Signal Decomposition Algorithm Research Articles

A Parallel Denoising Model for Dual-Mass MEMS Gyroscope Based on PE-ITD and SA-ELM

In a bid to solve the gyroscope temperature drift problem, a parallel denoising model based on PE-ITD and SA-ELM has been proposed in this paper, wherein, Intrinsic time-scale decomposition (ITD) is an effective signal decomposition algorithm, Permutation entropy (PE) is a entropy to accurately determine the complexity of the signal, Extreme Learning machine (ELM) is a machine learning algorithm for predicting, and Simulated Annealing(SA) for finding the optimal parameter set. First, ITD is employed to decompose the output signal of the gyroscope and the PE can distinguish the decomposition results into noise-only component, mixed component and trend component. Secondly, the mixed component is filtered by Forward liner prediction (FLP), the denoised signal can be obtained after processing. Finally, the trend component is compensated by SA-ELM, through reconstruction, the compensation result can be obtained. As is shown in experimental results, the parallel model proposed in this paper is able to effectively eliminate the temperature error compared with the traditional serial model. Compared with the EMD analysis method, the ITD is superior to the EMD analysis method in terms of computational efficiency and instantaneous information. The proposed SA-ELM algorithm takes simulated annealing algorithm to find hidden layer neurons’ optimal number adaptively, which can improve the model’s accuracy and generalization. This paper demonstrates the superiority of this method.

Rolling Bearing Fault Diagnosis Based on SVDP-Based Kurtogram and Iterative Autocorrelation of Teager Energy Operator

The emergence of periodic impacts in the vibration signal is considered as an essential sign of rolling bearing faults. Therefore, how to distinguish the periodic impact component from the interference components (e.g., the harmonics and noise) in the raw vibration signal is critical for detecting bearing faults. The kurtogram technique plays an essential role in the automatic selection of sub-component signals containing fault information. However, two significant shortcomings reduce its ability to detect early weak transients: 1) the decomposition accuracy of the filters used in kurtogram, i.e., short-time Fourier transform (STFT) and binary filter banks, is deficiency and 2) the detection ability of kurtosis to cyclic impact is insufficient. A singular value decomposition packet (SVDP)-based kurtogram is proposed to improve the kurtogram technique. More specifically, a novel parameter-less signal decomposition algorithm, termed SVDP, is employed as the filter for sub-components extraction. The L-kurtosis indicator is then introduced to replace the kurtosis indicator to select the optimal sub-component from the SVDP processing results. Moreover, a fault signature highlighting technique named iterative autocorrelation of Teager energy operator (TEO-IAC) is presented, and the TEO-IAC spectrum is adopted to replace the Hilbert envelope spectrum to detect the fault characteristic frequencies of the optimal sub-component to determine the fault types of bearings. Finally, the presented fault diagnosis framework based on the SVDP-based kurtogram and TEO-IAC is compared with the original kurtogram, improved kurtogram and autogram in simulated and experimental signals analysis, which demonstrates its validity and superiority for extracting weak fault features of bearings.

IoT-Based Vibration Analytics of Electrical Machines

The aim of this paper is to propose an IoT-based model for real-time condition monitoring of electrical machines, which addresses the challenges of data storage and scalability. The proposed model is evolved with an experimental setup having two sets of dc motor coupled to ac generator and an IoT device to elucidate integrated monitoring and decision making. This IoT-based vibration analytic model uses an IoT2040 Gateway with custom Linux OS image built for acquisition and streaming of vibration signals. The Python target application acquires dc motors shaft vibration using vibration sensors and communicates the data as events to cloud through serial device driver interface. The IoT service running in cloud receives the data from multiple machines through lightweight RESTful HTTP and records the same which are retrievable for analysis and algorithm development in any platform. The retrieved data have been analyzed using the proposed statistical classification-based signal decomposition algorithm as well as time-frequency analysis to estimate the vibration thresholds of every machine connected to IoT cloud. Such estimated thresholds corresponding to different operating and environmental conditions maintained in cloud are used to build a repository of context specific solutions for machine conditions leading to improved maintenance decisions. The uniformity of threshold values obtained from IoT-based model in comparison with that of analysis carried out on the machines locally using myRIO for data acquisition ensures the integrity of the proposed statistical classification algorithm and reliability of the IoT model for condition monitoring with assured scalability.

