Signal Denoising Research Articles

A hybrid denoising method for gyroscopes based on multiple screening

To reduce the random error of microelectromechanical system (MEMS) gyroscope, a hybrid method combining improved empirical mode decomposition (EMD) and least squares algorithm (LS) is proposed. Firstly, based on the multiple screening mechanism, intrinsic mode functions (IMFs) from the first decomposition are divided into noise IMFs, strong noise mixed IMFs, weak noise mixed IMFs and signal IMFs. Secondly, according to their characteristics, they are processed again. IMFs from the second decomposition are divided into noise IMFs and signal IMFs. Finally, useful signal is gathered to obtain the final denoising signal. Compared with some other denoising methods proposed in recent years, the experimental results show that the proposed method has obvious advantages in suppressing random error, greatly improving the signal quality and improving the accuracy of inertial navigation.

Noise Reduction Method for the Vibration Signal of Reactor CRDM Based on CEEMDAACN-SK

The reactor control rod drive mechanism (CRDM) controls the startup, shutdown and power of the reactor; it is one of the key pieces of equipment to ensure the normal operation of the reactor. CRDM is complex, mainly composed of stator, rotor, bearing, roller, etc. The characteristic analysis of the vibration monitoring signal is one of the important methods of the CRDM state evaluation. In view of the characteristics of large noise interference and the difficulty in analyzing the vibration monitoring signal of CRDM, this paper proposes a noise reduction method for the vibration signal of CRDM based on complete ensemble empirical mode decomposition with adaptive amplitude correction noise and spectral kurtosis (CEEMDAACN-SK), which can deeply reduce the vibration signal of CRDM. Firstly, the proposed CEEMDAACN algorithm is used to decompose the vibration signal of CRDM to obtain multiple intrinsic mode functions (IMF). Then, the spectral kurtosis of each IMF component is analyzed to obtain the spectral kurtosis map of each IMF component, which is compared with the spectral kurtosis map of the original signal. Finally, the denoising reconstruction of the signal is carried out to obtain the final denoising signal. Through experimental analysis, the performance of the proposed CEEMDAACN-SK denoising algorithm is better than the complete ensemble empirical mode decomposition with adaptive noise and spectral kurtosis (CEEMDAN-SK) algorithm in terms of results. The method proposed in this paper can be applied not only to the vibration signal noise reduction of CRDM, but also to other equipment and fields, such as nuclear power main circulation pump and the chemical industry.

Noise reduction and characteristic analysis of fluid signal in the jet impact‐negative pressure deamination reactor based on wavelet transform

AbstractThe jet impact‐negative pressure deamination reactor (JI‐NPDR) is a new type of continuous and efficient deamination equipment. The study of the random flow pattern of porous jet impingement in the reactor under negative pressure conditions is an important issue. In this work, the signal processing method based on wavelet transform is used to analyze the characteristics of random flow signals in the reactor. Meanwhile, an analog similar signal is built and three sets of Gaussian white noise with various signal‐to‐noise ratios are employed via the MATLAB platform. Based on the adjustment of threshold function, threshold, decomposition level and other parameters of wavelet transform, the noise ratio (SNR) and mean squared error (MSE) are used to evaluate the wavelet denoising effect. And then, the optimal denoising scheme for the obtained signal will be applied in processing the vacuum flow signal collected inside the deamination reactor. Subsequently, the 8‐layer wavelet decomposition is investigated by using sym7 as the wavelet basis, soft threshold function, and heursure threshold for signal denoising. Then, the analog signal is fed back through the results of the actual signal denoising, and the number of wavelet decomposition layers is adjusted from 8 to 9 layers to optimize the original wavelet denoising combination. By analyzing the low‐frequency and high‐frequency parts of the signal spectrum before and after denoising, it was found that wavelet transform can effectively denoise the fluid signal in the reactor.

