Noise Interference Problem Research Articles

Fault diagnosis method of rotating machinery based on MSResNet feature fusion and CAM

To solve the problem of noise interference, it is difficult to extract multi-scale information from complex vibration signals in fault diagnosis with the single-scale convolution kernel of classical deep learning model convolutional neural network (CNN). Therefore, a fault diagnosis method of rotating machinery based on MSResNet feature fusion and CAM is proposed. The residual network (ResNet) and multi-scale convolutional neural network (MSCNN) are combined to extract multi-scale feature information according to convolution kernels with different sizes, so as to avoid the loss of single-scale feature extraction. Make full use of the advantages of the residual network to skip the connection and prevent the feature information extracted by the multi-scale convolution kernel from being lost when the convolution layer propagates forward. In addition, in order to avoid the interference of invalid features after multi-scale information feature fusion, a channel attention mechanism module (CAM) is introduced to screen important features adaptively. The effectiveness of MSResNet-CAM is verified by the bearing data set of Western Reserve University (CWRU) and the data set of QPZZ-II gearbox, and the anti-noise ability is verified by adding noise to the two data sets. The experimental results show that MSResNet-CAM has the characteristics of high fault classification accuracy, good robustness and strong anti-noise ability.

MmWave-RM: A Respiration Monitoring and Pattern Classification System Based on mmWave Radar.

Breathing is one of the body's most basic functions and abnormal breathing can indicate underlying cardiopulmonary problems. Monitoring respiratory abnormalities can help with early detection and reduce the risk of cardiopulmonary diseases. In this study, a 77 GHz frequency-modulated continuous wave (FMCW) millimetre-wave (mmWave) radar was used to detect different types of respiratory signals from the human body in a non-contact manner for respiratory monitoring (RM). To solve the problem of noise interference in the daily environment on the recognition of different breathing patterns, the system utilised breathing signals captured by the millimetre-wave radar. Firstly, we filtered out most of the static noise using a signal superposition method and designed an elliptical filter to obtain a more accurate image of the breathing waveforms between 0.1 Hz and 0.5 Hz. Secondly, combined with the histogram of oriented gradient (HOG) feature extraction algorithm, K-nearest neighbours (KNN), convolutional neural network (CNN), and HOG support vector machine (G-SVM) were used to classify four breathing modes, namely, normal breathing, slow and deep breathing, quick breathing, and meningitic breathing. The overall accuracy reached up to 94.75%. Therefore, this study effectively supports daily medical monitoring.

Hyperspectral Image Denoising Based on Deep and Total Variation Priors

To address the problems of noise interference and image blurring in hyperspectral imaging (HSI), this paper proposes a denoising method for HSI based on deep learning and a total variation (TV) prior. The method minimizes the first-order moment distance between the deep prior of a Fast and Flexible Denoising Convolutional Neural Network (FFDNet) and the Enhanced 3D TV (E3DTV) prior, obtaining dual priors that complement and reinforce each other’s advantages. Specifically, the original HSI is initially processed with a random binary sparse observation matrix to achieve a sparse representation. Subsequently, the plug-and-play (PnP) algorithm is employed within the framework of generalized alternating projection (GAP) to denoise the sparsely represented HSI. Experimental results demonstrate that, compared to existing methods, this method shows significant advantages in both quantitative and qualitative assessments, effectively enhancing the quality of HSIs.

Research and analysis on fault diagnosis of multistage centrifugal pump for mining

Abstract The vibration data collected by the fault experiment are analyzed in the time domain and frequency domain respectively, and the conclusion shows that the frequency characteristics of some vibration data cannot be fully characterized due to noise interference. In order to solve the problem of noise interference, this paper uses the VMD algorithm based on the kurtosis peak to denoise the collected data, constructs wavelet time-frequency map samples through the wavelet transform method to provide input sources for the subsequent convolutional neural network, and uses the image samples for fault diagnosis and identification. [1] Through the convolutional neural network built to diagnose and identify the different states of the multistage centrifugal pump, it can be seen that the recognition accuracy, training time, and recognition time of a single sample of various image samples in the three cases of no noise reduction, noise reduction and a small number of samples can be found through the method of comparative analysis, and compared with the traditional SVM method, the superiority of the convolutional neural network using image recognition can be found.

