Singular Spectrum Decomposition Research Articles

Package CovRegpy: Regularized covariance regression and forecasting in Python

Abstract This paper will outline the functionality available in the CovRegpy package which was written for actuarial practitioners, wealth managers, fund managers, and portfolio analysts in the language of Python 3.11. The objective is to develop a new class of covariance regression factor models for covariance forecasting, along with a library of portfolio allocation tools that integrate with this new covariance forecasting framework. The novelty is in two stages: the type of covariance regression model and factor extractions used to construct the covariates used in the covariance regression, along with a powerful portfolio allocation framework for dynamic multi-period asset investment management. The major contributions of package CovRegpy can be found on the GitHub repository for this library in the scripts: CovRegpy.py, CovRegpy_DCC.py, CovRegpy_RPP.py, CovRegpy_SSA.py, CovRegpy_SSD.py, and CovRegpy_X11.py. These six scripts contain implementations of software features including multivariate covariance time series models based on the regularized covariance regression (RCR) framework, dynamic conditional correlation (DCC) framework, risk premia parity (RPP) weighting functions, singular spectrum analysis (SSA), singular spectrum decomposition (SSD), and X11 decomposition framework, respectively. These techniques can be used sequentially or independently with other techniques to extract implicit factors to use them as covariates in the RCR framework to forecast covariance and correlation structures and finally apply portfolio weighting strategies based on the portfolio risk measures based on forecasted covariance assumptions. Explicit financial factors can be used in the covariance regression framework, implicit factors can be used in the traditional explicit market factor setting, and RPP techniques with long/short equity weighting strategies can be used in traditional covariance assumption frameworks. We examine, herein, two real-world case studies for actuarial practitioners. The first of these is a modification (demonstrating the regularization of covariance regression) of the original example from Hoff & Niu ((2012). Statistica Sinica, 22(2), 729–753) which modeled the covariance and correlative relationship that exists between forced expiratory volume (FEV) and age and FEV and height. We examine this within the context of making probabilistic predictions about mortality rates in patients with chronic obstructive pulmonary disease. The second case study is a more complete example using this package wherein we present a funded and unfunded UK pension example. The decomposition algorithm isolates high-, mid-, and low-frequency structures from FTSE 100 constituents over 20 years. These are used to forecast the forthcoming quarter’s covariance structure to weight the portfolio based on the RPP strategy. These fully funded pensions are compared against the performance of a fully unfunded pension using the FTSE 100 index performance as a proxy.

A novel method of rolling bearings fault diagnosis based on singular spectrum decomposition and optimized stochastic configuration network

Fault diagnosis of rolling bearings is essential for the safe operation of rotating machinery. However, in the production process, the rolling bearings have a complex working environment embedded with weak fault signals and a large number of interfering signals, which is a considerable challenge to automatically and accurately detect bearing fault type from the actual vibration signal. Therefore, a novel fault diagnosis scheme is proposed based on singular spectrum decomposition (SSD) and an optimized stochastic configuration network (SCN). Firstly, SSD is used to pre-process the original rolling bearings vibration signal to obtain several singular spectral components (SSCs) and the practical component is selected according to the maximum correlation coefficient for signal reconstruction. Furthermore, time domain and power spectrum entropy (PSE) features of the reconstructed signal are extracted to obtain a fault information-rich feature sets. In addition, the parameters of the SCN are optimized by marine predators algorithm (MPA) to enhance the learning ability and generalization performance of the SCN. Finally, the feature sets are input into MPA-SCN to achieve fault classification. Experimental results exhibit that the proposed method has higher accuracy in rolling bearings fault diagnosis compared with other methods, which provides a high-efficiency solution for rolling bearings fault diagnosis.

Generalized adaptive singular spectrum decomposition and its application in fault diagnosis of rotating machinery under varying speed

