Remove Noise Components Research Articles

Weak faults diagnosis of traction motor bearings based on SR-MED-EEMD method

Rolling bearings play a crucial role in ensuring the security and reliable operation of high-speed trains (HST) as a vital component of their traction system. In response to the difficulty in extracting early fault features of high-speed train traction motor bearings under strong background noise, a method based on sparse representation (SR), minimum entropy deconvolution (MED), and ensemble empirical mode decomposition (EEMD) is proposed. The method reduces the dimensionality of complex signals through SR and then filters the sparse signals through MED to improve the fault signal pulse amplitude. The processed signals are decomposed by using EEMD to remove noise components, further improving the peak signal-to-noise ratio of fault characteristic frequency. The method’s efficacy in detecting faults within rolling bearings has been confirmed through simulation experiments and demonstrated its high precision.

Denoising multispectral images using non-local rank tensor decomposition and bilateral filtering based on sunflower optimization

ABSTRACT Image denoising is an important pre-processing process in the fields of computer vision and image processing. Traditional denoising techniques blur edges excessively and degrade image quality by removing noise components but failing to maintain clarity. To overcome these problems, this paper proposes a multispectral image denoising strategy combining non-local rank tensor decomposition (NLRTD) and bilateral filtering. To extract patches from noisy images, single-level discrete wavelet transform (DWT) is utilized. Then, similar patches from the extracted images are grouped using spectral clustering. After that, mixed noise is reduced by separating clean images from each clustered group using NLRTD. An optimized bilateral filter using Sunflower optimization (SFO) is used for denoising by preserving edge details and is reconstructed using its constituent parts. The effectiveness of the proposed denoising method is assessed using performance matrices, such as BER, PSNR, MSE, RMSE, SNR and SSIM were 0.8544%, 53.21%, 2.41%, 2.41%, 25.06% and 0.90%, respectively.

Efficient PCA denoising of spatially correlated redundant MRI data

Abstract Marčenko-Pastur PCA (MPPCA) denoising is emerging as an effective means for noise suppression in MR imaging (MRI) acquisitions with redundant dimensions. However, MPPCA performance can be severely compromised by spatially correlated noise—an issue typically affecting most modern MRI acquisitions—almost to the point of returning the original images with little or no noise removal. In this study, we explore different threshold criteria for principal component analysis (PCA) component classification that enable efficient and robust denoising of MRI data even when noise exhibits high spatial correlations, especially in cases where data are acquired with Partial Fourier and when only magnitude data are available. We show that efficient denoising can be achieved by incorporating a-priori information about the noise variance into PCA denoising thresholding. Based on this, two denoising strategies developed here are: 1) General PCA (GPCA) denoising that uses a-priori noise variance estimates without assuming specific noise distributions; and 2) Threshold PCA (TPCA) denoising which removes noise components with a threshold computed from a-priori estimated noise variance to determine the upper bound of the Marčenko-Pastur (MP) distribution. These strategies were tested in simulations with known ground truth and applied for denoising diffusion MRI data acquired using pre-clinical (16.4T) and clinical (3T) MRI scanners. In synthetic phantoms, MPPCA denoising failed to denoise spatially correlated data, while GPCA and TPCA better classified components as dominated by signal/noise. In cases where the noise variance was not accurately estimated (as can be the case in many practical scenarios), TPCA still provides excellent denoising performance. Our experiments in pre-clinical diffusion data with highly corrupted by spatial correlated noise revealed that both GPCA and TPCA robustly denoised the data while MPPCA denoising failed. In in vivo diffusion MRI data acquired on a clinical scanner in healthy subjects, MPPCA weakly removed noised, while TPCA was found to have the best performance, likely due to misestimations of the noise variance. Thus, our work shows that these novel denoising approaches can strongly benefit future pre-clinical and clinical MRI applications.

