Signal Blind Source Separation Research Articles

Application of Lock-in Amplifier Technique in Signal Blind Source Separation

Abstract In the realm of signal processing, frequency error stands as a critical challenge that cannot be overlooked, particularly in scenarios characterized by noise disturbances and constantly changing environmental variables, as it significantly undermines the precision of waveform separation. Faced with a spectrum of frequency error phenomena stemming from hardware instability, such as clock drift, transmission distortion, and environmental changes, this paper proposes a waveform separation scheme centered around a core closed-loop control architecture aimed at addressing such challenges. Leveraging the high-performance STM32F407 microcontroller platform, the system harnesses the powerful algorithmic advantages of Fast Fourier Transform (FFT) to precisely locate and delineate the spectral characteristics of signals. Furthermore, we creatively integrate lock-in amplifier technology into the meticulously designed closed-loop control system, coupled with state-of-the-art zero-crossing detection algorithms for in-depth optimization. The research findings demonstrate outstanding signal-to-noise ratio performance within predefined target frequency bands for the separation device based on lock-in amplifier technology, showcasing not only remarkable resilience against interference but also the ability to accurately separate and reconstruct target signal components with high fidelity from complex and dynamic signal environments. This significantly enhances the robustness and accuracy of the entire system.

Rolling bearing compound fault diagnosis based on spatiotemporal intrinsic mode decomposition

Aiming at the vibration signal characteristics of multi-channel rolling bearing complex faults containing various shock components, a rolling bearing complex fault diagnosis model based on spatiotemporal intrinsic mode decomposition (STIMD) method and fast spectral kurtosis method was proposed. The spatiotemporal intrinsic mode decomposition method combines the signal atomic decomposition method with the idea of signal blind source separation. Through the fast independent component analysis and the nonlinear matching pursuit method of the established overcomplete dictionary base, various fault mode components are separated. The initial phase function selected based on the high kurtosis fault frequency band obtained by the fast spectral kurtosis method can better fit the bearing fault frequency domain characteristics, so that the spatiotemporal intrinsic mode decomposition method can more accurately separate various impact components in the vibration signal. The simulation model of bearing compound fault was established and the data collected from fault diagnosis experiment platform were used to verify that the STIMD method was effective in solving the problem of rolling bearing compound fault diagnosis. By analyzing the kurtosis changes under different signal noise ratio (SNR) conditions and comparing the simulation results with the fast independent component analysis method, it shows that the kurtosis index decomposed by the proposed method is more able to prove the existence of faults under the condition of low SNR, that is, the impact is completely covered by noise. Therefore, a spatiotemporal intrinsic mode decomposition method with fast spectral kurtosis optimization can solve the problem of blind source separation in the field of composite faults of multi-channel rolling bearings and realize composite fault diagnosis.

Convenient Online Approach to Multisource Partial Discharge Localization in Transformer

Partial discharge (PD) online monitoring is an essential part of transformer insulation condition based maintenance and prognostics and health management in industrial power system. In order to realize online localization of multiple PD sources conveniently, an approach that uses single-channel mixed acoustic emission (AE) signal is proposed in this article. First, the combined method based on the singular spectrum analysis and the independent component analysis is used to calculate the number of PD sources and realize the separation of mixed AE signals. Next, a difference selection algorithm is proposed to solve the problem that the inherent disorder of signal blind source separation makes the time difference of arrival technology difficult to localize the multiple PD sources directly. Then, the simulation signals and measured signals have verified that the signal separation effect of the proposed single-channel-based method is no less than that of the multichannel-based method. Finally, the proposed approach is applied to a transformer. The disordered separated signals were successfully grouped to realize multisource PD localization.

Blind Source Separation Based on Double-Mutant Butterfly Optimization Algorithm.