Rotating machinery fault signals often consist of multiple components with time varying frequencies under variable speed conditions. Spectral overlap exists among these components, making it difficult to independently separate the features of the components. Singular spectrum decomposition (SSD), a singular spectrum analysis-based signal decomposition method, has shown its great potential in suppressing background noise and extracting fault-related components in complex background noise environments. However, SSD is a frequency domain decomposition method with equivalent filtering characteristics, and it is susceptible to the mode mixing when processing signals with spectral overlap. Moreover, the choice of a key parameter in the iteration decomposition process of SSD, the embedding dimension, is determined using an empirical formula, which might cause suboptimal decomposition outcomes. To address these issues, this paper proposes a generalized adaptive singular spectrum decomposition (GASSD) method, which combines generalized demodulation with improved embedding dimension selection for SSD. GASSD incorporates SSD into the framework of adaptive generalized demodulation to separate specific frequency domain features. Firstly, for an effective generalized demodulation analysis, a region block synchronous ridge extraction method is proposed to accurately estimate the instantaneous frequency ridges from the time-frequency plane, which helps construct proper demodulation phase functions. Secondly, to achieve optimal analysis of SSD, a Gini moderation decomposition index is designed to improve the construction of the trajectory matrix by determining an appropriate embedding dimension. Finally, the reliability of the proposed method is demonstrated by analyzing wind turbine generator bearing fault signals and rotor rubbing fault signals.

Adaptive spectrum segmentation Ramanujan decomposition and its application to gear fault diagnosis

Ramanujan Fourier mode decomposition (RFMD) is a novel non-stationary signal decomposition method, which can decompose a complex signal into several components and extract the periodic characteristics of the signal. However, the mode generation method adopted by RFMD does not consider the physical meaning of the component signal, which makes over-decomposition when dealing with real-life gear signals with complex modulation characteristics, thus destroying the integrity of the signal sideband, increasing the difficulty of subsequent analysis, and even losing key fault information. The iterative envelope-segmentation algorithm combines the modulation characteristics of the local fault gear signal and divides the original signal into a limited number of dominant frequency bands containing the modulation region in the Fourier spectrum, thereby ensuring that the obtained frequency bands contain rich fault information. Based on the above algorithm, a new adaptive decomposition method is proposed in this paper, which is adaptive spectrum segmentation Ramanujan decomposition (ASSRD). ASSRD uses fault envelope harmonic noise ratio as the index to evaluate the fault information content of component signals and uses it to assist the iterative envelope-segmentation algorithm to complete the adaptive segmentation of the Fourier spectrum. Finally, based on the segmentation result, the inverse RFT reconstruction of each frequency band is performed. Thus, the signal is decomposed into a finite number of component signals containing rich fault information. In addition, through the experiment on the gear simulation signal and the measured crack fault gear signal, the ASSRD method is compared with the original RFMD method and the existing ensemble empirical mode decomposition (EMD), variational mode decomposition, empirical wavelet transform, and singular spectrum decomposition method, verifying the feasibility and superiority of ASSRD in gear fault diagnosis. Besides, a comparative experiment based on compound faults diagnosis is carried out, in which ensemble EMD, Fourier decomposition method, empirical wavelet transform, and sparse decomposition are involved. The results show that the proposed method can extract the local fault information in the gear signal more effectively, and the performance is better than the comparison method.

A multi-factor forecasting model for carbon emissions based on decomposition and swarm intelligence optimization

With the growth of national economy, carbon emissions are increasing day by day, which has caused a series of adverse effects on people’s production, life and environment. Therefore, it is very important to establish an accurate carbon emissions forecasting system for developing a low-carbon economy. Aiming at the problems of many influencing factors and randomness of carbon emissions, a multi-factor forecasting model for carbon emissions with generalized phase permutation entropy (GPPE), singular spectrum decomposition (SSD), bidirectional long short-term memory (BiLSTM) optimized by artificial tasmanian devil optimizer (TDO), named GPPE-SSD-TDO-BiLSTM, is proposed. Firstly, Pearson correlation coefficient method is used to analyze the main influencing factors of carbon emissions. Secondly, carbon emissions is decomposed by GPPE-SSD to obtain different singular spectrum components (SSCs). Finally, each component and influencing factors are brought into the TDO-BiLSTM forecasting model, and each forecasting result is reconstructed to obtain the final forecasting result. To verify the validity of the proposed model, carbon emissions of China and India in recent five years are used as experimental data, compares eight different forecasting models, and evaluates the forecasting models with six evaluation indexes. Taking China’s carbon emissions as an example, the MAE, MAPE, RMSE, R2 and STD of the proposed model can reach 0.1593, 0.5226, 0.2131, 0.9973 and 3.1056 respectively, which shows that its forecasting effect is better than that of other comparative models.