Accurate expression of neck motion signal by piezoelectric sensor data analysis

The development of high-precision sensors using flexible piezoelectric materials has the advantages of high sensitivity, high stability, good durability, and lightweight. The main problem with sensing equipment is low sensitivity, which is due to the mismatch between materials and analysis methods, resulting in the inability to effectively eliminate noise. To address this issue, we developed the denoising analysis method to motion signals captured by a flexible piezoelectric sensor fabricated from poly(l-lactic acid) (PLLA) and polydimethylsiloxane (PDMS) materials. Experimental results demonstrate that this improved denoising method effectively removes noise components from neck muscle motion signals, thus obtaining high-quality, low-noise motion signal waveforms. Wavelet decomposition and reconstruction is a signal processing technique that involves decomposing a signal into different scales and frequency components using wavelets and then selectively reconstructing the signal to emphasize specific features or eliminate noise. The study employed the sym8 wavelet basis for wavelet decomposition and reconstruction. In the denoised signals, a high degree of stability and periodic peaks are distinctly manifested, while amplitude and frequency differences among different types of movements also become noticeably visible. As a result of this study, we are enabled to accurately analyze subtle variations in neck muscle motion signals, such as nodding, shaking the head, neck lateral flexion, and neck circles. Through temporal and frequency domain analysis of denoised motion signals, differentiation among various motion states can be achieved. Overall, this improved analytical approach holds broad application prospects across various types of piezoelectric sensors, such as healthcare monitoring, sports biomechanics.

A Harmonic and Interharmonic Detection Method for Power Systems Based on Enhanced SVD and the Prony Algorithm

To address the problem of harmonic pollution in power systems, a harmonic and interharmonic detection method based on the adaptive order and the dominant factor algorithms is proposed. The proposed method greatly improves the accuracy and precision of harmonic detection, overcoming the notorious problem of high sensitivity to noise of the traditional Prony algorithm that often leads to unsatisfactory detection results. In the proposed method, the “adaptive order determination” algorithm is first used to determine the optimal order of Singular Value Decomposition (SVD) denoising, resulting in a more accurate distinction between signal and noise components. Then, signal reconstruction is carried out to effectively remove noise components to enhance the denoising ability of SVD. This mitigates the Prony algorithm’s high sensitivity to noise and greatly reduces the amplitude of false components in the fitting results. Finally, the dominant factor algorithm is applied to accurately screen out the non-false components in Prony’s fitting results. Simulation results show that the proposed method can effectively reduce signal noise in different noise environments with noise intensities ranging from 5 dB to 30 dB, achieving an average signal-to-noise ratio improvement of around 20 dB. Meanwhile, the identification and screening results of harmonic and interharmonic components in the signal are accurate and reliable, with detection errors in amplitude, frequency, and phase at around 0.5%, 0.01%, and 0.5%, respectively. Overall, the proposed method is well suited for detecting harmonics and interharmonics in power systems under various noise environments.

Study on Denoising Method of Vibration Signal Induced by Tunnel Portal Blasting Based on WOA-VMD Algorithm

Because of the impact of the complex environment of tunnel portals, the measured blasting vibration signals in a tunnel portal contains a lot of high-frequency noise. To achieve effective noise reduction, a novel method of noise reduction for blasting vibration signals based on the whale optimization algorithm (WOA) optimized with the variational mode decomposition (VMD) algorithm was proposed. The WOA algorithm is used to optimize globally for the mode number K and penalty factor α of VMD for measured signals and to determine the optimal parameters of [K, α], and to obtain the intrinsic mode function (IMF). Multi-scale permutation entropy (MPE) was used to identify and remove noise components in IMF, and then the reserved IMF was reconstructed to achieve a denoised signal. The method is applied to the blasting vibration analysis of the Xiali tunnel of the Jixin expressway in the Henan Province. Results indicate that the novel method can acquire the optimal decomposition mode number and identify the high frequency noise. Its denoising effect is better than the conventional VMD algorithm and the complete ensemble empirical mode decomposition with adaptive ability (CEEMDAN), which verifies the self-adaptivity and effectiveness of the WOA-VMD denoising method.