Dual quaternion hand-eye calibration algorithm for hunter-prey optimization based on twice opposition-learning and random differential variation

In order to solve the problem of noise interference in camera calibration and robot forward kinematics solution, this study proposes a dual quaternion hand-eye calibration algorithm based on twice opposition-learning and random differential variation (ODHPO). The hand-eye calibration equation was innovatively rewritten in the form of dual quaternion, and the F-norm minimization model of the rotation and translation error function was constructed for optimization. At the same time, the penalty function method is introduced to effectively transform the constrained optimization problem in hand-eye calibration into an unconstrained problem, which is further solved by the ODHPO algorithm for global optimization. Compared with the singular value decomposition algorithm based on the traditional dual quaternion method, the ODHPO algorithm performs better in global optimization ability and convergence stability. Through numerical simulation and real robot hand-eye calibration experiments, it is proved that the proposed algorithm is superior to the traditional dual quaternion (CDQ) algorithm, the classical algorithms Tsai method and Navy method in terms of solution accuracy, sensitivity to the number of pose transformations and stability, demonstrating its potential for application and practical significance in robot vision system.

Ultrasound signal processing based on joint GWO-VMD wavelet threshold functions

To overcome the problem of noise interference in the ultrasonic echo signal received by the probe in ultrasonic nondestructive testing, a method of ultrasonic signal processing using the jointed wavelet threshold function of GWO-VMD (Grey Wolf Optimisation, Variational Mode Decomposition) is proposed. The method first uses the GWO algorithm to optimise the VMD parameters to find the optimal parameter combination penalty factor α and decomposition modulus number K, and then uses the correlation coefficient method to differentiate between the desired signal and noise components after decomposition to obtain the intrinsic mode function (IMF) components. Secondly, the noise components are processed by combining the improved wavelet threshold function algorithm, and lastly, each modal component is restored to produce a denoised signal. Experimental results show that the method improves the signal-to-noise ratio (SNR) by 2.55%, reduces the root mean square error (RMSE) by 3.8%, preserves more useful information, and is more effective in denoising.

Segmentation of Seabed Sediment Images Based on Convolutional Neural Network

The analysis of particle sizes in seabed sediments plays an important role in various fields. However, acquiring digital sediment images has been challenging due to the characteristics of sediment particles and the complexity of the seabed exploration environment. The goal of this study is to address the issue of particle segmentation in sediment images and propose an innovative network called SAMU-Net. Innovations are as follows. Firstly, SAMU-Net combines spatial attention modules with the traditional U-Net model, which allows for contextual information extraction and adaptive perception of feature variations. Secondly, the model adopts the idea of multi-scale fusion, enabling networks to better understand the structural and semantic information of the images by fusing feature maps at different scales. In addition, using the drop blocks and normalization, we speed up the training convergence of the model and avoid the overfitting phenomenon. The results show that the segmentation results predicted by the SAMU-Net network are highly similar to the ground truth and have a better segmentation performance compared to the existing algorithms. At the same time, it provides a new solution idea to uneven illumination and other noise interference problems in the digital imaging information of sediments.

Diagnosis of bearing fault signals based on empirical standard autoregressive power spectrum signal decomposition method

Signal decomposition is an essential tool for the time–frequency analysis of bearing fault signals. Methods for extracting effective fault characteristic information from bearing vibration signals have received increasing attention from researchers. This paper proposes a novel signal decomposition method, called empirical standard autoregressive power spectrum decomposition (ESARPSD), to diagnose bearing faults. First, the normalized autoregressive power spectrum of the bearing fault signal is obtained and its bounds are derived using the lowest minima principle. The decomposed component signals are then filtered through a zero-phase filter bank. Each decomposition component is then demodulated and the respective envelope spectrum is observed to determine the corresponding fault frequency. Zero-phase filter banks are used to deal with the problems of noise interference, which makes decomposition difficult, and frequency aliasing, which occurs when the signal-to-noise ratio is low. Moreover, through normalized autoregressive power spectrum and resonance demodulation techniques, adaptive signal decomposition can accurately separate the target high-frequency vibration signals and detect the fault frequency. The accuracy and performance of the proposed ESARPSD method were validated using simulated signals and actual experimental data. The results demonstrate that this method can effectively decompose bearing fault signal and identify all fault characteristics.