Optimized variational mode decomposition algorithm based on adaptive thresholding method and improved whale optimization algorithm for denoising magnetocardiography signal

In the diagnosis and treatment of cardiac diseases, magnetocardiography (MCG) technology is characterized by non-invasive, non-contact, and high precision. Therefore, it has currently become a research hotspot in the field of new medical technologies. However, due to the weak signal of the magnetocardiography, the noise needs to be filtered after acquisition. In this paper, an optimized variational mode decomposition algorithm based on the improved threshold method and the improved whale optimization algorithm (WOA) is proposed to process the MCG signal. In order to improve the denoising accuracy, the improved whale optimization algorithm, VMD algorithm, and the improved threshold method are combined. Firstly, the correlation coefficients are obtained by the improved whale optimization algorithm to decompose the IMFs, then the baseline drift is removed by using the moving average method for the low-frequency IMFs, and then the improved thresholding algorithm is applied to each IMF. Finally, the denoised signal is obtained by integration. Experimental tests show that the algorithm has good denoising performance compared with similar algorithms and can filter environmental noise as much as possible without changing the original signal information. The proposed method has the highest Signal-to-Noise Ratio improvement (SNRimp) and Correlation Coefficient (CC) and the lowest Percentage Root Mean Square Difference (PDR). Also, the method is validated in real MCG signal processing. The proposed algorithm can be applied in the field of signal denoising, and it has a wide range of application backgrounds.

An Improved Denoising Method for Fault Vibration Signals of Wind Turbine Gearbox Bearings

Vibration monitoring (VM) is an important tool for fault diagnosis in key components of wind turbine gearboxes (WTGs). However, due to the influence of white noise and random interference, it is difficult to realize high-quality denoising of WTG-VM signals. To overcome this limitation, a novel joint denoising method for fault WTG-VM signals is proposed in this article, which we have named EWTKC-SVD. First, the empirical wavelet transform (EWT) boundary exploration method is used to optimize frequency band allocation and obtain the multiple intrinsic mode functions (IMFs). Second, the sensitive IMFs are selected according to the calculated correlation coefficient and kurtosis index, avoiding IMF redundancy. Finally, the fault WTG-VM signals are obtained using SVD denoising. Using this approach, the proposed method realizes high-quality denoising of WTG-VM signals. Furthermore, it also effectively solves the existing problems of conventional methods, namely, inefficient IMF selection, high noise, false frequencies, mode mixing, and end effect. Finally, the effectiveness, superiority, and reliability of the proposed method are proved using simulation and practical case results.

Pipeline Leak AE Signal Denoising Based on Improved SSA-K-α Index-VMD-MD

Pipeline Leak AE Signal Denoising Based on Improved SSA-<i>K</i>-α Index-VMD-MD

Microseismic Signal Denoising Based on Variational Mode Decomposition With Adaptive Non-local Means Filtering

Microseismic Signal Denoising Based on Variational Mode Decomposition With Adaptive Non-local Means Filtering

JCCM: Joint conformer and CNN model for overlapping radio signals recognition

AbstractOverlapping radio signals recognition is attracting more attention as the development and ubiquitous application of radio technologies. The traditional blind signal separation (BSS) method is mostly not effective when both radio propagation effects and low signal‐to‐noise ratio (SNR) scenarios are taken into consideration. In this letter, joint conformer and CNN model (JCCM) is proposed to separate and recognize the overlapping radio signals which are also unknown by the monitor node. JCCM utilizes the attention mechanism of conformer to encode signal spectrum into feature maps and decodes the feature maps into signal component proportions by perceiving the global and local features through convolutional neural networks (CNN). In addition, a signal preprocessing module is designed including MinPool and AvgPool layers for signal denoising. JCCM is evaluated in terms of signal separation, scaling preservation and principal signal classification experiments. The results show that JCCM has better performance especially in low SNR conditions.

