Adaptive Signal Processing Method Research Articles

Parameter estimation of the systems with irregularly missing data by using sequentially parallel distributed adaptive signal processing architecture

This paper considers problems related to output error models for output data estimation and parameter identification in missing output data systems. In this regard, a new sequentially parallel distributed adaptive signal processing method with the implementation of a low complexity least squares algorithm is introduced to estimate the parameters of an auxiliary model relative to the original system, as well as handling irregularly missing output data in a stochastic framework. The validation of the proposed distributed architecture using the low complexity least squares algorithm is presented in terms of computational complexity and processing time. Measurement results show that the proposed architecture using the low complexity least squares approach provides fast convergence, parallel linear computational complexity, and significantly reduced processing time compared to the sequentially operated recursive least square (RLS) algorithm for parameter identification in missing output data systems. Finally, the effectiveness of the low complexity least squares algorithm is tested with a system example

Intelligent fault diagnosis of rolling bearing using variational mode extraction and improved one-dimensional convolutional neural network

When the rolling bearing fails, the fault features contained in bearing vibration signal are easily submerged by fortissimo noise interference signals, and have obvious non-stationary and nonlinear properties. This means that it is extremely challenging to acquire useful bearing fault features and identify bearing fault patterns effectively by traditional diagnosis methods. To more efficiently learn bearing fault information and improve bearing fault diagnosis accuracy, this research proposes a new intelligent fault diagnosis method for rolling bearing based on variational mode extraction (VME) and an improved one-dimensional convolutional neural network (I-1DCNN). Firstly, a new adaptive signal processing method named VME is employed to handle the collected bearing vibration signals with the aim of obtaining the desired mode component and removing the noise interference information. Meanwhile, the extracted mode components are randomly divided into the training set, validation set and test set. Then, the training set and validation set are input into the proposed I-1DCNN model for training, where the proposed I-1DCNN model may not only learn the discriminant features intelligently, but also boost the computational efficiency and alleviate the problem of over-fitting by incorporating the early stopping method and self-attention mechanism into the traditional one-dimensional convolutional neural network (1DCNN). Finally, the test set is input into the well-trained I-1DCNN to realize the automatic identification of different fault types of rolling bearing. The effectiveness of the suggested method is illustrated by analyzing two experimental data sets. In addition, by comparing with other representative methods, the superiority of the proposed method is testified in bearing health condition identification.

Microgrid Fault Detection and Classification Based on the Boosting Ensemble Method With the Hilbert-Huang Transform

In this paper, a sequential ensemble of intelligence-based methods is used for fault detection and classification in microgrids. The proposed scheme is recommended because of the impracticality of conventional fault detection and protection due to microgrid dynamic behavior and dependency of traditional methods on faults current level or impedance. These methods use the collective decision of learners to increase their accuracy. This is done by distributing the knowledge among the classifiers. The proposed ensemble method is called Brownboost. Its main advantage over its counterparts is that it uses a nonconvex optimization method. This makes it robust to overfitting and applicable and practical for a real-world noisy or misclassified data. In addition, a signal processing method called the Hilbert-Huang transform was chosen for feature extraction from signals transient behavior to reduce the noise sensitivity. In this method, current difference of the both ends of line is selected to decompose various types of adaptive basis signal processing method. The results are validated in IEC test microgrid with various noise penetration level and synchronization delays. Compared to traditional strong classifiers, this method is easy to tune and program and is robust to overfitting. Moreover, it can be justified for real-world imperfect data.