Variational mode decomposition denoising combined with the Euclidean distance for diesel engine vibration signal

Variational mode decomposition (VMD) is a recently introduced adaptive signal decomposition algorithm with a solid theoretical foundation and good noise robustness compared with empirical mode decomposition (EMD). There is a lot of background noise in the vibration signal of diesel engine. To solve the problem, a denoising algorithm based on VMD and Euclidean Distance is proposed. Firstly, a multi-component, non-Gauss, and noisy simulation signal is established, and decomposed into a given number K of band-limited intrinsic mode functions by VMD. Then the Euclidean distance between the probability density function of each mode and that of the simulation signal are calculated. The signal is reconstructed using the relevant modes, which are selected on the basis of noticeable similarities between the probability density function of the simulation signal and that of each mode. Finally, the vibration signals of diesel engine connecting rod bearing faults are analyzed by the proposed method. The results show that compared with other denoising algorithms, the proposed method has better denoising effect, and the fault characteristics of vibration signals of diesel engine connecting rod bearings can be effectively enhanced.

Greedy Automatic Signal Decomposition and Its Application to Daily GPS Time Series

AbstractThe recognition of transient motion in terrestrial continuous Global Positioning System (GPS) time series implies the knowledge of certain time functions that we assume to be ever present in the time series. By assuming that the permanent time functions are the long‐term secular velocity of the Earth and the seasonal oscillations, we define the total remaining signal as transient motion. Here we adopt the multitransient as a versatile function for modeling transient motion over a range of time scales. We define the multitransient as the sum of two or more transient decaying functions with different characteristic time scales and identical onset times. We then demonstrate the greedy approach to fitting the time series by using a minimum number of multitransients (sparse functions) in addition to the permanent time functions in a linear regression. The Greedy Automatic Signal Decomposition algorithm decomposes the signal into three parts: (1) background seasonal motion, (2) secular and transient motion, and (3) a residual (noise). We describe the greedy algorithm with synthetic examples before demonstrating its application to time series of daily GPS solutions. The implementation of the multitransient allows for a more realistic plate‐trajectory model, whereby a full range of transient signal time scales, from short‐duration slow slip to longer‐duration processes such as postseismic slab accelerations or postseismic decay, can all be estimated with the same function. Since Greedy Automatic Signal Decomposition algorithm automatically estimates trend, its application to a GPS network allows for the common mode filter to be applied seamlessly.

An Enhanced Adaptive Phasor Power Oscillation Damping Approach With Latency Compensation for Modern Power Systems

In this study, a novel enhanced adaptive phasor power oscillation damping (EAPPOD) strategy is proposed, which is capable of compensating time-varying data transmission latencies between phasor measurement unit sites and the control center, and also mitigating the low-frequency oscillations (LFO) of interarea signals. The proposed method can handle general communication delay-related problems and fulfill LFO mitigation tasks in modern power systems, and in this study this method is integrated with the doubly fed induction generator (DFIG) rotor side control (RSC) scheme to achieve the control purpose. The control signal is produced for the purpose of minimizing the amplitude of the phasor component disaggregated from the measured signal, using a novel signal decomposition algorithm. It is then transmitted to the active power regulation scheme in the DFIG RSC structure to modulate the power reference value, so as to realize LFO mitigation. Improving upon the recently established APPOD method, the EAPPOD strategy incorporates a series of integral newly designed methods, including average assignment, phase tracking, and magnitude attenuation, to overcome the limitations of the APPOD method operating in varying-latency situations, and consequently to achieve a better Low Frequency Oscillation Damping (LFOD) performance. The newly proposed EAPPOD method will thus benefit both online power system monitoring and LFOD enhancement.

Symplectic geometry mode decomposition and its application to rotating machinery compound fault diagnosis

Abstract Various existed time-series decomposition methods, including wavelet transform, ensemble empirical mode decomposition (EEMD), local characteristic-scale decomposition (LCD), singular spectrum analysis (SSA), etc., have some defects for nonlinear system signal analysis. When the signal is more complex, especially noisy signal, the component signal is forced to decompose into several incomplete components by LCD and SSA. In addition, the wavelet transform and EEMD need user-defined parameters, and they are very sensitive to the parameters. Therefore, a new signal decomposition algorithm, symplectic geometry mode decomposition (SGMD), is proposed in this paper to decompose a time series into a set of independent mode components. SGMD uses the symplectic geometry similarity transformation to solve the eigenvalues of the Hamiltonian matrix and reconstruct the single component signals with its corresponding eigenvectors. Meanwhile, SGMD can efficiently reconstruct the existed modes and remove the noise without any user-defined parameters. The essence of this method is that signal decomposition is converted into symplectic geometry transformation problem, and the signal is decomposed into a set of symplectic geometry components (SGCs). The analysis results of simulation signals and experimental signals indicate that the proposed time-series decomposition approach can decompose the analyzed signals accurately and effectively.