An Improved Second-Order Multi-Synchrosqueezing Transform for the Analysis of Non-Stationary Signals

Second-order multi-synchrosqueezing transform (SMSST), an effective tool for the analysis of non-stationary signals, can significantly improve the time-frequency resolution of a non-stationary signal. Though, the noise energy in the signal can also be enhanced in the transform which can largely affect the characteristic frequency component identification for an accurate fault diagnostic. An improved algorithm termed as an improved second-order multi-synchrosqueezing transform (ISMSST) is then proposed in this study to alleviate the problem of noise interference in the analysis of non-stationary signals. In the study, the time-frequency (TF) distribution of a non-stationary signal is calculated first using SMSST, and then a δ function is constructed based on a newly proposed time-frequency operator (TFO) which is then substituted back into SMSST to produce a noise-free time frequency result. The effectiveness of the technique is validated by comparing the TF results obtained using the proposed algorithm and those using other TFA techniques in the analysis of a simulated signal and an experimental data. The result shows that the current technique can render the most accurate TFA result within the TFA techniques employed in this study.

Classification of multiple power quality disturbances based on continuous wavelet transform and lightweight convolutional neural network

AbstractAiming at the problems of noise interference and too many network parameters for power quality disturbances' (PQDs') classification based on deep learning, the lightweight convolutional neural network combining maximum likelihood Kalman filter and continuous wavelet transform is proposed. In this proposed method, the disturbed PQD signals are denoised by maximum likelihood Kalman filter, and then the denoised PQDs are converted to time‐frequency diagrams, which can provide rich time and frequency domain information, and finally the lightweight convolution neural network is used for automatically extracting and classifying multiple PQDs. To verify the effectiveness and superiority of the proposed method, a variety of PQDs were tested under different noise levels, the experiment results indicate that the average classification accuracy can reach more than 99% even in the case of 10 dB noise. Compared with the existing classification methods, the accuracy and noise immunity ability are improved. Additionally, the proposed method has decided advantages, as evidenced by its low parameter count of 0.73M and short average test time with only 0.7 ms.

Fault Diagnosis Method of Planetary Gearbox Based on ISSA-VME and Multi-scale Fuzzy Entropy

To solve the problem of noise interference and difficulty in feature extraction of vibration signals of planetary gearbox, propose a fault diagnosis method of planetary gearbox based on improved sparrow search algorithm optimized variational mode extraction (ISSA-VME) and multi-scale fuzzy entropy (MFE). The vibration signal was decomposed by ISSA-VME, and the multi-scale fuzzy entropy of the original signal was calculated according to the IMF component. The fault feature set composed of MFE was applied to the SVM classifier optimized by the improved sparrow search algorithm for fault pattern recognition. The experimental results demonstrate that compared with ICEEMDAN and VMD methods, ISSA-VME has a better effect and higher efficiency on vibration signal decomposition. The identification rate of missing teeth, cracks, wear, and other faults of planetary gearbox sun gear by the proposed method reaches 97.50%.

Multi-Point Displacement Synchronous Monitoring Method for Bridges Based on Computer Vision

Bridge displacement is an important part of safety evaluations. Currently, bridge displacement monitoring uses only a few measurement points, making it difficult to evaluate safety. To address this problem, we propose a multi-point displacement synchronous monitoring method. The structural surface has abundant natural texture features, so we use the feature points of the structural surface as the displacement measurement points and propose a feature point displacement calculation method. Furthermore, we conduct experiments on a beam in the laboratory and obtain the beam’s multi-point displacement monitoring results. The monitoring results show that the displacement of some feature points is mismatched. We propose the use of the structural deflection curve to eliminate the feature point displacement mismatches. This method uses the maximum rotation angle of the deflection curve to eliminate displacement mismatches. The results indicate that it is effective to eliminate displacement mismatches in simple structures, such as simply supported beams. Finally, we obtain the test beam’s multi-point displacement synchronous monitoring results. Compared with the 3D laser scanning measurement method, the maximum error of the monitoring results is 8.70%. Research shows that the main reason for the monitoring error is image noise, and the noise interference problem due to its application in practical bridges requires further investigation. Compared with traditional displacement monitoring, this method has significant economic, efficiency, and data integrity advantages. The method has application prospects for multi-point displacement monitoring of simple structures, such as simply supported beams.