SAVMD: An adaptive signal processing method for identifying protein coding regions

The identification of protein coding regions is a major topic of research in the field of gene prediction. A number of digital signal processing (DSP) based approaches, which exploit 3-base periodicity to detect coding regions, have been proposed. According to these previously published approaches, we summarize that an effective method or filter for identifying protein coding regions should fulfill three important properties, including the independence of the window length, an effective and adaptive frequency response, a fixed basic frequency of 1∕3f. However, most of published approaches cannot simultaneously satisfy these three points, which causes that their identification accuracy is still limited. In this paper, we propose an adaptive signal processing method, called sinusoidal-assisted variational mode decomposition (SAVMD) for identifying coding regions. The adaptability of SAVMD reflects in two aspects including: (i) The proposed method analyzes numerical sequences without needing any window information; (ii) The spectrum of period-3 component can be automatically fitted by SAVMD in Fourier domain. From this, our proposed method outperforms other DSP-based methods in terms of identification accuracy, which is verified by the experimental results on five benchmark datasets. When processing the dataset where most sequences contain undetermined nucleotides (UDT), SAVMD shows more superior performance than the model-dependent method AUGUSTUS as well as other model-independent methods. In addition, we conduct a comparative analysis on different numerical conversions of DNA sequences using SAVMD. Several applicable methods for SAVMD, which are selected from this experimentation, can provide a reference to the applications of other time–frequency decomposition methods in the field of gene prediction.

Hereditary disease prediction in eukaryotic DNA: an adaptive signal processing approach

Hereditary disease prediction in eukaryotic DNA using signal processing approaches is an incredible work in bioinformatics. Researchers of various fields are trying to put forth a noninvasive approach to forecast the disease-related genes. As diseased genes are more random than the healthy ones, in this work, a comparison of the diseased gene is made against the healthy ones. An adaptive signal processing method like functional link artificial neural network-based Levenberg–Marquardt filter has been proposed in this regard. For parameter upgradation, the algorithm is modified using particle swarm optimization. Here, disease genes are discriminated from healthy ones based on the magnitude of mean square error (MSE), which is calculated through the adaptive filter. The performance of the algorithm is inspected by computing some evaluation parameters. Since accuracy is the prime concern, authors in this work have taken an attempt to improve the accuracy level compared to the existing methods. Taking the reference gene as healthy, the overall process is accomplished by categorizing the diseased and healthy targets with MSE value at a threshold of 0.012. The proposed technique predicts the test gene sets successfully.

Research on Rolling Bearing Fault Diagnosis Using Improved Majorization-Minimization-Based Total Variation and Empirical Wavelet Transform

Bearings are among the most widely used core components in mechanical equipment. Their failure creates the potential for serious accidents and economic losses. Vibration signature analyses are the most common approach to assess the viability of bearings due to its ease of measurement and high correlation with structural dynamics. However, the collected vibration signals of rolling bearings are usually nonstationary and are inevitably accompanied by noise interference. This makes it difficult to extract the feature frequency for the failed bearing and affects the diagnosis accuracy. The majorization-minimization-based total variation (TV-MM) denoising algorithm effectively removes the noise interference from the signal and highlights the related feature information. The value of its main parameter λ determines the quality of the denoising effect. However, manually selecting parameters requires professional experience in a process that it is time-consuming and laborious, while the use of genetic algorithms is cumbersome. Therefore, an improved particle swarm algorithm (IPSO) is used to find the optimal solution of λ. The IPSO utilises the mutation concept in genetic algorithms to reinitialise the particles with a certain probability after each update. In addition, the empirical wavelet transform (EWT) is an adaptive signal processing method suitable for processing nonlinear and nonstationary signals. Therefore, this paper presents an ensemble analysis method that combines the IPSO, TV-MM, and EWT. First, IPSO is used to optimise the denoising parameter λ. The TV-MM under this parameter effectively removes the background noise interference and improves the accuracy of the subsequent modal decomposition. Then, the EWT is used for the adaptive division to produce a set of sequences. Finally, Hilbert envelope demodulation is performed on each component to realise fault diagnosis. The results from simulations and signals received from defective bearings with outer race fault, inner race fault, and rolling element fault demonstrate the effectiveness of the proposed method for fault diagnosis of rolling bearings.