A reference free non-negative adaptive learning system for health care monitoring and adaptive physiological artifact elimination in brain waves

Electroencephalogram (EEG), also referred to as brain wave (BW), is a physiological phenomenon that depicts how the human brain functions. Brain wave analysis is fundamental in applications like brain-computer interference (BCI), beamforming, sleep analysis, epilepsy detection, and emotion recognition. In real-time applications, the brain wave encounters many physiological and non-physiological artifacts during acquisition. Due to this phenomenon, the analysis method is complicated and obscures the brain wave’s tiny features. This study proposes an intelligent signal enhancement unit (SEU) for processing EEG signals to enable decision-making under certainty. The proposed SEU enables healthcare professionals to analyze high-resolution brain wave components for various applications. A new singular spectrum decomposition (SSD) based on score reconstruction (score RC) is used in the first phase of the SEU to generate the artifact nature, which is then used as a reference signal in the adaptive artifact cancellation (AAC) method. The SSD performs the embedding, decomposition, grouping, and reconstruction procedures to provide the reference signal. A modified Logarithmic Non-Negative Adaptive Learning Algorithm (MLNNAL) is employed in the second stage of the SEU to improve the EEG signal. With the help of this proposed adaptive learning, a system with lower computing complexity which is stable and has non-negative weights can be realized. The adaptive learning algorithm’s weight recursion continually reweights the weight vector for each iteration to eliminate artifacts from contaminated brain waves. Excess mean square error (EMSE), signal to noise ratio improvement (SNRI) and computational cost of the adaptive learning algorithm are used to evaluate how the proposed SEU performs.

A new hybrid prediction model of COVID-19 daily new case data

With the emergence of new mutant corona virus disease 2019 (COVID-19) strains such as Delta and Omicron, the number of infected people in various countries has reached a new high. Accurate prediction of the number of infected people is of far-reaching sig Nificance to epidemiological prevention in all countries of the world. In order to improve the prediction accuracy of COVID-19 daily new case data, a new hybrid prediction model of COVID-19 is proposed, which consists of four modules: decomposition, complexity judgment, prediction and error correction. Firstly, singular spectrum decomposition is used to decompose the COVID-19 data into singular spectrum components (SSC). Secondly, the complexity judgment is innovatively divided into high-complexity SSC and low-complexity SSC by neural network estimation time entropy. Thirdly, an improved LSSVM by GODLIKE optimization algorithm, named GLSSVM, is proposed to improve its prediction accuracy. Then, each low-complexity SSC is predicted by ARIMA, and each high-complexity SSC is predicted by GLSSVM, and the prediction error of each high-complexity SSC is predicted by GLSSVM. Finally, the predicted results are combined and reconstructed. Simulation experiments in Japan, Germany and Russia show that the proposed model has the highest prediction accuracy and the lowest prediction error. Diebold Mariano (DM) test is introduced to evaluate the model comprehensively. Taking Japan as an example, compared with ARIMA prediction model, the RMSE, average error and MAPE of the proposed model are reduced by 93.17%, 91.42% and 81.20% respectively.

Research on Ultra-Wideband Radar Life Detection Algorithm Based on SE and SSD

Using pulsed Ultra-Wideband Radar non-contact detection technology, when detecting vital sign signals, the weak vital sign echo signals are often covered by various noises, resulting in the inability to identify and locate human targets. To solve this problem, an Ultra-Wideband Radar life detection algorithm based on the combination of sample entropy and singular spectrum decomposition is proposed. The human target position information is determined according to the SE value of the radar received pulse. In addition, the respiration and heart rate information were obtained using the SSD method. 15 groups of experiments were carried out, and the echo radar signals of 5 volunteers at 3 distances were collected respectively. The measured data analysis results show that the proposed algorithm can accurately and effectively extract the distance and vital signs information of human targets and has extensive research value and application prospects.