Noise Reduction Techniques in ECG Signal

The problem of noise interference in ECG signals has been addressed in this paper. Specifically, a method has been developed to filter out Electromyography noise (EMG) from ECG signals. A dataset of ECG signals with varying levels of EMG noise has been collected using the MIT-BIH dataset. An algorithm has been designed and implemented using the DA FIR filter coupled with Kaiser windowing technique to filter out the noise. The algorithm has been tested on the collected dataset using MATLAB. The performance of the algorithm has been evaluated by calculating the Signal-to-Noise Ratio (SNR) and the Mean Squared Error (MSE). The effectiveness of the algorithm in reducing the EMG noise in the ECG signals has been demonstrated by the results. The algorithm's limitations and future work were discussed in this paper. Interesting future works could include using other filtering techniques to enhance the performance or deep learning techniques to improve noise cancellation. Overall, the effectiveness of using signal processing techniques to filter out EMG noise from ECG signals has been demonstrated and resulted in clearer and more accurate signals for diagnostic purposes.

Research on Improved Wavelet Threshold Denoising Method for Non-Contact Force and Magnetic Signals

In order to solve the problem of noise interference in the collected magneto mechanical signals, a new wavelet shrinkage threshold based on adaptive estimation is proposed. Based on the shortcomings of the traditional threshold function, an improved threshold function is proposed, and the parameters of the threshold function are solved by the improved genetic algorithm, and the optimal denoising effect is finally obtained. The new threshold function can not only make up the defects of each threshold function, ensure the continuity of threshold function, but also flexibly adjust the threshold to adapt to different noise conditions, and solve the deviation caused by inherent threshold function, and protect the useful information with noise signals.

Research of Fault Diagnosis of Mine Rolling Bearing Based on Time-Frequency Analysis

Considering the shortcomings of traditional time-domain and frequency-domain analysis in processing non-stationary signals, this paper proposes to introduce time-frequency into the analysis of a one-dimensional vibration signal and transform it into a two-dimensional time-frequency image to obtain more abundant diagnostic information. At the same time, considering the problem of noise interference under complex working conditions, wavelet denoising is used to preprocess the signal, and the simulation signal is defined. The method of obtaining the time-frequency image verifies the denoising effect. Finally, through the public test data, it is proved that the method can effectively obtain reliable time-frequency images for further bearing fault diagnosis research.

Pre-stack Fidelity Denoising Technique in Loess Plateau Area

Based on the data of P work area in Ordos Basin, this paper makes a comparative analysis and summarizes its difficulties: there are many kinds of interference sources in the work area, linear noise is developed, external interference, abnormal noise interference and other background noise are serious, and the signal-to-noise ratio of the original data is low due to the influence of the thickness of the low deceleration zone, the thickness of loess cover and the excitation and reception conditions. Therefore, aiming at this difficulty, a set of pre stack fidelity denoising technology is adopted according to different noise types. For surface wave, refraction wave, linear interference, abnormal energy interference and other noise, classification, zoning, step-by-step, domain, frequency and time-sharing fidelity processing are used to improve the data imaging effect. Frequency division abnormal amplitude suppression technology, prestack coherent noise suppression and ground roll wave simulated removal technology are used to solve the problem of noise interference. The results show that the signal-to-noise ratio and resolution of seismic data in this area have been significantly improved by using the above technologies.

Micro-Vibration Signal Denoising Algorithm of Spectral Morphology Fitting Based on Variational Mode Decomposition

