Signal Noise Reduction Research Articles

RDTS noise reduction: A fast method study based on signal waveform type

In order to reduce the measurement error of Raman-based distributed temperature sensor (RDTS) caused by random noise, we have proposed an effective noise reduction method, which can reduce the time consumption of data processing due to the low time complexity of the method. We classified the signal as five models based on the waveform types, thus to filter the raw data according to the waveform types. Then, we established an experimental device to test the effects of different signal noise reduction methods, including maximum deviation (MD), root mean square error (RMSE), smoothness, and time consumption. Experimental results show that it can efficiently suppress random noise and reduce temperature measurement errors in RDTS compared with direct demodulation of the raw data, and significantly improve the curve smoothness compared with D-SVD and WT-Soft. The time consumption test based on three different devices shows that the time consumption of the proposed method is approximately 10% of the D-SVD method, and about 30% of the WT-Soft method. It proves that this method is an effective and fast noise reduction method in RDTS systems.

Noise reduction in optical gyroscope signals based on hybrid approaches

Noise reduction in optical gyroscope signals based on hybrid approaches

Noise Reduction Method for Low Voltage Power Line Carrier Communication

In order to improve the performance of electricity information collection system, this paper studies and proposes a new method of low-voltage power line carrier signal frequency domain noise reduction. It includes frequency domain decomposition of carrier signal, filtering noise reduction and signal frequency domain restoration. Then compared our method with the wavelet packet noise reduction method based on multi-resolution. The experimental results show that the proposed algorithm is better than the comparison algorithm under different modulation modes and carrier frequencies, especially suitable for carrier signal noise reduction under bad channel conditions. The algorithm proposed in this paper has good anti signal attenuation performance, and it can effectively enhance the SNR, and ensure the transmission performance and system reliability of the electricity information collection system.

A data-driven early micro-leakage detection and localization approach of hydraulic systems

Leakage is one of the most important reasons for failure of hydraulic systems. The accurate positioning of leakage is of great significance to ensure the safe and reliable operation of hydraulic systems. For early stage of leakage, the pressure of the hydraulic circuit does not change obviously and therefore cannot be monitored by pressure sensors. Meanwhile, the pressure of the hydraulic circuit changes frequently due to the influence of load and state of the switch, which further reduces the accuracy of leakage localization. In the work, a novel Bayesian networks (BNs)-based data-driven early leakage localization approach for multi-valve systems is proposed. Wavelet transform is used for signal noise reduction and BNs-based leak localization model is used to identify the location of leakage. A normalization model is developed to improve the robustness of the leakage localization model. A hydraulic system with eight valves is used to demonstrate the application of the proposed early micro-leakage detection and localization approach.

Influence of different wavelet bases and threshold estimation on noise reduction of substation fault signals under different SNR

With the rapid development of power industry, the number of substations is also increasing, and the voltage level is also higher and higher, and the types of equipment are diverse. The detection of equipment fault signal in substation is an important part to ensure the normal operation of electric power. Only timely detection of fault signal and corresponding processing can ensure the overall operation of substation. But the fault signal often contains a lot of white noise, which will interfere with our judgment of the fault point. How to extract the effective signal from the noisy signal becomes particularly important, but under different signal-to-noise ratio, the selection of different wavelet basis and threshold estimation also have a great impact on the noise reduction effect. In this paper, we choose different wavelet bases and threshold estimation under several different signal-to-noise ratios to study their influence on the effect of signal denoising.

Signal filtering method of variational mode decomposition and Euclidean distance based on optimizing parameters of classification particle swarm optimization algorithm

In order to deal with the problem that the noise of leakage signals from natural gas pipelines has a great influence on the feature extraction of pipeline leakage, this paper proposes a signal denoising method of variational mode decomposition (VMD) and Euclidean distance (ED) based on optimizing parameters of classification particle swarm optimization (CPSO) algorithm. First, CPSO algorithm is used to optimize parameters K and [Formula: see text] of VMD, adaptively. The sum of the ratio of the mean and variance of the cross-correlation coefficient and the ratio of the mean and variance of kurtosis is used as the fitness function of CPSO. Then, the optimized VMD is used to decompose the signal to obtain several intrinsic mode functions (IMFs). Finally, ED is used to select the effective modes, and the signal is reconstructed to achieve signal noise reduction. The corresponding evaluation indicators show that the accuracy and robustness of the improved method are better than other noise reduction methods. The denoising effect is significant, which proves that the algorithm proposed in this paper is effective in signal filtering.