When FrFT meets quadratic frequency modulation functions—A novel tool for nonstationary signals and time-varying systems

The fractional Fourier transform (FrFT) is a general form of the Fourier transform, which has provided a theoretical framework for linear frequency modulation signal processing. The quadratic frequency modulation (QFM) function is a generalization of the linear frequency modulation function, which balances the precision and complexity of nonstationary signals in practical applications. However, there is no framework for analyzing and processing QFM signals, which limits the applications of QFM signal model. The QFMFrFT, a generalization of the FrFT, is developed in this study to tackle this issue. The impact of the QFMFrFT on the time-frequency distribution explains its usefulness in sampling, denoising, and filter design of QFM signals. Two one-place statistical functions are defined for QFM-type stochastic signals, of which the input-output relationships of a linear time-varying system are discussed. Using these relations, a linear time-varying matched filter is designed. Two types of generalized convolution operators are designed associated with the QFMFrFT, which allow a multiplicative realization of a time-varying system. The QFM signal is demonstrated as a singular function of this type of linear time-varying system, which inspires a multi-carrier technique in spread-spectrum communications. Simulations verify the applications of the QFMFrFT in parameter estimation, matched filter design, and communications.

Adaptive Signal-to-Noise Ratio Indicator for Wearable Bioimpedance Monitoring.

Bioimpedance monitoring is an increasingly important non-invasive technique for assessing physiological parameters such as body composition, hydration levels, heart rate, and breathing. However, sensor signals obtained from real-world experimental conditions invariably contain noise, which can significantly degrade the reliability of the derived quantities. Therefore, it is crucial to evaluate the quality of measured signals to ensure accurate physiological parameter values. In this study, we present a novel wrist-worn wearable device for bioimpedance monitoring, and propose a method for estimating signal quality for sensor signals obtained on the device. The method is based on the continuous wavelet transform of the measured signal, identification of wavelet ridges, and assessment of their energy weighted by the ridge duration. We validate the algorithm using a small-scale experimental study with the wearable device, and explore the effects of variables such as window size and different skin/electrode coupling agents on signal quality and repeatability. In comparison with traditional wavelet-based signal denoising, the proposed method is more adaptive and achieves a comparable signal-to-noise ratio.

Improved signal processing for bearing fault diagnosis in noisy environments using signal denoising, time–frequency transform, and deep learning

Improved signal processing for bearing fault diagnosis in noisy environments using signal denoising, time–frequency transform, and deep learning

Consistent physical frequencies in time-frequency analysis

Wavelet transforms have been studied extensively for a wide variety of applications such as signal compression and signal denoising. Wavelet transforms have also been examined for the detection of transient signals. However, wavelet levels (and corresponding pseudo-frequencies) are inherently dependent on the sampling rate of the analyzed data. The work presented herein examines how wavelet analysis can produce inconsistent data representations for the same analytical signal digitized at different sampling rates. Results are compared to time-octave and other time-frequency representations to identify methodologies which produce consistent characterization of physical frequencies. Similarities and difference between approaches are discussed.

Deep neural network-based speech enhancement for cochlear implants

Noisy conditions make understanding speech with a cochlear implant (CI) difficult. Speech enhancement (SE) algorithms based on signal statistics can be beneficial in stationary noise, but rarely provide benefit in modulated multi-talker babble. Current approaches using deep neural networks (DNNs) rely on a data driven approach for training and promise improvements in a wide variety of noisy conditions. In this study a DNN-based SE algorithm was evaluated in CI listeners. The network was trained on a large database of publicly available recordings. A double-blinded acute evaluation was conducted with 10 adult CI users by assessing intelligibility and quality of speech embedded in a range of different noise types. The DNN-based SE algorithm provided significant benefits in speech intelligibility and sound quality in all noise types that were evaluated. Speech reception thresholds, the SNR required to understand 50% of the speech material, improved by 1.8 to 3.5 dB depending on noise type. Benefits varied with the SNR of the input signal and the mixing ratio parameter that was used to combine the original and de-noised signals. The results demonstrate that DNN-based SE can provide benefits in natural, modulated noise conditions, which is critical to CI users in their day-to-day environment.