Application of CEEMDAN combined wavelet threshold denoising algorithm to suppressing scattering cluster in underwater lidar

<sec>The echo of underwater lidar often contains a significant quantity of scattering clutters. In order to effectively suppress this scattering clutter and improve the ranging accuracy of underwater lidar, a novel denoising method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold denoising is proposed.</sec><sec>The CEEMDAN-wavelet threshold denoising algorithm uses the correlation coefficient to select intrinsic mode function (IMF) components obtained from the CEEMDAN decomposition. The IMFs, which are more closely related to the original signal, are selected. Then, the wavelet thresholding denoising algorithm is applied to each of the selected IMFs to perform additional denoising. For each IMF component, specific threshold values are calculated based on their frequency and amplitude characteristics. Subsequently, the wavelet coefficients of the IMF components are processed by using these threshold values. Finally, the denoised IMF components are combined and reconstructed to obtain the final denoised signal. Applying the wavelet threshold denoising algorithm to IMF components can effectively remove noise components that cannot be removed by traditional CEEMDAN partial reconstruction methods. By using the threshold value calculated based on the characteristics of each IMF component, the wavelet thresholding denoising process is improved in comparison with directly using a single threshold value. This approach enhances the algorithm’s adaptability and enables more effective removal of noise from the signal.</sec><sec>We apply the proposed method to underwater ranging experiments. A 532 nm intensity-modulated continuous wave laser is used as a light source. Ranging is performed for a target in water with varying attenuation coefficients. A white polyvinyl chloride (PVC) reflector is used as a target. When the correlation extreme value is directly used to determine the delay at a distance of 3.75 attenuation length, it results in a ranging error of 19.2 cm. However, after applying the proposed method, the ranging error is reduced to 6.2 cm, thus effectively improving the ranging accuracy. These results demonstrate that the method has a significant denoising effect in underwater lidar system.</sec>

QEEG‐based discernment of dementia pathologies through machine learning: Lewy body and Alzheimer’s disease

AbstractBackgroundAlzheimer’s disease is the most common cause of dementia, which destroys nerve cells in the brain through the formation and accumulation of amyloid plaques. However, various other pathologies of dementia also exist, such as Lewy body pathology where the destruction is caused by the accumulation of Lewy body peptides. Differing dementia pathologies carry distinguishing symptoms and treatment methods; hence we cannot disregard the importance of correct identification of the pathology.Currently, the correct pathology can be identified through positron emission tomography (PET), which is expensive and results in exposure to harmful ionizing radiation. Therefore, the present study aims to overcome the disadvantages of current screening methods and distinguish the quantitative electroencephalography (QEEG) of two dementia pathologies through machine learning.MethodEEG data employed in the present study were recorded at the 19 electrode locations defined by the international 10‐20 system, in eyes‐closed resting‐state condition. The acquired data were then re‐referenced to a common average reference. Hereafter, bandpass filtering at 0.1‐45.5Hz, bad epoch rejection and independent component analysis (ICA) was performed to remove noise components from the data. Standardized low resolution brain electromagnetic tomography (sLORETA) was further used for mathematical estimation of source cortical activity, yielding data for 68 cortical regions based on the Desikan‐Killiany atlas.Gamma wave (30‐45Hz) features were excluded from the data due to is vulnerability to electromyography. Further feature reduction was also performed, through the designated criteria on p‐value threshold (0.05) and feature importance threshold determined by shapely values. The final dataset (N = 104; 30 ADD, 74 DLB) was established, where 20% of the data (N =21; 6 ADD, 15 DLB) were randomly selected and excluded as test data for model verification.ResultThe final machine learning model trained and tested through the dataset yielded test accuracy at 85.7% with ADD sensitivity at 83.3%, DLB sensitivity at 86.7%. The 5 fold cross validation accuracy was at 82.1%.ConclusionThe model developed in the present study yielded a promising classification performance, through EEG data which is cheap and harmless to record. Such QEEG‐based classification models may sufficiently replace the PET in future, resolving current disadvantages.