Initial fault time estimation of rolling element bearing by backtracking strategy, improved VMD and infogram

Rolling bearing failure is widely regarded as a failure form of industrial machines. Owing to the poor operating circumstance with the stochastic contact between rolling elements, the performance of the bearing will deteriorate over time and cause a cascade breakdown in the mechanical system. Early fault detection has been found to be an effective strategy to avoid economic loss. Therefore, an integration method for fault diagnosis that combines backtracking strategy, improved variational mode decomposition (VMD), and infogram is proposed to tackle the challenge of the early feature extraction from the heavy noisy non-stationary signal. The backtracking strategy is adopted to track the data sample points earlier than the fault threshold determined based on the kurtosis index. The optimum parameters α and K of VMD are acquired through the particle swarm optimization (PSO) algorithm. In this way, the more accurate intrinsic mode functions (IMFs) can be gained by the improved VMD. The optimum IMFs are acquired according to the maximum values of kurtosis and correlation coefficients, and these IMFs can be reconstructed into the noise reduction signal. Since envelope analysis requires the selection of the appropriate central frequency and bandwidth, infogram is utilized to select the values of them. A simulated case is applied to demonstrate the validation of the proposed method. And to further illustrate its practicality, it is employed to perform early fault diagnosis for an experimental case. According to the diagnosis results, the proposed method has conspicuous superiority over the other existing technologies for estimating incipient fault time of the bearing.

Blind signal separation with Noise Reduction for efficient speaker identification

Generally, most blind signal separation algorithms deal with the separation problem in the absence of noise. The presence of noise degrades the performance of separated signals. This paper deals with the problem of blind separation of audio signals from noisy mixtures. Blind signal separation algorithm is applied on the discrete cosine transform, the discrete sine transform or the discrete wavelet transform of the mixed signals, instead of performing the separation on the mixtures in the time domain. All of these transforms have an energy compaction property, which concentrates most of the signal energy in a few coefficients in the transform domain, leaving most of the transform-domain coefficients close to zero. As a result, the separation is performed on a few coefficients in the transform domain. Another advantage of signal separation in transform domains is that the effect of noise on the signals in the transform domains is smaller than that in the time domain. The paper presents also an investigation of the rule of the speech enhancement techniques as pre- and post-processing steps for the blind signal separation process, instead of performing the separation on the mixtures in the time domain. The considered speech enhancement techniques are the spectral subtraction, the Wiener filtering, the adaptive Wiener filtering, and the wavelet denoising techniques. Both blind signal separation and noise reduction are applied within a real speaker identification system to reduce the effect of interference and noise on the system performance. The simulation results confirm the superiority of transform domain separation to time domain separation and the importance of the wavelet denoising technique, when used as a pre-processing step for noise reduction. Moreover, the speaker identification system performance is enhanced with blind signal separation and noise reduction.

A new radial basis function neural network-based method for denoising of partial discharge signals

Partial discharge (PD) assessment is one of the most prevalent techniques in the condition monitoring of high voltage (HV) equipment. In this regard, one of the main stages in the procedure of PD assessment is noise reduction of PD signals. Hence in this paper, a novel artificial neural network (ANN) based denoising method is introduced. Unlike the existing denoising algorithms, the proposed technique treats the noisy PD signal, in a data window, as a set of points that can be estimated by a curve. Hence, radial basis function (RBF)-ANN is utilized being a self-structured algorithm compared to multi-layer perceptron (MLP)-ANN that is influenced by the number of neurons. The performance of the proposed method has been compared with MLP-ANN, wavelet transform (WT) and empirical mode decomposition (EMD) based algorithms for different laboratory generated PD signals suppressed with white noise. The obtained results have proved the superiority of the proposed method.