A Fast Adaptive Speech Extraction Method using Blind Source Separation for Audio Signal Processing

The adaptive signal processing methods are used in several applications like channel estimation, Noise removal and extraction of signals also. The methods vary on time, frequency and statistical approach. In this paper, the source speech signals are separated using different methods like FastICA,PCA and kICA. Comparison of original signal and estimated signals are evaluated for different methods. The implementation was done in MATLAB. The spectrogram, Negentropy and Kurtosis waveforms are plotted for different methods.

Improved Wideband Adaptive Transmit Nulling Using Subband Phase-Only Pattern Synthesis

The phase-only transmit nulling method is widely used in narrowband arrays, but there are few reports of its application in wideband arrays. This paper describes the implementation of adaptive transmit nulls in wideband arrays based on subband phase-only pattern synthesis behind each array element. The filter bank behind each array element partitions the transmit signal into independent subbands and utilizes a phase shifter group in each subband to form frequency invariant spatial nulls in the array’s transmit pattern. The scheme developed in this paper includes an algorithm for computing a wideband phase-only weight vector and a subband phase-only adaptive signal processing method based on band partitioning. The validity of the scheme is proven by theoretical analysis and simulation experiments.

Application of optimized variational mode decomposition based on kurtosis and resonance frequency in bearing fault feature extraction

Variational mode decomposition (VMD) is an adaptive signal processing method proposed recently. It has gradually been widely used due to its good performance. According to the problem that the parameters of VMD need to be determined in advance, a simple and feasible method of determining the influence parameters based on the principle of kurtosis maximum is put forward. A novel intrinsic mode function (IMF) selection method based on resonance frequency is proposed in order to select the IMF that contains the abundant fault feature information. Firstly, the parameters of VMD are optimized by the principle of kurtosis maximum, the optimal penalty parameter and mode number of VMD are set, and the original fault signal is processed by the optimized VMD to obtain the established IMF components. Then, the sensitive IMF(s) with the fault information is selected by resonance frequency. Finally, the selected IMF(s) is analyzed by the envelope demodulation analysis to extract the fault characteristic frequency to judge the fault type of the rolling bearing. It is shown that the method can extract the weak characteristics of the early fault signal of the rolling bearing, and it can realize the judgment of the bearing fault accurately through the analysis of simulated signal and the actual data of bearing.

Application of fast singular spectrum decomposition method based on order statistic filter in rolling bearing fault diagnosis

Singular spectrum decomposition (SSD) is a new adaptive signal processing method for nonlinear and non-stationary signals. By constructing a trajectory matrix and adaptively selecting the embedding dimensions, the method divides non-stationary signals into several single-component signals successively from high frequency to low frequency. However, in the process of component reconstruction, bandwidth estimation and determining sizable trends by building a Gaussian function superposition spectral model are extremely complicated. Moreover, the parameter setting requires too much manual intervention and lacks theoretical support. Hence, aimed at nonlinear and non-stationary vibration signals of rolling bearings, a novel method of fault feature extraction based on the order statistic filter (OSF) for fast SSD (FSSD) is proposed. The FSSD method adopts the envelope method of OSFs to divide the spectrum and determine the sizable trend to improve the process. The proposed method is applied to bearing fault diagnosis. The analysis results of simulation signals and bearing experimental signals show that the new method can decompose signals quickly, effectively and accurately, and the mode mixing and time-consuming problems are refined.

Improved Vancouver Raman Algorithm Based on Empirical Mode Decomposition for Denoising Biological Samples.