RV Reducer Torque Signal Denoising Research Based VMD

The RV reducer is a core component in industrial robots, but its performance is facing higher demands due to the expanding application scenarios. Accurately obtaining the torque signal is crucial for achieving precise control of the RV reducer, as torque is an important performance indicator. In this paper, a VMD-based noise reduction method for RV reducer torque signals is proposed, in which the VMD algorithm is used to decompose the original signal, and the IMFs are selected for signal reconstruction by fusing the correlation index and sample entropy. Experimental results show that the proposed method achieves higher SNR and lower MSE after noise reduction, demonstrating its effectiveness. This method provides a foundation for the precise control of RV reducers.

A Review of Approaches for Mitigating Effects from Variable Operational Environments on Piezoelectric Transducers for Long-Term Structural Health Monitoring.

Extending the service life of ageing infrastructure, transportation structures, and processing and manufacturing plants in an era of limited resources has spurred extensive research and development in structural health monitoring systems and their integration. Even though piezoelectric transducers are not the only sensor technology for SHM, they are widely used for data acquisition from, e.g., wave-based or vibrational non-destructive test methods such as ultrasonic guided waves, acoustic emission, electromechanical impedance, vibration monitoring or modal analysis, but also provide electric power via local energy harvesting for equipment operation. Operational environments include mechanical loads, e.g., stress induced deformations and vibrations, but also stochastic events, such as impact of foreign objects, temperature and humidity changes (e.g., daily and seasonal or process-dependent), and electromagnetic interference. All operator actions, correct or erroneous, as well as unintentional interference by unauthorized people, vandalism, or even cyber-attacks, may affect the performance of the transducers. In nuclear power plants, as well as in aerospace, structures and health monitoring systems are exposed to high-energy electromagnetic or particle radiation or (micro-)meteorite impact. Even if environmental effects are not detrimental for the transducers, they may induce large amounts of non-relevant signals, i.e., coming from sources not related to changes in structural integrity. Selected issues discussed comprise the durability of piezoelectric transducers, and of their coupling and mounting, but also detection and elimination of non-relevant signals and signal de-noising. For long-term service, developing concepts for maintenance and repair, or designing robust or redundant SHM systems, are of importance for the reliable long-term operation of transducers for structural health monitoring.

A novel deep wavelet convolutional neural network for actual ECG signal denoising

Recently, more than 80% of sudden cardiac death is caused by arrhythmia, whose incidence has increased rapidly. In the actual wearable device acquisition process, ECG recordings are polluted by many different types of noise, mainly including baseline drift (BW), muscle artifact (MA) and electrode motion (EM). Therefore, the effective de-noising of ECG signals plays an important role in the subsequent accurate arrhythmia diagnosis. Nowadays, traditional filtering methods often only focus on one or two kinds of noise. When encountering different noises, different filtering algorithms need to be used, resulting in insufficient generalization ability of the algorithms. Aiming to effectively denoising ECG signals, this paper proposes a novel signal denoising method based on deep wavelet convolutional neural network. Inspired by the structure of the de-noising self-encoder, the convolution layers are used to replace the simple full-connected layers to build the encoder and decoder. Based on automatic feature extraction in convolution layer, the discrete wavelet transform is used to convert the signal into high-frequency and low-frequency components for replacing pooling layers to compress the input data and fully preserve the effective information. Finally, based on the dataset under the real noise conditions, compared with the existing methods, the proposed denoising model has better denoising performance, which reduces 0.194 RMSE and improves 5.99 SNR on average. Thus, the proposed denoising model has good potential in wearable devices.