The conventional blind source separation independent component analysis method has the problem of low-separation performance. In addition, the basic butterfly optimization algorithm has the problem of insufficient search capability. In order to solve the above problems, an independent component analysis method based on the double-mutant butterfly optimization algorithm (DMBOA) is proposed in this paper. The proposed method employs the kurtosis of the signal as the objective function. By optimizing the objective function, blind source separation of the signals is realized. Based on the original butterfly optimization algorithm, DMBOA introduces dynamic transformation probability and population reconstruction mechanisms to coordinate global and local search, and when the optimization stagnates, the population is reconstructed to increase diversity and avoid falling into local optimization. The differential evolution operator is introduced to mutate at the global position update, and the sine cosine operator is introduced to mutate at the local position update, hence, enhancing the local search capability of the algorithm. To begin, 12 classical benchmark test problems were selected to evaluate the effectiveness of DMBOA. The results reveal that DMBOA outperformed the other benchmark algorithms. Following that, DMBOA was utilized for the blind source separation of mixed image and speech signals. The simulation results show that the DMBOA can realize the blind source separation of an observed signal successfully and achieve higher separation performance than the compared algorithms.

Fault diagnosis of rolling element bearing based on spatiotemporal intrinsic mode decomposition

In view of the problem that source signals cannot be effectively separated in the process of blind source separation of similar nonstationary nonlinear signals, a spatiotemporal intrinsic mode decomposition method was proposed for bearing fault diagnosis. Spatiotemporal intrinsic mode decomposition can separate source signals and construct Fourier basis dictionaries and nonlinear signal models. The fault components can be separated by using this method in fault diagnosis. The proper initial phase function is selected for blind source separation of signals and signal decomposition components are obtained. By simulation analysis, spatiotemporal intrinsic mode decomposition than the fast independent component analysis method can more intuitive clearly separate the signals of admixed with large modulation components of correlation coefficient, and through the impact of component kurtosis index to judge fault, inherent modal decomposition method better prove time restore original bearing vibration signals and fault impact. Through the analysis and comparison of experimental data, the spatiotemporal intrinsic mode decomposition method has a significant effect on the fault diagnosis and analysis of rolling bearing outer ring, inner ring, and can intuitively express the fault characteristic frequency and frequency doubling through the analysis of envelope spectrum. In processing the industrial data, the spatiotemporal intrinsic mode decomposition method can also find the frequency characteristics of inner ring fault more clearly and accurately. Therefore, the spatiotemporal intrinsic mode decomposition method can solve the problem of blind source separation and realize fault diagnosis in rolling bearing fault field.

Single Channel Blind Source Separation Under Deep Recurrent Neural Network

In wireless sensor networks, the signals received by sensors are usually complex nonlinear single-channel mixed signals. In practical applications, it is necessary to separate the useful signals from the complex nonlinear mixed signals. However, the traditional array signal blind source separation algorithms are difficult to separate the nonlinear signals effectively. Building upon the traditional recurrent neural network, we improved the network structure, and further proposed the three layers deep recurrent neural networks to realize single channel blind source separation of nonlinear mixed signals. The experiments and simulation were conducted to verify the performance of this method; the results showed that the mixed signals can be separated excellently and the correlation coefficient can be reached up to 99%. Thus, a new method was given for blind signal processing with artificial intelligence.

A General Nonstationary and Time-Varying Mixed Signal Blind Source Separation Method Based on Online Gaussian Process

Most nonstationary and time-varying mixed source separation algorithms are based on the model of instantaneous mixtures. However, the observation signal is a convolutional mixed source in reverberation environment, such as mobile voice received by indoor microphone arrays. In this paper, a time-varying convolution blind source separation (BSS) algorithm for nonstationary signals is proposed, which can separate both time-varying instantaneous mixtures and time-varying convolution mixtures. We employ the variational Bayesian (VB) inference method with Gaussian process (GP) prior for separating the nonstationary source frame by frame from the time-varying convolution signal, in which the prior information of the mixing matrix and the source signal are obtained by the Gaussian autoregressive method, and the posterior distributions of parameters (source signal and mixing matrix) are obtained by the VB learning. In the learning process, the learned parameters and hyperparameters are propagated to the next frame for VB inference as the prior which is combined with the likelihood function to get the posterior distribution. The experimental results show that the proposed algorithm is effective for separating time-varying mixed speech signals.

