Mechanical Compound Fault Analysis Method Based on Shift Invariant Dictionary Learning and Improved FastICA Algorithm

For mechanical compound fault, it is of great significance to employ the vibration signal of a single-channel compound fault to analyze and realize the separation of multiple fault sources, which is essentially the problem of single-channel blind source separation. Shift invariant K-means singular value decomposition (shift invariant K-SVD) dictionary learning is suitable to extract the periodic and repeated fault features of a rotating machinery fault, hence in this article a single-channel compound fault analysis method is put forward which combines shift invariant K-SVD with improved fast independent component analysis (improved FastICA) algorithm. Firstly, based on single-channel compound fault signal, the shift invariant K-SVD algorithm can be used for learning multiple latent components that can be constructed as a virtual multi-channel signal. Then the improved FastICA algorithm is utilized to realize the separation of multiple fault source signals. With regard to the FastICA algorithm, the third-order convergence Newton iteration method is adopted to improve convergence speed. Moreover, in order to address the problem that FastICA is very sensitive to initialization, a steepest descent method can be applied. The experimental analysis of the compound fault of rolling bearing verifies that the presented method is effective to separate multiple fault source signals and the improved FastICA algorithm can increase convergence rate and overcome the problem of sensitivity to initialization.

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The Research and Simulation of Blind Source Separation Algorithm

International Journal of Advanced Pervasive and Ubiquitous Computing ◽

10.4018/ijapuc.2016070101 ◽

2016 ◽

Vol 8 (3) ◽

pp. 1-36

Author(s):

Tao Gao ◽

Jincan Li

Keyword(s):

Blind Source Separation ◽

Speech Enhancement ◽

Source Separation ◽

Separation Algorithm ◽

Original Source ◽

Fast Independent Component Analysis ◽

Fastica Algorithm ◽

Performance Evaluation Index ◽

The Mathematical Model ◽

Source Signals

When the original source signals and input channel are unknown, blind source separation (BSS) tries decomposing the mixed signals observed to obtain the original source signals, as seems mysterious. BSS has found many applications in biomedicine science, image processing, wireless communication and speech enhancement. In this paper the basic theory of blind source separation is described, which consists of the mathematical model, knowledge, performance evaluation index, and so on. And a further research on blind source separation algorithm has done when the number of source signals is more than (equal) the number of the signals observed, including the traditional ways of BSS—fast independent component analysis (FastICA) algorithm and equivariant adaptive separation via independence (EASI) algorithm, as well as the SOBI algorithm which is based on the joint diagonalization of matrices.

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Low-Element Image Restoration Based on an Out-of-Order Elimination Algorithm

Entropy ◽

10.3390/e21121192 ◽

2019 ◽

Vol 21 (12) ◽

pp. 1192 ◽

Cited By ~ 2

Author(s):

Yaqin Xie ◽

Jiayin Yu ◽

Xinwu Chen ◽

Qun Ding ◽

Erfu Wang

Keyword(s):

Image Restoration ◽

Blind Source Separation ◽

Source Separation ◽

Signal Recovery ◽

Separation Performance ◽

Underdetermined Blind Source Separation ◽

Elimination Algorithm ◽

Fast Independent Component Analysis ◽

Fastica Algorithm ◽

Source Signals

To reduce the consumption of receiving devices, a number of devices at the receiving end undergo low-element treatment (the number of devices at the receiving end is less than that at the transmitting ends). The underdetermined blind-source separation system is a classic low-element model at the receiving end. Blind signal extraction in an underdetermined system remains an ill-posed problem, as it is difficult to extract all the source signals. To realize fewer devices at the receiving end without information loss, this paper proposes an image restoration method for underdetermined blind-source separation based on an out-of-order elimination algorithm. Firstly, a chaotic system is used to perform hidden transmission of source signals, where the source signals can hardly be observed and confidentiality is guaranteed. Secondly, empirical mode decomposition is used to decompose and complement the missing observed signals, and the fast independent component analysis (FastICA) algorithm is used to obtain part of the source signals. Finally, all the source signals are successfully separated using the out-of-order elimination algorithm and the FastICA algorithm. The results show that the performance of the underdetermined blind separation algorithm is related to the configuration of the transceiver antenna. When the signal is 3 × 4 antenna configuration, the algorithm in this paper is superior to the comparison algorithm in signal recovery, and its separation performance is better for a lower degree of missing array elements. The end result is that the algorithms discussed in this paper can effectively and completely extract all the source signals.

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The Research and Simulation of Blind Source Separation Algorithm

Sensor Technology ◽

10.4018/978-1-7998-2454-1.ch025 ◽

2020 ◽

pp. 494-531

Author(s):

Tao Gao ◽

Jincan Li

Keyword(s):

Blind Source Separation ◽

Speech Enhancement ◽

Source Separation ◽

Separation Algorithm ◽

Original Source ◽

Fast Independent Component Analysis ◽

Fastica Algorithm ◽

Performance Evaluation Index ◽

The Mathematical Model ◽

Source Signals

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Implementation of a single-channel active noise control system with multiple reference sensors

Noise Control Engineering Journal ◽

10.3397/1/376830 ◽

2020 ◽

Vol 68 (5) ◽

pp. 358-366

Author(s):

H.E. Oh ◽

W.B. Jeong ◽

C. Hong

Keyword(s):

Single Channel ◽

Control Method ◽

Reference Signal ◽

Active Noise Control ◽

Multiple Source ◽

Weighted Sum ◽

Reference Signals ◽

Feedforward Controller ◽

Channel Control ◽

Source Signals

When multiple sources contribute competitively to the noise level, multi-channel control architecture is needed, leading to more cost and time for control computation. We, hence, are concerned with a single-channel control method with a single-reference signal obtained from a linear combination of the multiple source signals. First, we selected 3 source signal sensors for the reference signals and the error sensor, selected a proper actuator and designed the controllers: 3 cases of single-channel feedforward controllers with a single-reference signal respectively from the source signals, a multi-channel feedforward controller with the reference signals from the source signals, and the proposed controller with the reference signal from weighted sum of the source signals. The weighting factors and the filter coefficients of the controller were determined by the FxLMS algorithm. An experiment was then performed to confirm the effectiveness of the proposed method comparing the control performance with other methods for a tower air conditioner. The overall sound pressure level (SPL) detected by the error sensor is compared to evaluate their performance. The reduction in the overall SPL was obtained by 4.74 dB, 1.96 dB and 6.62 dB, respectively, when using each of the 3 reference signals. Also, the overall SPL was reduced by 7.12 dB when using the multi-reference controller and by 7.66 dB when using the proposed controller. Conclusively, under the multiple source contribution, a single-channel feed forward controller with the reference signal from a weighted sum of the source signals works well with lower cost than multi-channel feedforward controller.

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