Estimating the greenhouse gas emission factor of motorcycles under real-world driving conditions in Hanoi: a case study

The lack of country-specific greenhouse gas (GHG) emission factors vehicles in Vietnam makes the emissions inventory in the transport sector more difficult. This study developed the country-specific GHG emission factor in terms of CO2 equivalent for motorcycles (MCs) in Hanoi to overcome the above impediment. The real-world driving data, instantaneous speed versus time, were collected on the 30 road routes in Hanoi using a GPS device. A filtering process of nine-step was designed to repair misleading speed values and denoise signals before entering the International Vehicle Emissions (IVE) model. The typical GHG emission factor for the MC fleet in Hanoi was approximately 90.83 g/km. This study also found a remarkable difference in the MC’s specific power-related bins distribution between the real-world driving characteristics of the MC in Hanoi and the ones of the World Motorcycle Test Cycle. This contributes to reconfirming the necessity of developing the GHG emission factor based on the very real-world driving data of Vietnam

Unsupervised classification of the spectrogram zeros with an application to signal detection and denoising

Spectrogram zeros, originated by the destructive interference between the components of a signal in the time–frequency plane, have proven to be a relevant feature to describe the time-varying frequency structure of a signal. In this work, we first introduce a classification of the spectrogram zeros in three classes that depend on the nature of the components that interfere to produce them. Then, we describe an algorithm to classify these points in an unsupervised way, based on the analysis of the stability of their location with respect to additive noise. Finally, potential uses of the classification of zeros of the spectrogram for signal detection and denoising are investigated, and compared with other methods on both synthetic and real-world signals.

Denoising of discrete-time chaotic signals using echo state networks

Noise reduction is a relevant topic in the application of chaotic signals in communication systems, in modeling biomedical signals or in time series forecasting. In this paper an echo state network (ESN) is employed to denoise a discrete-time chaotic signal corrupted by additive white Gaussian noise. The choice of applying ESNs in this context is motivated by their successful exploitation for the separation and prediction of continuous-time chaotic signals. Our results show that the processing gain of the ESN is higher than the one obtained using a Wiener filter for chaotic signals generated by a skew-tent map. Since the power spectral density of the orbits in this map is well known, it was possible to analyze how the processing gain of the ESN in the denoising process varies according to the spectral characteristics of the chaotic signals.

Noise reduction and QRS detection in ECG signal using EEMD with modified sigmoid thresholding.

Novel noise reduction and QRS detection algorithms in Electrocardiogram (ECG) signal based on Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and the Modified Sigmoid Thresholding Function (MSTF) are proposed in this paper. EMD and EEMD algorithms are used to decompose the noisy ECG signal into series of Intrinsic Mode Functions (IMFs). Then, these IMFs are thresholded by the MSTF for reduction of noises and preservation of QRS complexes. After that, the thresholded IMFs are used to obtain the clean ECG signal. The characteristic points P, Q, R, S and T peaks are detected using peak detection algorithm. The proposed methods are validated through experiments on the MIT-BIH arrhythmia database and Additive White Gaussian Noise (AWGN) is added to the clean ECG signal at different input SNR (SNR in). Standard performance parameters output SNR (SNR out), mean square error (MSE), root mean square error (RMSE), SNR improvement (SNR imp) and percentage root mean square difference (PRD) are employed for evaluation of the efficacy of the proposed methods. The results showed that the proposed methods provide significant quantitative and qualitative improvements in denoising performance, compared with existing state-of-the-art methods such as wavelet denoising, conventional EMD (EMD-Conv), conventional EEMD (EEMD-Conv, Stockwell Transform (ST) and Complete EEMD with Adaptative Noise with hybrid interval thresholding and higher order statistic to select relevant modes (CEEMDAN-HIT). A detail quantitative analysis demonstrate that for abnormal ECG records 207 m and 214 m at input SNR of-2 dB the SNR imp value is 12.22 and 11.58 dB respectively, which indicates that the proposed algorithm can be used as an effective tool for denoising of ECG signals.