Deep residual-SVD network for brain image registration

Objective. Medical image registration aims to find the deformation field that can align two images in a spatial position. A medical image registration method based on U-Net architecture has been proposed currently. However, U-Net architecture has few training parameters, which leads to weak learning ability, and it ignores the adverse effects of image noise on the registration accuracy. The article aims at addressing the problem of weak network learning ability and the adverse effects of noisy images on registration. Approach. Here we propose a novel unsupervised 3D brain image registration framework, which introduces the residual unit and singular value decomposition (SVD) denoising layer on the U-Net architecture. Residual unit solves the problem of network degradation, that is, registration accuracy becomes saturated and then degrades rapidly with the increase in network depth. SVD denoising layer uses the estimated model order for SVD-based low-rank image reconstruction. we use Akaike information criterion to estimate the appropriate model order, which is used to remove noise components. We use the exponential linear unit (ELU) as the activation function, which is more robust to noise than other peers. Main results. The proposed method is evaluated on the publicly available brain MRI datasets: Mindboggle101 and LPBA40. Experimental results demonstrate our method outperforms several state-of-the-art methods for the metric of Dice Score. The mean number of folding voxels and registration time are comparable to state-of-the-art methods. Significance. This study shows that Deep Residual-SVD Network can improve registration accuracy. This study also demonstrate that the residual unit can enhance the learning ability of the network, the SVD denoising layer can denoise effectively, and the ELU is more robust to noise.

The statistical characteristics of knock signal waveforms

Individual instances of the knock resonant response are easy to acquire but these are subject to noise and vary considerably from cycle to cycle due to random variations in the knock process. This work provides a new way to quantify and model the stochastic properties of knock signals, capturing both the time domain resonant characteristics within a cycle as well as the random variations from cycle to cycle. A new phase alignment method enables the ensemble mean knock waveform to be identified from the data which also removes noise components without the need for narrowband filtering. This ensemble waveform shows the empirical characteristics of knock onset, decay, and frequency slurring within the cycle as the gas expands and cools. The phase-aligned cyclic variations of the knock waveform are also shown to approximate a (time-varying) dual-Gaussian distribution, and fitting such a model to the data enables the statistical properties of the dataset as a whole to be decomposed into separate knocking and non-knocking populations providing further insight into the knock process. The technique is applied both to filtered cylinder pressure signals and to accelerometer-based knock signals, and the results are compared and contrasted.

Analysis of Denoising Methods of Underwater Acoustic Pulse Signal Based on Wavelet and Wavelet Packet

Underwater acoustic countermeasures include active sonar countermeasures and passive sonar countermeasures. In order to achieve the best operational efficiency of jamming equipment, the generation of jamming signals must adapt to the development trend of underwater acoustic detection technology-nonlinear time-varying and broadband characteristics. Feature extraction is to map the high-dimensional original data to the low-dimensional transformation space through a certain mapping relationship, which can suppress a large amount of redundant information in the data and highlight the category information of the data. One of the applications of wavelet analysis in signal analysis and processing is to remove noise components from signals. In practical engineering applications, sampled signals are inevitably polluted by various noises and interferences. Through the analysis of noise characteristics, it can be seen that the wavelet denoising method is very effective in removing signal noise. In this paper, aiming at the signals with different spectrum distribution, we use various methods to de-noise in order to find the de-noising methods suitable for underwater acoustic pulse signals with different frequency characteristics, and thus find an effective means to detect signals.

An automatic feature selection and classification framework for analyzing ultrasound kidney images using dragonfly algorithm and random forest classifier

In medical imaging, the automatic diagnosis of kidney carcinoma has become more difficult because it is not easy to detect by physicians. Pre-processing is the first identification method to enhance image quality, remove noise and unwanted components from the backdrop of the kidneys image. The pre-processing method is essential and significant for the proposed algorithm. The objective of this analysis is to recognize and classify kidney disturbances with an ultrasound scan by providing a number of substantial content description parameters. The ultrasound pictures are prepared to protect the interest pixels before extracting the feature. A series of quantitative features were synthesized of each images, the principal component analysis was conducted for minimizing the number of features to produce set of wavelet-based multi-scale features. Dragonfly algorithm (DFA) was executed in this method. In the proposed work, the design and training of a random decision forest classifier and selected features are implemented. The classification of e-health information using ideal characteristics is used by the RF classifier. The proposed technique is activated in MATLAB/simulink work site and the experimental results show that the peak accuracy of the proposed technique is 95.6% using GWO-FFBN techniques compared to other existing techniques.