A novel method based on the Vancouver Raman algorithm (VRA) and empirical mode decomposition (EMD) for denoising Raman spectra of biological samples is presented. The VRA is one of the most used methods for denoising Raman spectroscopy and is composed of two main steps: signal filtering and polynomial fitting. However, the signal filtering step consists in a simple mean filter that could eliminate spectrum peaks with small intensities or merge relatively close spectrum peaks into one single peak. Thus, the result is often sensitive to the order of the mean filter, so the user must choose it carefully to obtain the expected result; this introduces subjectivity in the process. To overcome these disadvantages, we propose a new algorithm, namely the modified-VRA (mVRA) with the following improvements: (1) to replace the mean filter step by EMD as an adaptive parameter-free signal processing method; and (2) to automate the selection of polynomial degree. The denoising capabilities of VRA, EMD, and mVRA were compared in Raman spectra of artificial data based on Teflon material, synthetic material obtained from vitamin E and paracetamol, and biological material of human nails and mouse brain. The correlation coefficient (ρ) was used to compare the performance of the methods. For the artificial Raman spectra, the denoised signal obtained by mVRA () outperforms VRA () for moderate to high noise levels whereas mVRA outperformed EMD () for high noise levels. On the other hand, when it comes to modeling the underlying fluorescence signal of the samples (i.e., the baseline trend), the proposed method mVRA showed consistent results (. For Raman spectra of synthetic material, good performance of the three methods ( for VRA, for EMD, and for mVRA) was obtained. Finally, in the biological material, mVRA and VRA showed similar results ( for VRA, for EMD, and for mVRA); however, mVRA retains valuable information corresponding to relevant Raman peaks with small amplitude. Thus, the application of EMD as a filter in the VRA method provides a good alternative for denoising biological Raman spectra, since the information of the Raman peaks is conserved and parameter tuning is not required. Simultaneously, EMD allows the baseline correction to be automated.

An Improved Signal Processing Approach Based on Analysis Mode Decomposition and Empirical Mode Decomposition

Empirical mode decomposition (EMD) is a widely used adaptive signal processing method, which has shown some shortcomings in engineering practice, such as sifting stop criteria of intrinsic mode function (IMF), mode mixing and end effect. In this paper, an improved sifting stop criterion based on the valid data segment is proposed, and is compared with the traditional one. Results show that the new sifting stop criterion avoids the influence of end effects and improves the correctness of the EMD. In addition, a novel AEMD method combining the analysis mode decomposition (AMD) and EMD is developed to solve the mode-mixing problem, in which EMD is firstly applied to dispose the original signal, and then AMD is used to decompose these mixed modes. Then, these decomposed modes are reconstituted according to a certain principle. These reconstituted components showed mode mixing phenomena alleviated. Model comparison was conducted between the proposed method with the ensemble empirical mode decomposition (EEMD), which is the mainstream method improved based on EMD. Results indicated that the AEMD and EEMD can effectively restrain the mode mixing, but the AEMD has a shorter execution time than that of EEMD.

A Novel Adaptive Signal Processing Method Based on Enhanced Empirical Wavelet Transform Technology.

Empirical wavelet transform (EWT) is a novel adaptive signal decomposition method, whose main shortcoming is the fact that Fourier segmentation is strongly dependent on the local maxima of the amplitudes of the Fourier spectrum. An enhanced empirical wavelet transform (MSCEWT) based on maximum-minimum length curve method is proposed to realize fault diagnosis of motor bearings. The maximum-minimum length curve method transforms the original vibration signal spectrum to scale space in order to obtain a set of minimum length curves, and find the maximum length curve value in the set of the minimum length curve values for obtaining the number of the spectrum decomposition intervals. The MSCEWT method is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs), which are processed by Hilbert transform. Then the frequency of each component is extracted by power spectrum and compared with the theoretical value of motor bearing fault feature frequency in order to determine and obtain fault diagnosis result. In order to verify the effectiveness of the MSCEWT method for fault diagnosis, the actual motor bearing vibration signals are selected and the empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) methods are selected for comparative analysis in here. The results show that the maximum-minimum length curve method can enhance EWT method and the MSCEWT method can solve the shortcomings of the Fourier spectrum segmentation and can effectively decompose the bearing vibration signal for obtaining less number of intrinsic mode function (IMF) components than the EMD and EEMD methods. It can effectively extract the fault feature frequency of the motor bearing and realize fault diagnosis. Therefore, the study provides a new method for fault diagnosis of rotating machinery.