Rolling bearing faults identification based on multiscale singular value

To solve the vulnerability of singular value denoising to the effective number of singular value orders and influence of Hankel matrix structure, the paper has proposed a multiscale singular value denoising algorithm (M-SVD). Meanwhile, in concerning that when a fault of bearing occurs, the fault feature information contained in vibration signal tends to be weak and complex, the M-SVD method is blended with variational mode decomposition (VMD) algorithm in identification. Firstly, when bearing faults occur, raw vibration acceleration signals are decomposed using the superiority of VMD algorithm for non-stationary signal; in addition, considering that with the larger difference between singular values of signal, feature information will be more obvious, the method takes advantage of sensibility of variance to signal intensity and proposes to constitute Hankel matrix based on variance feature of signal. During the construction of Hankel matrix, the method to determine effective length of variance sequence (optimal scale) has been brought forward by sample entropy which is able to precisely measure the regularity of signal and detect the weak change of signal. Furthermore, component signals are denoised in line with reconstructed Hankel matrix and the spectrum of signals denoised are selected to describe fault characteristic information and judge the failure types. For the sake of verification of the accuracy and effectiveness of proposed method, the paper has analyzed and verified the data of rolling bearing in 3 different failure types and 2 different installation positions of accelerometers. The comparative analysis with classical singular value difference spectrum (SVDS) and singular value median decomposition (SVMD) denoising has further proved that presented VMD combined M-SVD method can effectively restrain noise while retaining fault characteristic information of signal. Therefore, the identification for rolling bearing fault types can be implemented, accurately.

Hybrid quantum singular spectrum decomposition for time series analysis

Classical data analysis requires computational efforts that become intractable in the age of Big Data. An essential task in time series analysis is the extraction of physically meaningful information from a noisy time series. One algorithm devised for this very purpose is singular spectrum decomposition (SSD), an adaptive method that allows for the extraction of narrow-banded components from non-stationary and non-linear time series. The main computational bottleneck of this algorithm is the singular value decomposition (SVD). Quantum computing could facilitate a speedup in this domain through superior scaling laws. We propose quantum SSD by assigning the SVD subroutine to a quantum computer. The viability for implementation and performance of this hybrid algorithm on a near term hybrid quantum computer is investigated. In this work, we show that by employing randomized SVD, we can impose a qubit limit on one of the circuits to improve scalibility. Using this, we efficiently perform quantum SSD on simulations of local field potentials recorded in brain tissue, as well as GW150914, the first detected gravitational wave event.

A combined prediction model based on secondary decomposition and intelligence optimization for carbon emission

Accurate prediction of carbon emission is critical for the development of low-carbon economy. However, most carbon emission prediction studies use a single model with low prediction accuracy, and do not consider the instability of carbon emission. Therefore, this paper proposes a combined prediction model of carbon emission. Firstly, the original data is decomposed by singular spectrum decomposition to obtain a limited amount of singular spectrum components. Secondly, high complexity components are secondarily decomposed by variational mode decomposition. Then, chameleon swarm algorithm and carnivorous plant algorithm are used to train the regularization coefficients and kernel parameters of kernel extreme learning machine and least squares support vector machine respectively, and the trained model is used to predict the decomposition components. Finally, induced ordered weighted averaging operator is used to calculate the weight of single model, and error correction is introduced to further promote the prediction accuracy. The carbon emission data of China and the United States is used to make a prediction experiment. The results indicate that the proposed model is superior to other comparative models in different indexes, which provides a new idea for carbon emission prediction.

Ultra-Short-Term Forecasting Method for Photovoltaic Power Based on Singular Spectrum Decomposition and Double Attention Mechanism

Ultra-Short-Term Forecasting Method for Photovoltaic Power Based on Singular Spectrum Decomposition and Double Attention Mechanism

Tensor Singular Spectrum Decomposition: Multisensor Denoising Algorithm and Application

Realising multi-sensor signal fusion and weak feature adaptive extraction is a challenging task. Therefore, a new algorithm called tensor singular spectrum decomposition is proposed in this study for the adaptive decomposition of multi-sensor time series. Traditional tensor decomposition algorithms, such as CP, HOSVD and Tucker decomposition, are derived from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -mode product. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -mode product essentially uses the idea of matrices to deal with tensors given that it defines the multiplication between matrix and higher-order tensor, thereby creating problems of non-pseudodiagonal core tensor and nonunique decomposition results in traditional tensor decomposition algorithms. To this end, decomposition of the original tensor signal and reconstruction of multi-sensor component signals are realised in this study by combining the trajectory tensor construction, superposition of the Gaussian function spectral model, adaptive iterative optimisation of embedding dimension and diagonal average method on the basis of the principle of tensor–tensor order-preserving multiplication. The proposed algorithm inherits the perfect mathematical theory and excellent properties of matrix singular value decomposition in processing single-sensor signals whilst retaining the inherent structure and coupling relationship between multi-sensor data and realising the organic fusion and adaptive decomposition of multi-sensor signals. The analysis results of simulation, experimental and engineering signals showed that the proposed method can effectively extract weak fault quantification features hidden in original multi-sensor signals compared with existing methods.