FPGA-based Implementation of IIR Filter for Real-Time Noise Reduction in Signal

Filtering of unwanted frequencies represents the main aspect of digital signal processing (DSP) in any modern communication system. The main role of the filter is to perform attenuation of certain frequencies and pass only frequencies of interest. In a DSP system, sampled or discrete-time signals are processed by digital filters using different mathematical operations. Digital filters are commonly categorized as Finite Impulse Response (FIR) and Infinite Impulse Response (IIR). This research focuses on the full VHDL implementation of digital second-order lowpass IIR filter for reducing the noisy frequencies on the FPGA board. The initial step is to determine, from continuous time domain function, the transfer function in the complex {s} domain, then map transfer function in complex {z} domain and finally calculate the difference equation in discrete-time domain of the system with adequate coefficients. Prior to the FPGA implementation, the IIR filter is tested in MATLAB using a signal with mixed frequencies and signal with randomly generated noise. The digital implementation is completed by using fixed-point binary vectors and clocked processes.

Application of a noise reduction method combining AVMD and SVD in natural gas pipeline leakage signal

Due to the large amount of noise in pipeline leakage signal, the accuracy of the leakage detection device's judgment will be reduced by direct leakage detection. Therefore, the noise reduction of pipeline leakage signal is critical for preprocessing technology of pipeline leakage detection. A denoising method combining adaptive variational mode decomposition (AVMD) and singular value decomposition (SVD) is proposed in this paper. First, the mode number and the penalty factor of VMD are searched automatically by AVMD. The AVMD algorithm is coupled to a fitness function based on improved refine composite multiscale dispersion entropy (RCMDE). Subsequently, a time–frequency matrix which obtains time–frequency subspace after SVD is constructed for all mode components decomposed by VMD, and the number of effective time–frequency subspaces is determined by the relative change rate of singular values, thereby the denoised signal is achieved. Finally, the experimental results show that the AVMD-SVD method proposed in this paper has the significant denoising effect and strong robustness.

Improving the Signal-to-Noise-Ratio of Free Induction Decay Signals Using a New Multilinear Singular Value Decomposition-Based Filter

The free induction decay (FID) signal output by a proton precession magnetometer (PPM) is usually only of the microvolt level, and its frequency is proportional to the magnetic field strength. Therefore, obtaining a high signal-to-noise ratio (SNR) FID signal is crucial for improving the measurement accuracy of the magnetometer. The current gold standards for noise reduction in FID signals—singular value decomposition (SVD) and principal component analysis (PCA)—still have limited denoising capabilities, especially in cases with strong noise interference. In this study, a new noise-reduction algorithm for FID based on multilinear SVD (MLSVD) is proposed. First, equal delay-based multichannel data sampling is used to obtain multiple correlated signals, and thus, the obtained multiple signals are constructed as a third-order tensor; second, the MLSVD is employed to calculate and remove the noise singular value of the tensor; and third, canonical polyadic decomposition (CPD) is used to fuse the multichannel FID signal processed by MLSVD and eliminate signal noise, further improving the SNR. Subsequently, a PPM experimental test platform was constructed, and extensive simulation and practical comparison tests were conducted. The results show that, when the SNR is −10 dB, the noise-reduction effect of the MLSVD is about 9.12 dB higher than that of the SVD and about 8.15 dB higher than that of the PCA, with an overall increase of 28%. In an environment with strong noise interference—with an SNR of −30 dB—both PCA and SVD are no longer viable, while MLSVD can still effectively suppress noise, with a signal-to-noise improvement ratio (SNIR) being as high as 50.36 dB.

An Efficient Hybrid Technique for Noise Reduction in Optical Gyroscope Signals

An Efficient Hybrid Technique for Noise Reduction in Optical Gyroscope Signals

Distributed Adaptive Signal Estimation in Wireless Sensor Networks With Partial Prior Knowledge of the Desired Sources Steering Matrix

In the past two decades, wireless sensor networks (WSNs) and their applications have been the topic of many studies. Different multi-sensor nodes are used to collect, process and distribute data over wireless links to perform different tasks such as smart detection, target tracking, node localization, etc. In this article, the problem of distributed adaptive estimation of node-specific signals for signal enhancement or noise reduction is addressed. First the centralized rank R generalized eigenvalue decomposition (GEVD) based multichannel Wiener filter (MWF) with prior knowledge for node-specific signal estimation in a WSN is introduced, where (some of) the nodes have partial prior knowledge of the desired sources steering matrix. A distributed adaptive estimation algorithm for a fully-connected WSN is then proposed demonstrating that this MWF can be obtained by letting the nodes work on compressed (i.e. reduced-dimensional) sensor signals compared to the centralized algorithm. The distributed algorithm can be used in applications such as speech enhancement in a wireless acoustic sensor network (WASN), where (some of) the nodes have prior knowledge on the location of the desired speech sources and on their local microphone array geometry or have access to clean noise reference signals. Foundations for a proof of convergence using a Lagrangian framework, are given, since convergence is observed in batch-mode simulations. Finally, numerical simulation results are provided for a speech enhancement scenario.