Environmental micro-vibration has a significant impact on the proper functioning of semiconductor production and testing equipment such as the Czochralski growth furnace, polishing machine, photoetching machine, scanning electron microscope, etc. Low-frequency micro vibration has a significant influence on the normal operation of high-precision machining and testing equipment, and even causes irreversible damage to the equipment. Therefore, the micro-vibration test has important theoretical significance and engineering value for guiding the vibration isolation design of an electronic industrial workshop and ensuring the stable operation of various precision equipment in the workshop. As the observed acceleration signal is affected by noise introduced by the acceleration sensor itself, the signal processing circuit, the external power supply and interference from environmental factors, direct integration operations can lead to problems such as baseline drift and signal distortion in the calculation results. Aiming at the problem of noise interference in the micro-vibration measurement process, this paper proposed a micro-vibration signal denoising algorithm of spectral morphology fitting based on variational mode decomposition. The observed acceleration signal is decomposed into several orders of finite bandwidth intrinsic mode function components. The asymmetric Gaussian mixture model is used to complete the fitting of the spectral curves of each order of intrinsic mode function components. We can obtain the relevant parameters of the asymmetric Gaussian mixture model curves to complete the division of the effective information frequency band and finally achieve the denoising of the observed acceleration signal. Finally, the algorithm of this paper is compared with traditional denoising algorithms through numerical simulation examples and comparative experiments on denoising effects. The results show that the proposed algorithm has higher accuracy and anti-noise ability.

Adaptive Correction Method of Uneven Intensity in Fourier Ptychographic Microscopy

Fourier ptychographic microscopy (FPM) is a computational imaging technique that combines a large field of view and high-resolution. It stitches low-resolution images captured under varying illumination corresponding to different angles in the frequency domain to realize quantitative phase imaging with high spatial bandwidth product. Our previous algorithm (AA algorithm) effectively solves the problem of noise interference and the algorithm is difficult to converge. In FPM experiments, the fluctuation of LED intensity is inevitable, which significantly affects the quality of reconstructed images. The usual solution is to perform intensity correction in the reconstruction algorithm. However, in this study, it is found that the AA algorithm is incompatible with the intensity correction method, which will decrease the convergence performance. To solve this problem, we develop a more general reconstruction algorithm based on the AA algorithm in this study. The simulation and experimental results show that the improved algorithm framework is compatible with the intensity correction method, resulting in an increase in intensity correction reliability and effectiveness. Compared with the GS and AS methods with intensity correction, the error in reconstruction results can be reduced by 52.03% and 29.35%, respectively, without increasing the amount of calculation.

Joint fast time-varying channel estimation with noise elimination and ICI cancellation for LTE-V2X systems

Vehicle-to-everything (V2X) communication is essential for intelligent transportation systems (ITS) and critical technology to ensure traffic safety. Aiming at the problems of noise interference, time-varying, and inter-carrier interference (ICI) in the LTE-V2X channel, a joint fast time-varying channel estimation with noise elimination and ICI cancellation is proposed in this paper. Firstly, using the autocorrelation characteristics of the Zadoff Chu (ZC) pilot sequence, a modified discrete Fourier transform (M-DFT) channel estimation algorithm is proposed to eliminate the noise in cyclic prefix (CP). Secondly, a joint iterative direct decision (IDD) and time-varying channel fitting (CF), called IDD-CF channel estimation algorithm, is proposed to track the rapid changes of channels on different symbols and eliminate ICI. The system simulation results show that the proposed joint fast time-varying channel estimation algorithm can effectively eliminate the noise and ICI, improve the performance of channel estimation, and have better robustness under different Doppler frequency shifts than the representative channel estimation algorithm.

Crowd counting in complex scenes based on an attention aware CNN network

Crowd counting with density estimation has been an active research community due to its significant applications in the fields of public security, video surveillance, traffic monitoring. However, Crowd counting for congested scenes often suffers from some obstacles including severe occlusions, large scale variations, noise interference, etc. In this paper, using the first ten layers of a modified VGG16 and dilated convolution layers as the framework, we have proposed a CNN based crowd counting and density estimation model improved by the attention aware modules with residual connections. To tackle the problem of noise interference, convolutional block attention modules have been introduced into the deep network to segment the foreground and background to focus on interest information, refining deeper features of the input image. To improve information transmission and reuse, residual connections are utilized to link 3 attention blocks. Meanwhile, dilated convolution layers keep larger reception fields and obtain high-resolution density maps. The proposed method has been evaluated on three public benchmarks, i.e. Shanghai Tech A & B, UCF-QNRF and MALL, achieving the mean absolute errors of 64.6 & 8.3, 113.8 and 1.68, respectively. The results outperform some existing excellent approaches. This indicates that the proposed model has high accuracy and better robustness, which is suitable for crowd counting and density estimation in various congested scenes.