Impulsive feature extraction with improved singular spectrum decomposition and sparsity-closing morphological analysis

The singular spectrum decomposition (SSD) is an effective signal denoising tool and has been attracted much attention in fault diagnosis. However, the filtering effect and calculation efficiency of SSD are seriously affected by the embedding dimension of trajectory matrix. To overcome these disadvantages, the improved SSD (ISSD) is proposed in this paper. A length factor is designed to optimize the construction of trajectory matrix, which considers the fault information in both time-domain and frequency-domain. A series of analysis, including impulse response analysis and multi-components signal decomposition, demonstrate the ISSD lifts the performance of SSD. After that, a new indicator, named singular Gini index, is applied to select the optimal singular spectrum components (SSCs) decomposed by the ISSD. To further supplement the impulses extraction effect of the ISSD, the sparsity operation is improved by combing the morphological analysis to process the vibration and sound signals. The sparsity factor is updated in each iteration and the structure element in morphological analysis is determined adaptively. Benefiting from the virtues of ISSD and sparsity analysis, the fault impulses in the processed signal are more prominent. Finally, according to the information of bearing characteristic frequencies in the spectrum, the fault type of bearing is determined. The reliability and feasibility of the proposed method is identified by analyzing the different simulation and experimental cases.

Bridge modal identification based on successive variational mode decomposition using a moving test vehicle

Bridge modal identification using an instrumented test vehicle as a moving sensor is promising but challenging. A key factor is to extract bridge dynamic components from vehicle responses measured when the bridge is operating. A new method based on an advanced adaptive signal decomposition technique, the successive variational mode decomposition (SVMD), has been developed to estimate the bridge modal parameters from the dynamic responses of a passing test vehicle. When bridge-related dynamic components are extracted from the decomposition, the natural excitation technique and/or random-decrement technique based fitting methods are used to estimate the modal frequencies and damping ratios of the bridge. Effects of measurement noise, moving speed and vehicle properties on the decomposition are investigated numerically. The superiority of SVMD in the decomposition is verified by comparing to another adaptive decomposition technique, the singular spectrum decomposition. The results of the proposed method confirm that the bridge modal frequencies can be identified from bridge related components with high accuracy, while damping ratio is more sensitive to the random operational load. Finally, the feasibility of the proposed method for bridge monitoring using a moving test vehicle is further verified by an in-situ experimental test on a cable-stayed bridge. The components related to the bridge dynamic responses are successfully extracted from vehicle responses.

Rolling bearing fault feature extraction via improved SSD and a singular-value energy autocorrelation coefficient spectrum

It is usually difficult to extract weak fault features from rolling bearing vibration signals under noise pollution. To address this problem, a fault feature extraction approach for rolling bearings using improved singular spectrum decomposition (SSD) and a singular-value energy autocorrelation coefficient spectrum (SVEACS) is proposed. Firstly, to facilitate the determination of the optimal modal parameters in the SSD algorithm, the number of SSD layers is adaptively determined using an improved SSD algorithm based on permutation entropy. Then, the optimal modal components are selected, and the proposed SVEACS is used to determine the order of singular-value noise reduction. Finally, envelope analysis is used to extract the accurate shock characteristics of the denoised signal. The results of the experiments on simulated and real signals indicate that the proposed method can effectively extract the weak characteristics of the vibration signal under strong noise, and accurately diagnose the fault of a rolling bearing.

Singular Spectrum Analysis of Tremorograms for Human Neuromotor Reaction Estimation

Singular spectrum analysis (SSA) is a method of time series analysis and is used in various fields, including medicine. A tremorogram is a biological signal that allows evaluation of a person’s neuromotor reactions in order to infer the state of the motor parts of the central nervous system (CNS). A tremorogram has a complex structure, and its analysis requires the use of advanced methods of signal processing and intelligent analysis. The paper’s novelty lies in the application of the SSA method to extract diagnostically significant features from tremorograms with subsequent evaluation of the state of the motor parts of the CNS. The article presents the application of a method of singular spectrum decomposition, comparison of known variants of classification, and grouping of principal components for determining the components of the tremorogram corresponding to the trend, periodic components, and noise. After analyzing the results of the SSA of tremorograms, we proposed a new algorithm of grouping based on the analysis of singular values of the trajectory matrix. An example of applying the SSA method to the analysis of tremorograms is shown. Comparison of known clustering methods and the proposed algorithm showed that there is a reasonable correspondence between the proposed algorithm and the traditional methods of classification and pairing in the set of periodic components.