Quantitative diagnosis for bearing faults by improving ensemble empirical mode decomposition

In the bearing health assessment issues, using the adaptive nonstationary vibration signal processing methods in the time-frequency domain, lead to improving of early fault detection. On the other hand, the noise and random impulses which contaminates the input data, are a major challenge in extracting fault-related features. The main goal of this paper is to improve the Ensemble Empirical mode decomposition (EEMD) algorithm and combine it with a new proposed denoising process and the higher order spectra to increase the accuracy and speed of the fault severity and type detection. The main approach is to use statistical features without using any dimension reduction and data training. To eliminate unrelated components from faulty condition, the best combination of denoising parameters based on the wavelet transform, is determined by a proposed performance index. In order to enhance the efficiency of the EEMD algorithm, a systematic method is presented to determine the proper amplitude of the additive noise and the Intrinsic Mode Functions (IMFs) selection scheme. The fault occurrence detection and the fault severity level identification are performed by the Fault Severity Index (FSI) definition based on the energy level of the Combined Fault-Sensitive IMF (CFSIMF) envelope using the central limit theorem. Also, taking the advantages of a bispectrum analysis of CFSIMF envelope, fault type recognition can be achieved by Fault Type Index (FTI) quantification. Finally, the proposed method is validated using experimental data set from two different test rigs. Also, the role of the optimum denoising process and the algorithm of systematic selection of the EEMD parameters are described regardless of its type and estimating the consistent degradation pattern.

High-resolution wavenumber estimation in ultrasonic guided waves using adaptive array signal processing for bone quality assessment

Quantitative ultrasound is a modality that is used to evaluate bone quality. It is considered that the analysis of ultrasound guided wave propagating along cortical bone may be useful for the assessment of cortical bone quality. Because the frequency-dependent wavenumbers reflect the elastic parameters of the medium, high-resolution estimation of the wavenumbers at each frequency is important. We report an adaptive array signal processing method with a technique to estimate the numbers of propagation modes at each frequency using information theoretic criteria and the diagonal loading technique. The proposed method estimates the optimal diagonal loading value required for guided wave estimation. We investigate the effectiveness of the proposed method via simple numerical simulations and experiments using a copper plate and a bone-mimicking plate, where the center frequency of the transmit wave was 1.0 MHz. An experimental study of 4 mm thick copper and bone-mimicking plates showed that the proposed method estimated the wavenumbers accurately with estimation errors of less than 4% and a calculation time of less than 0.5 s when using a laptop computer.

Rapid High-Resolution Wavenumber Extraction from Ultrasonic Guided Waves Using Adaptive Array Signal Processing

Quantitative ultrasound techniques for assessment of bone quality have been attracting significant research attention. The axial transmission technique, which involves analysis of ultrasonic guided waves propagating along cortical bone, has been proposed for assessment of cortical bone quality. Because the frequency-dependent wavenumbers reflect the elastic parameters of the medium, high-resolution estimation of the wavenumbers is required at each frequency with low computational cost. We use an adaptive array signal processing method and propose a technique that can be used to estimate the numbers of propagation modes that exist at each frequency without the need for time-consuming calculations. An experimental study of 4-mm-thick copper and bone-mimicking plates showed that the proposed method estimated the wavenumbers accurately with estimation errors of less than 4% and a calculation time of less than 0.5 s when using a laptop computer.