Hybrid Wavelet Stacking Ensemble Model for Insulators Contamination Forecasting

Contaminated insulators can have higher surface conductivity, which can result in irreversible failures in the electrical power system. In this paper, the ultrasound equipment is used to assist in the prediction of failure identification in porcelain insulators of the 13.8 kV, 60 Hz pin profile. To perform the laboratory analysis, insulators from a problematic branch are removed after an inspection of the electrical system and are evaluated in the laboratory under controlled conditions. To perform the time series predictions, the stacking ensemble learning model is applied with the wavelet transform for signal filtering and noise reduction. For a complete analysis of the model, variations in its configuration were evaluated. The results of root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R2) are presented. To validate the result, a benchmarking is presented with well-established models, such as an adaptive neuro-fuzzy inference system (ANFIS) and long-term short-term memory (LSTM).

A SVD-Based Signal De-Noising Method With Fitting Threshold for EMAT

The electromagnetic acoustic transducer (EMAT) is a powerful and useful non-destructive testing technology for structural health monitoring. However, EMAT has an issue of low efficiency in conversion and its signal is easily affected by noise, which make it difficult to accurately identify and evaluate structural defects. Thereby, signal de-noising preprocessing is essential for the evaluation of defects. In this paper, we proposed an improved singular value decomposition (SVD) de-noising method based on the fitting threshold for EMAT signal. For SVD de-noising method, the key point is to determine the singular value threshold for reconstructing the signal. We applied a segmented regression model to find the appropriate threshold in this approach. To investigate the efficacy of the proposed method, simulation signals and experimental signals are used for verification respectively. A comparative analysis has been under-taken to confirm that the proposed signal de-noising has advantages over other methods in EMAT signal noise reduction, and it is useful for more accurate evaluation of defects.

Crack classification in rotor-bearing system by means of wavelet transform and deep learning methods: an experimental investigation

Parallel with significant growth in industry, especially mysteries related to energy engineering, condition monitoring of rotating systems have been experiencing a noticeable increase. One of the prevalent faults in these systems is fatigue crack, so finding reliable procedures in identification of cracks in rotating shafts has become a pressing problem among engineers during recent decades. While a vast majority of cracked rotors can operate for a specific period of time, to prevent catastrophic failures, crack detection and measuring its characteristics (i.e. size and its location) seem to be essential. In the present essay, a hybrid procedure, consisting of Deep Learning and Discrete Wavelet transform (DWT), is applied in detection of a breathing transverse crack and its depth in a rotor-bearing-disk system. DWT with Daubechies 32(db32) as wavelet mother function is applied in signal noise reduction until level 6, also its Relative Wavelet Energy (RWE) and Wavelet entropy (WE) are extracted. A characteristic vector that is a combination of RWE and WE is considered as input to a multi-layer Artificial Neural Network (ANN). In this supervised learning classifier, a multi-layer Perceptron neural network is used; in addition, Rectified Linear Unit (ReLU) function is exerted as activation function in both hidden and output layers. By comparing the results, it can be seen that the applied procedure has strong capacity in identification of crack and its size in the rotor system.

CO‐registration of proton magnetic resonance spectroscopy (1H‐MRS) and diffusion imaging (DMRI) in healthy aging: A prospective study of cerebral neurochemistry and microstructure