An improved snow depth retrieval method with adaptive noise reduction for GPS/GLONASS/Galileo/BDS multi-frequency signals

The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique is an effective method of monitoring snow depth. The detrended signal-to-noise ratio (dSNR) series is analyzed by a Lomb–Scargle periodogram (LSP) to extract the characteristic frequency, which can be converted to the snow depth. However, the dSNR data are greatly affected by noise in the observation environment, which leads to an abnormal characteristic frequency and low accuracy of snow depth retrieval. In order to reduce the influence of noise and to ensure the correct extraction of the characteristic frequency, we present an improved adaptive retrieval method for the multi-constellation retrieval scenario. First, the dSNR sequences are decomposed adaptively into several singular spectrum components (SSCs) with different frequency scales by singular spectrum decomposition. Then, the corresponding SSCs are selected, according to the empirical scope of snow depth, to reconstruct the ‘pure’ dSNR series. Finally, the reconstructed signals are analyzed by LSP to derive the characteristic frequency, in order to obtain the snow depth. Multi-GNSS observations of site SG27 and site P351 from the plate boundary observation network in a representative period from winter 2019 to spring 2020 were used to validate the proposed method. The snow depths were estimated from individual signals, individual constellations and multi-GNSS combination using both the traditional and improved methods. The experimental results show that compared with the traditional method, the snow depth trend of the improved method is more consistent with the measured snow depth trend, especially in the early stage of snowfall. Furthermore, the proposed method shows a universal applicability to various signals of GPS, GLONASS, Galileo and BDS and the retrieval accuracy of all signals is improved to different degrees. When using multi-GNSS combination signals, the mean bias and root mean square error (RMSE) of multi-GNSS snow depth retrieval at site SG27 are improved from 4.6 and 6.2 cm to 4.2 and 5.4 cm, respectively. The mean bias and RMSE at site P351 are improved from 10.5 and 12.4 cm to 9.5 and 11.5 cm, respectively.

A boundary division guiding synchrosqueezed wave packet transform method for rolling bearing fault diagnosis

Synchrosqueezed wave packet transform (SSWPT) can effectively reconstruct the band-limited components of the signal by inputting the specific reconstructed boundaries, and it provides an alternative bearing fault diagnosis method. However, the selection of reconstructed boundaries can significantly affect the fault feature extraction performance of SSWPT. Accordingly, this paper presents a boundary division guiding SSWPT (BD-SSWPT) method. In this method, an adaptive boundary division method is developed to effectively determine the reconstructed boundaries of SSWPT. Firstly, the marginal spectrum of SSWPT, more robust to noise than the Fourier spectrum, is defined for the scale-space division to obtain the initial boundaries. Secondly, the inverse transform of SSWPT is conducted based on the initial boundaries to obtain the initial reconstructed components. Thirdly, a boundary redefinition scheme, composed of clustering and combination, is conducted to redefine the boundaries. Finally, the potential components are extracted by the inverse transform of SSWPT based on the redefined boundaries. The validity of BD-SSWPT is verified by simulated and experimental analysis, and the superiority of BD-SSWPT is highlighted through comparison with singular spectrum decomposition (SSD) and an adaptive parameter optimized variational mode decomposition (AVMD). The results demonstrate that BD-SSWPT identifies more significant fault features and has higher computational efficiency than SSD and AVMD.

A Minimum Mutual Information Optimized SSD and Application In Bearing Fault Detection

For purpose of efficiently extracting the fault detailed characteristics of rolling bearing vibration signals in the midst of heavy noise interference, the author combines the mutual information theory and proposes a singular spectrum decomposition effective component discrimination method based on the minimum mutual information criterion. First, the SSD algorithm is used to decompose the original vibration signal into a number of SSC; then the mutual information value between the original signal and each singular spectrum component is calculated separately, based on the nature of the bearing fault vibration signal and the mutual information theory for derivation and analysis, and the selection is determined the singular spectrum component with the smallest mutual information value among the original signal and each component is taken as the best component; finally, the best component selected based on the minimum mutual information value criterion is analysed by envelope spectrum to extract the fault characteristics of rolling bearing. Through the analysis of bearing fault simulation signal and the comparison with other indicators, it is shown that this set of experiments significantly extracts the characteristics of the vibration signals emitted by rolling bearings due to faults in noisy environments, thus revealing promising applications and development prospects.