Sparsity guided empirical wavelet transform for fault diagnosis of rolling element bearings

Abstract Rolling element bearings are widely used in various industrial machines, such as electric motors, generators, pumps, gearboxes, railway axles, turbines, and helicopter transmissions. Fault diagnosis of rolling element bearings is beneficial to preventing any unexpected accident and reducing economic loss. In the past years, many bearing fault detection methods have been developed. Recently, a new adaptive signal processing method called empirical wavelet transform attracts much attention from readers and engineers and its applications to bearing fault diagnosis have been reported. The main problem of empirical wavelet transform is that Fourier segments required in empirical wavelet transform are strongly dependent on the local maxima of the amplitudes of the Fourier spectrum of a signal, which connotes that Fourier segments are not always reliable and effective if the Fourier spectrum of the signal is complicated and overwhelmed by heavy noises and other strong vibration components. In this paper, sparsity guided empirical wavelet transform is proposed to automatically establish Fourier segments required in empirical wavelet transform for fault diagnosis of rolling element bearings. Industrial bearing fault signals caused by single and multiple railway axle bearing defects are used to verify the effectiveness of the proposed sparsity guided empirical wavelet transform. Results show that the proposed method can automatically discover Fourier segments required in empirical wavelet transform and reveal single and multiple railway axle bearing defects. Besides, some comparisons with three popular signal processing methods including ensemble empirical mode decomposition, the fast kurtogram and the fast spectral correlation are conducted to highlight the superiority of the proposed method.

Denoising of Raman spectroscopy for biological samples based on empirical mode decomposition

Raman spectroscopy of biological samples presents undesirable noise and fluorescence generated by the biomolecular excitation. The reduction of these types of noise is a fundamental task to obtain the valuable information of the sample under analysis. This paper proposes the application of the empirical mode decomposition (EMD) for noise elimination. EMD is a parameter-free and adaptive signal processing method useful for the analysis of nonstationary signals. EMD performance was compared with the commonly used Vancouver algorithm (VRA) through artificial data (Teflon), synthetic (Vitamin E and paracetamol) and biological (Mouse brain and human nails) Raman spectra. The correlation coefficient ([Formula: see text]) was used as performance measure. Results on synthetic data showed a better performance of EMD ([Formula: see text]) at high noise levels compared with VRA ([Formula: see text]). The methods with simulated fluorescence added to artificial material exhibited a similar shape of fluorescence in both cases ([Formula: see text] for VRA and [Formula: see text] for EMD). For synthetic data, Raman spectra of vitamin E were used and the results showed a good performance comparing both methods ([Formula: see text] for EMD and [Formula: see text] for VRA). Finally, in biological data, EMD and VRA displayed a similar behavior ([Formula: see text] for EMD and [Formula: see text] for VRA), but with the advantage that EMD maintains small amplitude Raman peaks. The results suggest that EMD could be an effective method for denoising biological Raman spectra, EMD is able to retain information and correctly eliminates the fluorescence without parameter tuning.

Improving dynamic proximity sensing and processing for smart work-zone safety

Abstract Equipment/vehicles striking workers is one of the most frequent accidents that occur in roadway workzones. As a means of prevention, a number of active technologies have been developed to provide proximity sensing and alerts for workers and equipment operators. However, most of these systems are based on the distance/proximity level between workers and equipment and neglect the variations caused by different settings and environmental conditions, such as equipment types and approaching speeds, which can result in inconsistency and delay of the systems. As of yet, previous research has insufficiently investigated these issues. This research addresses the issues by utilizing the Bluetooth Low Energy (BLE)-based proximity sensing and alert system developed by the authors. This paper discusses the development and assessment of parameter adjustment and adaptive signal processing (ASP) methods. The research conducted field trials in various dynamic conditions and settings to assess the performance of the system. The test results showed that the parameter adjustment function reduced the inconsistency of the alert distances resulting from different types of equipment, and that the ASP method reduced the time delay resulting from high approaching speeds. The developed proximity safety alerts system provides stakeholders with better understanding of dynamic spatial relationships among equipment, operator, workers, and a surrounding work environment; thus, improving construction work zone safety.

Erratum to: Adaptive Signal Processing Method for Speech Organ Diagnostics

Erratum to: Adaptive Signal Processing Method for Speech Organ Diagnostics