Ensemble empirical mode decomposition based adaptive wavelet neural network method for wind speed prediction

Wind energy is one of the emerging sustainable sources of electricity. Wind is intermittent in nature. The typical grid operation of wind energy is complex. The significance of wind energy generation and integration with the grid is increasing day by day. An accurate wind speed forecasting method will help the utility planners and operators to meet the balance of supply and demand by generating wind energy. In this paper, a statistical-based wind speed prediction is implemented without utilizing the numerical weather prediction inputs. This analytical study proposes a hybrid short-term prediction approach that can successfully preprocess the original wind speed data to enhance the forecasting accuracy. The most efficient signal decomposition algorithm, Ensemble Empirical Mode Decomposition is used for preprocessing. This ensemble empirical mode decomposition technique decomposes the original wind speed data. Each decomposed signal is regressed to forecast the future wind speed value by utilizing the Adaptive Wavelet Neural Network model. The proposed hybrid approach is subsequently investigated with respect to the wind farm of South India. The results from a real-world case study in India are reported along with comprehensive comparison. The prediction performance delivered high accuracy, less uncertainty and low computational burden in the forecasts attained. The developed hybrid model outperforms the six other benchmark models such as persistence method, back propagation neural network, radial basis function neural network, Elman neural network, Gaussian regression neural network, and wavelet neural network.

A Novel Hydrocarbon Detection Approach via High-Resolution Frequency-Dependent AVO Inversion Based on Variational Mode Decomposition

Amplitude-versus-offset (AVO) inversion always plays an important role in reservoir fluid identification, which allows the estimation of various rock and fluid properties from prestack seismic data. In this paper, we propose a new method for discrimination of hydrocarbon accumulation that combines frequency-dependent AVO inversion scheme and variational mode decomposition (VMD). VMD is a recently developed algorithm for adaptive signal decomposition that is able to nonrecursively decompose a multicomponent signal into a number of quasi-orthogonal intrinsic mode functions and avoid mode mixing effectively. VMD is superior to other state-of-the-art approaches in obtaining high-resolution and high-fidelity local time–frequency depiction performance. Two synthetic signals are employed to illustrate that VMD achieves higher temporal and frequency resolution than the conventional continuous wavelet transform (CWT) decomposition. Other synthetic examples, elastic and dispersive, are utilized to demonstrate that the proposed method is more reliable for the detection of hydrocarbon saturation and a comparison is made with the CWT-based inverted results. Application on field data has further shown that the proposed approach has the potential in identifying the reservoir related to hydrocarbon.

Variational mode decomposition denoising combined with the Hausdorff distance.

Variational mode decomposition (VMD) is a recently introduced adaptive signal decomposition algorithm with a solid theoretical foundation and good noise robustness compared with empirical mode decomposition (EMD). However, there is still a problem with this algorithm associated with the selection of relevant modes. To solve this problem, this paper proposes a novel signal-filtering method that combines VMD with Hausdorff distance (HD) in the VMD-HD method. A noisy signal is first decomposed into a given number K of band-limited intrinsic mode functions by VMD. The probability density function is then estimated for each mode. The aim of this method is to reconstruct the signal using the relevant modes, which are selected on the basis of noticeable similarities between the probability density function of the input signal and that of each mode. Various similarity measures are investigated and compared, and the HD is shown to offer the best performance. The results of filtering of simulation signals illustrate the validity of the proposed method when compared with EMD-based methods under comprehensive quantitative evaluation criteria. As a specific example, the proposed method is successfully used for filtering the pipeline leakage signal as evaluated by the de-trended fluctuation analysis algorithm.

A Signal Decomposition Method for Ultrasonic Guided Wave Generated from Debonding Combining Smoothed Pseudo Wigner-Ville Distribution and Vold–Kalman Filter Order Tracking

Carbon fibre composites have a promising application future of the vehicle, due to its excellent physical properties. Debonding is a major defect of the material. Analyses of wave packets are critical for identification of the defect on ultrasonic nondestructive evaluation and testing. In order to isolate different components of ultrasonic guided waves (GWs), a signal decomposition algorithm combining Smoothed Pseudo Wigner-Ville distribution and Vold–Kalman filter order tracking is presented. In the algorithm, the time-frequency distribution of GW is first obtained by using Smoothed Pseudo Wigner-Ville distribution. The frequencies of different modes are computed based on summation of the time-frequency coefficients in the frequency direction. On the basis of these frequencies, isolation of different modes is done by Vold–Kalman filter order tracking. The results of the simulation signal and the experimental signal reveal that the presented algorithm succeeds in decomposing the multicomponent signal into monocomponents. Even though components overlap in corresponding Fourier spectrum, they can be isolated by using the presented algorithm. So the frequency resolution of the presented method is promising. Based on this, we can do research about defect identification, calculation of the defect size, and locating the position of the defect.