The neuronal associations of respiratory-volume variability in the resting state

The desire to enhance the sensitivity and specificity of resting-state (rs-fMRI) measures has prompted substantial recent research into removing noise components. Chief among contributions to noise in rs-fMRI are physiological processes, and the neuronal implications of respiratory-volume variability (RVT), a main rs-fMRI-relevant physiological process, is incompletely understood. The potential implications of RVT in modulating and being modulated by autonomic nervous regulation, has yet to be fully understood by the rs-fMRI community. In this work, we use high-density electroencephalography (EEG) along with simultaneously acquired RVT recordings to help address this question. We hypothesize that (1) there is a significant relationship between EEG and RVT in multiple EEG bands, and (2) that this relationship varies by brain region. Our results confirm our first hypothesis, although all brain regions are shown to be equally implicated in RVT-related EEG-signal fluctuations. The lag between RVT and EEG is consistent with previously reported values. However, an interesting finding is related to the polarity of the correlation between RVT and EEG. Our results reveal potentially two main regimes of EEG-RVT association, one in which EEG leads RVT with a positive association between the two, and one in which RVT leads EEG but with a negative association between the two. We propose that these two patterns can be interpreted differently in terms of the involvement of higher cognition. These results further suggest that treating RVT simply as noise is likely a questionable practice, and that more work is needed to avoid discarding cognitively relevant information when performing physiological correction rs-fMRI.

Видалення шумових компонент інформаційних сигналів за допомогою ортогональних вейвлет-перетворень

Видалення шумових компонент інформаційних сигналів за допомогою ортогональних вейвлет-перетворень

Application Research of a New Adaptive Noise Reduction Method in Fault Diagnosis

The feature extraction of composite fault of gearbox in mining machinery has always been a difficulty in the field of fault diagnosis. Especially in strong background noise, the frequency of each fault feature is different, so an adaptive time-frequency analysis method is urgently needed to extract different types of faults. Considering that the signal after complementary ensemble empirical mode decomposition (CEEMD) contains a lot of pseudo components, which further leads to misdiagnosis. The article proposes a new method for actively removing noise components. Firstly, the best scale factor of multi-scale sample entropy (MSE) is determined by signals with different signal to noise ratios (SNRs); secondly, the minimum value of a large number of random noise MSE is extracted and used as the threshold of CEEMD; then, the effective Intrinsic mode functions(IMFs) component is reconstructed, and the reconstructed signal is CEEMD decomposed again; finally, after multiple iterations, the MSE values of the component signal that are less than the threshold are obtained, and the iteration is terminated. The proposed method is applied to the composite fault simulation signal and mining machinery vibration signal, and the composite fault feature is accurately extracted.

Normal voting tensor based points set denoising

<h2>Abstract</h2> Point set denoising is a vital pre-processing step in several computer graphics and medical imaging applications. This article introduces an effective point set denoising software, which is able to remove noise components from the input point set without blurring important sharp features. This software is capable of producing a high fidelity point set, which is desired in several medical applications as it controls the volume shrinkage during the denoising process.

A Reliable Fault Diagnosis Method for a Gearbox System with Varying Rotational Speeds.

