHEN Hong ◽  
CHEN Han-yi
Keyword(s):  
Particle Swarm Optimization ◽  
Blind Source Separation ◽  
Optimization Algorithm ◽  
Source Separation ◽  
Whale Optimization Algorithm ◽  
Signal Separation ◽  
Blind Signal Separation ◽  
Swarm Optimization ◽  
Blind Signal ◽  
Whale Optimization

Aiming at the problem of linear instantaneous aliasing in blind source separation, a new method of blind signal separation using whale optimization algorithm is proposed in this paper, which provides a new research idea and method for blind signal separation. The new method adopts the method of independent component analysis, optimizes the objective function by using the whale optimization algorithm, realizes the blind separation of instantaneous aliasing signals, and effectively avoids the problem of complex parameters and slow convergence rate of the particle swarm optimization algorithm. The simulation results show that the performance of whale optimization algorithm is better than that of particle swarm optimization for blind source separation, and it is effective for blind signal separation.

INDEPENDENT COMPONENT ANALYSIS (ICA) DAN SPARSE COMPONENT ANALYSIS (SCA) DALAM PEMISAHAN VOKAL DAN INSTRUMEN PADA SENI GEGUNTANGAN

2019 ◽  
Vol 8 (1) ◽  
pp. 105
Author(s):  
Angga Pramana Putra ◽  
Ni Wayan Wiantari ◽  
Putu Mira Novita Dewi ◽  
I Dewa Made Bayu Atmaja Darmawan
Keyword(s):  
Independent Component Analysis ◽  
Mean Square Error ◽  
Blind Source Separation ◽  
Component Analysis ◽  
Independent Component ◽  
Signal Separation ◽  
Blind Signal Separation ◽  
Mean Square ◽  
Blind Signal ◽  
Sparse Component Analysis

Geguntangan adalah pesantian dalam upacara keagamaan yang diiringi dengan gamelan. Indra  pendengaran manusia cenderung memiliki keterbatasan, yang menyebabkan tidak semua vokal yang  tercampur dengan gamelan bisa didengar jelas. Oleh karena itu diperlukan suatu sistem yang dapat digunakan untuk memisahkan vokal dengan gamelan pada geguntangan. Pemisahan sumber suara ini dikategorikan sebagai Blind Source Separation (BSS) atau disebut juga Blind Signal Separation yang  artinya sumber tidak dikenal. Algoritma yang digunakan untuk menangani BSS adalah algoritma Independent Component Analysis (ICA) dan Sparse Component Analysis (SCA) dengan berfokus  pada pemisahan sinyal suara pada file suara berformat *.wav. Algoritma SCA dan ICA digunakan  untuk proses pemisahan suara dengan parameter nilai yang digunakan adalah Mean Square Error (MSE) dan Signalto Interference Ratio(SIR). Dari hasil simulasi menunjukkan Hasil perhitungan MSE dan SIR dengan dengan menggunakan mixing matriks [0.3816, 0.8678], [0.8534, -0.5853] didapatkan untuk metode ICA nilai MSE sebesar 4.169380402433175 x 10-6 untuk instrumennya dan 2.884749383815846 x 10-5 untuk vokalnya dan didapatkan nilai SIR sebesar 53.79928479270223 untuk instrumennya dan 45.39891910741724 untuk vokalnya. Selanjutnya untuk metode SCA, nilai MSE sebesar 3.382207103335018 x 10-5 untuk instrumennya dan 3.099942460987607 x 10-5 untuk vokalnya dan didapatkan nilai SIR sebesar 44.707998026869014 untuk instrumennya dan 45.08646367168143 untuk vokalnya.

APPLICATION OF BLIND SIGNAL SEPARATION TECHNIQUES FOR DETECTION OF PHASE-SHIFTED RADIO SIGNALS

2021 ◽  
Author(s):  
Н.Ю. ЛИБЕРОВСКИЙ ◽  
Д.С. ЧИРОВ ◽  
Н.Д. ПЕТРОВ
Keyword(s):  
Radio Signal ◽  
Signal To Noise Ratio ◽  
Signal Separation ◽  
Blind Signal Separation ◽  
Signal To Noise ◽  
Separation Algorithm ◽  
Blind Signal ◽  
Radio Signals ◽  
Joint Cumulants ◽  
Mixing Matrix

Целью данной работы является исследование эффективности алгоритма слепого разделения сигналов (СРСв задаче обнаружения цифровых фазоманипулированных радиосигналов. Рассмотрены классические методы СРС и критерии независимости сигналов. Исследована модель алгоритма СРС, основанного на вычислении размешивающей матрицы, которая приводит совместные кумулянты второго и четвертого порядков к нулю. Для исключения тривиального решения накладываются дополнительные ограничения на дисперсии сигналов. Приводится система уравнений для нахождения коэффициентов размешивающей матрицы. Показан вид коэффициентов размешивающей матрицы, приводящей сигналы к некоррелированному виду. Доказана возможность аналитического решения уравнения, связанного с равенством совместного кумулянта четвертого порядка к нулю. По результатам моделирования алгоритма СРС показано, что предложенный алгоритм позволяет обеспечить прием ФМ-2 радиосигнала на фоне гауссовой помехи. Выигрыш в отношении сигнал-помеха составляет не менее 2 дБ. The purpose of this work is to study the effectiveness of the blind signal separation algorithm in the problem of detecting digital PSK radio signals. Classical methods of blind signal separation and criteria of signal independence are considered. A model of a blind signal separation algorithm based on the calculation of a mixing matrix that reduces the joint cumulants of the second and fourth orders to zero is investigated. To eliminate the trivial solution, additional restrictions are imposed on the signal variances. A system of equations for finding the coefficients of the mixing matrix is given. The view of the coefficients of the mixing matrix, which leads the signals to an uncorrelated form, is shown. The possibility of an analytical solution of the equation associated with the equality of the joint cumulant of the fourth order to zero is proved. Based on the results of the simulation of the blind signal separation algorithm, it is shown that the proposed algorithm allows receiving the PSK-2 radio signal against the background of Gaussian interference. The gain in the signal-to-noise ratio is at least 2 dB.

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