Medical Image Segmentation of Improved Genetic Algorithm Research Based on Dictionary Learning

The image signal is represented by using the atomic of image signal to train an over complete dictionary and is described as sparse linear combinations of these atoms. Recently, the dictionary algorithm for image signal tracking and decomposition is mainly adopted as the focus of research. An alternate iterative algorithm of sparse encoding, sample dictionary and dictionary based on atomic update process is K-SVD decomposition. A new segmentation algorithm of brain MRI image, which uses the noise reduction method with adaptive dictionary based on genetic algorithm, is presented in this paper, and the experimental results show that the algorithm in brain MRI image segmentation has fast calculation speed and the advantage of accurate segmentation. In a very complicated situation, the results show that the segmentation of brain MRI images can be accomplished successfully by using this algorithm, and it achieves the ideal effect and has good accuracy.

Super-resolution in sound field recording and reproduction based on sparse representation

Sound field recording and reproduction enables us to construct more realistic audio systems. In practical systems, sound pressure is obtained with microphones in a recording area, and then the sound field is reproduced with loudspeakers in a target area. Therefore, a signal conversion algorithm for obtaining the driving signals of the loudspeakers from the signals received by the microphones is necessary. Most of the current methods are based on the sound field analysis and synthesis in the spatial frequency domain. Although these methods make it possible for stable and efficient signal conversion, artifacts originating from spatial aliasing notably occur, which depends on the intervals of microphones and loudspeakers. We have proposed a signal conversion method based on sparse sound field decomposition, which enables sound field recording and reproduction above the spatial Nyquist frequency. By using sparse decomposition algorithms, the sound pressure distribution can be represented using a small number of fundamental solutions of the Helmholtz equation, such as Green's functions. Group sparse signal models are also required for accurate and robust decomposition. In this presentation, we reports on comparisons between several group sparse signal models and decomposition algorithms as well as their relation to reproduction performances.

Constructions of ϵ-mono-components and mathematical analysis on signal decomposition algorithm

The concept of mono-component is widely used in non-stationary signal processing and time-frequency analysis. A special class of mono-components, called ϵ-mono-components, were proposed in our recent publication. It was illustrated that this model coincides with the intuition of human beings on the physical mono-components very well provided that the parameter ϵ is sufficiently small. It is then very meaningful to construct desired ϵ-mono-components and design algorithms to decompose and represent non-stationary signals adaptively. This paper studies the constructions of ϵ-mono-components and makes mathematical analysis on an adaptive signal decomposition algorithm based on ϵ-mono-components.

Time-frequency distribution decomposition with applications to recognize the looseness state of the viscoelastic sandwich structure

In general, a vibration signal consists of several frequency modulation (FM) components. Every component contains different information, and can be characterized by its instantaneous amplitude (IA) and instantaneous phase (IP). In engineering applications, conventional time-frequency analysis methods and signal decomposition methods have shown their power in investigating features of the vibration signal. However, they are limited in resolution and it is hard to analyze these FM components individually. To overcome these deficiencies, a novel signal decomposition algorithm, named time-frequency distribution decomposition (TFDD), is proposed in this paper, which reconstructs one FM component of the signal at a time by estimating its IP and IA. The IA and IP are approximated by two polynomial functions respectively. One important advantage of TFDD is that it can directly extract the component we are interested in. Therefore, we can analyze the key component of the signal with little influence from other components. This will help us to characterize the vibration signal more deeply. Furthermore, it is very stable to noise. This is conductive to protecting the information of the vibration signal. The effectiveness of the TFDD is validated by a numerical simulation and the study of the vibration response signal collected from a viscoelastic sandwich structure (VSS). From the value of permutation entropy of the component extracted by TFDD, the looseness state of the VSS is recognized.