AbstractBackgroundChanges in brain microstructure are markers of aging and implicate changes at the neurochemical level. Prior magnetic resonance spectroscopy (MRS) works have demonstrated effect of aging on gamma‐aminobutyric acid (GABA) and myo‐inositol (mI), suggesting changes in cellular metabolism and neuroinflammation. Diffusion MRI (dMRI) works have shown that neurite density (NDI) is decreased in aging, suggesting a decrease in dendritic and axonal health. We obtained MRS and dMRI indices co‐registered within the same voxel in the medial frontal cortex (MFC) to investigate how tissue microstructure locally influences neurochemistry. We hypothesized that NDI would predict age‐related changes in concentration of GABA and age‐related neuroinflammation with concentration of mI.MethodParticipants: 138 healthy older adults (age 64.8±6.52 years; range 51‐86 years). Informed consent was obtained from all subjects. MRS: acquired on a 3T MRI system with a 32‐channel head coil. PRESS and MEGAPRESS spectra were acquired from a 3x3x3cm voxel positioned in the MFC. GABA and mI levels were estimated with Gannet and LC Model. Segmentation of images was performed with SPM 12. A voxel mask registered to T1 was generated to perform registration of dMRI in the same volume. dMRI: acquired in the axial plane with a multi‐band signal‐shot spin echo echo‐planar sequence (MB acceleration factor = 3). A multi‐shell scheme was used with 3 b0, 10 b150, 10 b350, 64 b1000, 64 b2000, 64 b3000 and 104 b5000 directions. Raw images were processed to reduce signal noise. Analyses: Linear regression analyses were performed: Effect of age with NDI and MRS metabolites as dependent variables. Effect of NDI with MRS metabolites as dependent variables with age as covariate.ResultAge predicted levels of GABA (B=0.413, P<0.001), mI (B=0.309, P=0.002), and NDI (B=‐0.351, P<0.001). NDI predicted levels of GABA (B=0.24, P=0.005) and mI (B=‐0.236, P=0.022).ConclusionThis study demonstrates that local microstructure differentially impacts GABA and mI in a healthy aging cohort. It also supports age as a predictor of microstructure and neurochemistry in the MFC. These suggest that neurite density impacts neurochemistry prior to age‐related, clinically significant structural changes and demonstrate relevance of co‐registration of MRS and dMRI in aging.

Extracting weak multi-frequency signal in heavy colored noise

The extraction of weak multi-frequency signal in the background of heavy colored noise is studied. For the multi-frequency signal which cannot be directly decomposed by ensemble empirical mode decomposition (EEMD), further processing is made in the paper. Based on the normalized autocorrelation analysis and EEMD, a new method of weak multi-frequency signal detection is proposed in the article. Firstly, the adaptive noise reduction of multi-frequency signal with strong colored noise is realized by the normalized autocorrelation analysis. Then, the denoised multi-frequency signal is decomposed by the EEMD method. A series of monochromatic intrinsic mode functions are obtained to representing the characteristics of multi-frequency signal. Finally, two mechanical fault experiments are designed to verify the applicability of the proposed method in the fault diagnosis. The vibration signals with bearing pedestal bolt looseness fault, bearing outer raceway and rolling element compound fault were collected, respectively. The experimental results show that the weak multi-frequency fault features in the vibration signals with heavy colored noise are effectively extracted by the new method. The proposed method has a good application prospect in the field of signal processing.

Fault diagnosis based on extremely randomized trees in wireless sensor networks

Wireless Sensor Network (WSN) being highly diversified cyber–physical system makes it vulnerable to numerous failures, which can cause devastation towards safety, economy, and systems’ reliability. Precise detection and diagnosis of failures or faults in WSN is a challenging issue due to the diversity of deployment and the limitations in the sensors’ resources. In this paper, supervised machine learning-based technique is considered to scrutinize the behavior of sensors through their data for the detection and diagnosis of faults. Most of the faults that commonly occur in WSN are considered: hardover, drift, spike, erratic, data-loss, stuck, and random fault. A trusted dataset published online by the researchers at the University of North Carolina composed of temperature and humidity sensor measurements of multi-hop scenario was acquired and the aforementioned faults were simulated in non-faulty (normal) data. Events from fault occurrences were generated to replicate realistic scenarios of WSN. To detect and diagnose the faults in timely manner, we adopt an ensemble learning-based lightweight technique called Extremely Randomized Trees or Extra-Trees. The proposed Extra-Trees-based detection scheme has the ability of robustness towards signal noise and strong reduction of bias and variance error. The performances of the proposed scheme were compared with those of the state-of-the-art machine learning algorithms such as support vector machine, random forest, neural network, and decision tree. Performance evaluation shows the efficiency of the proposed scheme in terms of accuracy, precision, and F1-score. In addition, the proposed scheme has low training time compared to state-of-the-art approaches.