The vibration signals of gearbox gear fault signatures are informative components that can be used for gearbox fault diagnosis and early fault detection. However, the vibration signals are normally non-linear and non-stationary, and they contain background noise caused by data acquisition systems and the interference of other machine elements. Especially in conditions with varying rotational speeds, the informative components are blended with complex, unwanted components inside the vibration signal. Thus, to use the informative components from a vibration signal for gearbox fault diagnosis, the noise needs to be properly distilled from the informational signal as much as possible before analysis. This paper proposes a novel gearbox fault diagnosis method based on an adaptive noise reducer–based Gaussian reference signal (ANR-GRS) technique that can significantly reduce noise and improve classification from a one-against-one, multiclass support vector machine (OAOMCSVM) for the fault types of a gearbox. The ANR-GRS processes the shaft rotation speed to access and remove noise components in the narrowbands between two consecutive sideband frequencies along the frequency spectrum of a vibration signal, enabling the removal of enormous noise components with minimal distortion to the informative signal. The optimal output signal from the ANR-GRS is then extracted into many signal feature vectors to generate a qualified classification dataset. Finally, the OAOMCSVM classifies the health states of an experimental gearbox using the dataset of extracted features. The signal processing and classification paths are generated using the experimental testbed. The results indicate that the proposed method is reliable for fault diagnosis in a varying rotational speed gearbox system.

Speech enhancement - an enhanced principal component analysis (EPCA) filter approach

Speech enhancement aims at improving the quality of speech in a noisy environment, also in applications such as speech recognition systems, hearing aids, teleconferencing, etc. Researchers have suggested various techniques to improve the quality of speech signal in a noisy environment. Kalman filtering technique showed better performance and was found remarkable in removing noise components. Therefore, this proposed method employs an improved version of the Kalman filter for the removal of noise in speech signals. A Principal Component Analysis (PCA) developed Kalman filter is proposed for enhancing speech intelligibility and quality. The simulation results prove the recommended technique of enhancing speech signals. It is better in performance compared with Modulation Compressive Sensing, Compressed Sensing Frequency, Wiener filter, and Log Minimum Mean Square Error (LogMMSE) in terms of Short-Time Objective Intelligibility (STOI), Segmental Signal to Noise Ratio (SSNR) and Perceptual Evaluation of Speech Quality (PESQ) for different noise levels.

A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise, minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics. Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing. Owing to the complexity of marine environment and the particularity of underwater acoustic channel, noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing. In order to solve the dilemma, we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), minimum mean square variance criterion (MMSVC) and least mean square adaptive filter (LMSAF). This noise reduction technique, named CEEMDAN-MMSVC-LMSAF, has three main advantages: (i) as an improved algorithm of empirical mode decomposition (EMD) and ensemble EMD (EEMD), CEEMDAN can better suppress mode mixing, and can avoid selecting the number of decomposition in variational mode decomposition (VMD); (ii) MMSVC can identify noisy intrinsic mode function (IMF), and can avoid selecting thresholds of different permutation entropies; (iii) for noise reduction of noisy IMFs, LMSAF overcomes the selection of decomposition number and basis function for wavelet noise reduction. Firstly, CEEMDAN decomposes the original signal into IMFs, which can be divided into noisy IMFs and real IMFs. Then, MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs. Finally, both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained. Compared with other noise reduction techniques, the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals, which has the better noise reduction effect and has practical application value. CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection, feature extraction, classification and recognition of underwater acoustic signals.

Planetary gear fault diagnosis using stacked denoising autoencoder and gated recurrent unit neural network under noisy environment and time-varying rotational speed conditions

Owing to high transmission ratio and compact structure, planetary gearboxes are widely used in industrial applications. However, due to running under noisy environment and time-varying rotational speed conditions, the acquired vibration signals are nonstationary, which seriously degrade the performance of fault diagnosis methods of planetary gears. To address this problem, a fault diagnosis method of planetary gears using a stacked denoising autoencoder (SDAE) and a gated recurrent unit neural network (GRUNN) is proposed in this paper. First, a hybrid model based on SDAE and GRUNN is developed to remove noise components from input data and process pre- and post-correlation time-series data. The training samples for planetary gear fault diagnosis are regarded as the input data of the developed hybrid model to automatically extract robust fault features. Then, the training process of the developed hybrid model is presented. The Adam optimization algorithm is utilized to optimize the parameters, and the dropout technique is employed to prevent from overfitting. Finally, a softmax classifier is employed to identify planetary gear states in test samples. The effectiveness of the proposed method is validated through a fault diagnosis experiment of planetary gears. The experimental results show that the proposed method possesses strong anti-noise ability and adaptability to time-varying rotational speed.