Fast Algorithm for Adaptive Gaussian Chirplet Signal Decomposition

It is well known that adaptive Gaussian chirplet signal decomposition algorithm has the best time frequency resolution in all signal decomposition algorithms. It is widely used in non linear and non stationary signal decomposition, especially for the signal which is superposition of chirplet functions decomposition. But it has a large amount of computation. In this paper, we propose a fast algorithm based on short time Fourier transform (STFT) method and we change parameters’ domain. Using this fast algorithm to decompose a four atoms non-linear signal computes very fast and it can also avoid the cross term’s interferer of the Wigner-Ville distribution.

A Constrained ICA Approach for Real-Time Cardiac Artifact Rejection in Magnetoencephalography

Recently, magnetoencephalography (MEG)-based real-time brain computing interfaces (BCI) have been developed to enable novel and promising methods of neuroscience research and therapy. Artifact rejection prior to source localization largely enhances the localization accuracy. However, many BCI approaches neglect real-time artifact removal due to its time consuming processing. With cardiac artifact rejection for real-time analysis (CARTA), we introduce a novel algorithm capable of real-time cardiac artifact (CA) rejection. The method is based on constrained independent component analysis (ICA), where a priori information of the underlying source signal is used to optimize and accelerate signal decomposition. In CARTA, this is performed by estimating the subject's individual density distribution of the cardiac activity, which leads to a subject-specific signal decomposition algorithm. We show that the new method is capable of effectively reducing CAs within one iteration and a time delay of 1 ms. In contrast, Infomax and Extended Infomax ICA converged not until seven iterations, while FastICA needs at least ten iterations. CARTA was tested and applied to data from three different but most common MEG systems (4-D-Neuroimaging, VSM MedTech Inc., and Elekta Neuromag). Therefore, the new method contributes to reliable signal analysis utilizing BCI approaches.

Signal decomposition algorithm for reducing peak-to-average power ratio of OFDM

Signal decomposition algorithm for reducing peak-to-average power ratio of OFDM

Damage Signal Decomposition of Lamb Wave Based on Similarity in SHM

Rapid detecting structural damage by lamb wave is an emerging technology in the field of structural health monitoring, and damage signal decomposition of Lamb wave, with the intent of obtaining reflection signal from damage, is the key point of detecting damage by Lamb wave. Therefore, this paper proposes a new algorithm for damage signal decomposition--similarity-based algorithm. In order to obtain reflection signal form damage, it simulates the reflection signals from damage and boundary with the waveform after the input signal has propagated a distance, and then optimizes the various parameters of the synthetic signal, which consists of reflection signals from damage and boundary, by genetic algorithm based on similarity so that the similarity between the synthetic signal and the damage signal reaches the maximum. Finally, simulation results showed the feasibility and accuracy of the algorithm proposed in this paper.

Detection of Sleep Disordered Breathing and Its Central/Obstructive Character Using Nasal Cannula and Finger Pulse Oximeter

To assess the accuracy of novel algorithms using an oximeter-based finger plethysmographic signal in combination with a nasal cannula for the detection and differentiation of central and obstructive apneas. The validity of single pulse oximetry to detect respiratory disturbance events was also studied. Patients recruited from four sleep laboratories underwent an ambulatory overnight cardiorespiratory polygraphy recording. The nasal flow and photoplethysmographic signals of the recording were analyzed by automated algorithms. The apnea hypopnea index (AHI(auto)) was calculated using both signals, and a respiratory disturbance index (RDI(auto)) was calculated from photoplethysmography alone. Apnea events were classified into obstructive and central types using the oximeter derived pulse wave signal and compared with manual scoring. Sixty-six subjects (42 males, age 54 ± 14 yrs, body mass index 28.5 ± 5.9 kg/m(2)) were included in the analysis. AHI(manual) (19.4 ± 18.5 events/h) correlated highly significantly with AHI(auto) (19.9 ± 16.5 events/h) and RDI(auto) (20.4 ± 17.2 events/h); the correlation coefficients were r = 0.94 and 0.95, respectively (p < 0.001) with a mean difference of -0.5 ± 6.6 and -1.0 ± 6.1 events/h. The automatic analysis of AHI(auto) and RDI(auto) detected sleep apnea (cutoff AHI(manual) ≥ 15 events/h) with a sensitivity/specificity of 0.90/0.97 and 0.86/0.94, respectively. The automated obstructive/central apnea indices correlated closely with manually scoring (r = 0.87 and 0.95, p < 0.001) with mean difference of -4.3 ± 7.9 and 0.3 ± 1.5 events/h, respectively. Automatic analysis based on routine pulse oximetry alone may be used to detect sleep disordered breathing with accuracy. In addition, the combination of photoplethysmographic signals with a nasal flow signal provides an accurate distinction between obstructive and central apneic events during sleep.