An Independent Analysis of the Spitzer/IRAC Phase Curves of WASP43 b

Abstract We present here a reanalysis of the Spitzer Space Telescope phase curves of the hot Jupiter WASP43 b, using the wavelet pixel-independent component analysis, a blind signal-source separation method. The data analyzed were recorded with the Infrared Array Camera and consisted of two visits at 3.6 μm, and one visit at 4.5 μm, each visit covering one transit and two eclipse events. To test the robustness of our technique we repeated the analysis on smaller portions of the phase curves, and by employing different instrument ramp models. Our reanalysis presents significant updates of the planetary parameters compared to those reported in the original phase curve study of WASP43 b. In particular, we found (1) higher nightside temperatures, (2) smaller hotspot offsets, (3) a greater consistency (∼1σ) between the two 3.6 μm visits, and (4) a greater similarity with the predictions of the atmospheric circulation models. Our parameter results are consistent within 1σ with those reported by a recent reanalysis of the same data sets. For each visit we studied the variation of the retrieved transit parameters as a function of various sets of stellar limb-darkening coefficients, finding significant degeneracy between the limb-darkening models and the analysis output. Furthermore, we performed the analysis of the single transit and eclipse events, and we examined the differences between these results with the ones obtained with the whole phase curve. Finally we provide a formula useful to optimize the trade-off between precision and duration of observations of transiting exoplanets.

Underdetermined blind source separation of adjacent satellite interference based on sparseness

The problem of underdetermined blind source separation of adjacent satellite interference is proposed in this paper. Density Clustering algorithm (DC-algorithm) presented in this article is different from traditional methods. Sparseness representation has been applied in underdetermined blind signal source separation. However, some difficulties have not been considered, such as the number of sources is unknown or the mixed matrix is ill-conditioned. In order to find out the number of the mixed signals, Short Time Fourier Transform (STFT) is employed to segment received mixtures. Then, we formulate the blind source signal as cluster problem. Furthermore, we construct Cost Function Pair (ρ, δ) and Decision Coordinate System by using density clustering. At the end of this paper, we discuss the performance of the proposed method and verify the novel method based on several simulations. We verify the proposed method on numerical experiments with real signal transmission, which demonstrates the validity of the proposed method.

Single-channel bearing vibration signal blind source separation method based on morphological filter and optimal matching pursuit (MP) algorithm

A novel method to solve the single-channel source separation problem is proposed, which is based on a morphological filter to remove noise and the optimal matching pursuit (MP) algorithm to create pseudo sources. The signal is first purified by the morphological filter. Then, the purified signal is decomposed by the MP method, the Gabor atoms of the MP are selected by genetic algorithm in order to be less time-consuming. The selected atoms and the purified signal are combined into multi-dimensional signals, and the underdetermined problem of single-channel source separation is solved. The singular value decomposition method is used to estimate the number of new constructed signals. Then, the fast independent component analysis algorithm is used to achieve the separation of the signals. The proposed method is applied to the separation of the simulated signals and the mixed faults of bearing, the results indicate that the method can well solve the single-channel signal separation problem.

Study on BSS Algorithm used on Fault Diagnosis of Gearbox

The gearbox is a complicated rotating machinery equipment, in order to realize the gearbox fault early detection and prevention, it is the key to carry out the online diagnosis. This paper used the adaptive variable step-length natural gradient blind source separation algorithm to realize the helicopter gearbox meshing vibration signal and fault vibration signal effective separation. Through the algorithm simulation, the accuracy of the algorithm gained the verification and the separation error trended to zero, which has higher separation precision. This algorithm can realize the complicated mechanical vibration signal blind source separation and fault diagnosis, which has a broad application prospect. DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.2260

Diagnosing the CO2 Air Conditioning Fault of Rescue Cabin Based on Underdetermined Blind Signal Source Separation

Diagnosing the CO2 Air Conditioning Fault of Rescue Cabin Based on Underdetermined Blind Signal Source Separation

Blind source separation of signals with known alphabets using /spl epsi/-approximation algorithms

We show that blind separation of signals in given alphabets can be formulated into a quadratic optimization problem with integer constraints. Then, efficient /spl epsi/-approximation algorithms are applied to directly estimate the transmitted signals. The proposed approach does not require any high order statistics. Moreover, the algorithms converge to an /spl epsi/ neighborhood of the global optimum with polynomial computational complexity. Simulations show that the algorithm achieves satisfactory performance using a short length of data.