Optimal Noise Reduction Research Articles

Fault Diagnosis of Pumped Storage Units—A Novel Data-Model Hybrid-Driven Strategy

Pumped storage units serve as a crucial support for power systems to adapt to large-scale and high-proportion renewable energy sources by providing a stable and flexible energy supply. However, due to the coupling effects of electric power load demands and the complex multi-source factors within the water–mechanical–electrical system, the interrelationship between unit parameters becomes more intricate, posing significant threats to the operational reliability and health status of the units. The complexity of fault diagnosis is further aggravated by the intricate and varied nature of fault characteristics, as well as the challenges in signal extraction under conditions of strong electromagnetic interference and high noise levels. To address these issues, this paper proposes a novel data-model hybrid-driven strategy that analyzes vibration signals to achieve rapid and accurate fault diagnosis of the units. Firstly, the spectral kurtosis theory is employed to enhance the traditional empirical mode decomposition, achieving optimal decomposition and noise reduction effects for vibration signals. Secondly, the intrinsic mode functions (IMFs) obtained from the decomposition are reconstructed, and the entropy values of effective IMFs are calculated as fault feature vectors. Subsequently, the CNN-LSTM model is utilized for fault diagnosis. The effectiveness and feasibility of the proposed method are verified through actual operational data from pumped storage units in a specific region. Through analysis, the fault diagnosis accuracy of the method proposed in this paper can be maintained above 95%, demonstrating robustness in complex engineering environments and effectively ensuring the safe and stable operation of pumped storage units.

3D Cementitious composites printing with pretreated recycled crumb rubber: mechanical and acoustic insulation properties

ABSTRACT Cementitious materials incorporating recycled crumb rubber have become a common sustainable resolution in diverse building environments to achieve various functions in terms of lightweight, ductility, as well as energy absorption. This study explored the 3D printed rubberised cementitious composites (3DPRC) in two aspects: examining the effects of crumb rubber pretreatment conditions on compressive properties; conducting experimental and numerical analysis on the acoustic dissipation characteristics of 3DPRC. Fine crumb rubber granules (3-5 mm) replaced 10%, 20%, and 30% of river sand in the composites. Uniaxial compression tests indicated that the compressive strength of 3DPRC decreased with the increase of crumb rubber content and introduced anisotropic behaviour. Impedance tube tests were conducted to evaluate the sound absorption and insulation capabilities of 3DPRC. An optimal Noise Reduction Coefficient (NRC) of 0.35 was achieved with 30% crumb rubber. The sound insulation properties depend strongly on the mass density and porosity of the 3DPRC. Additionally, it is proved that the volume of built-in air gap has positive effects on both sound absorption and insulation properties. The results from Finite Element Method (FEM) numerical simulations correlated well with experimental data, proving the efficiency of the simulation and validating the experimental results.

Ambulatory ECG noise reduction algorithm for conditional diffusion model based on multi-kernel convolutional transformer.

Ambulatory electrocardiogram (ECG) testing plays a crucial role in the early detection, diagnosis, treatment evaluation, and prevention of cardiovascular diseases. Clear ECG signals are essential for the subsequent analysis of these conditions. However, ECG signals obtained during exercise are susceptible to various noise interferences, including electrode motion artifact, baseline wander, and muscle artifact. These interferences can blur the characteristic ECG waveforms, potentially leading to misjudgment by physicians. To suppress noise in ECG signals more effectively, this paper proposes a novel deep learning-based noise reduction method. This method enhances the diffusion model network by introducing conditional noise, designing a multi-kernel convolutional transformer network structure based on noise prediction, and integrating the diffusion model inverse process to achieve noise reduction. Experiments were conducted on the QT database and MIT-BIH Noise Stress Test Database and compared with the algorithms in other papers to verify the effectiveness of the present method. The results indicate that the proposed method achieves optimal noise reduction performance across both statistical and distance-based evaluation metrics as well as waveform visualization, surpassing eight other state-of-the-art methods. The network proposed in this paper demonstrates stable performance in addressing electrode motion artifact, baseline wander, muscle artifact, and the mixed complex noise of these three types, and it is anticipated to be applied in future noise reduction analysis of clinical dynamic ECG signals.

Synchrophasing control of multiple propellers based on hardware in the loop experimental platform

A hardware in the loop experimental platform is established to evaluate the optimal noise reduction prediction and maintenance capability of multiple propellers under high-precision synchrophasing control. This platform incorporates an online propeller noise model and a digital turboprop engine model into a novel integrated measurement system. Firstly, an improved propeller signature theory using CFD simulation's sound pressure signals is proposed to predict the online propeller noise efficiently. It achieves acceptable noise prediction accuracy using a subset of synchrophase angles to predict noise for all synchrophase angles at all receivers. Secondly, a high-priority interrupt method is proposed for the novel integrated measurement system to guarantee precise measurement and ultimate high-precision synchrophasing control. Thirdly, a turboprop engine model based on a component level model and propeller performance maps' CFD data is also established. To enhance the simulation confidence of the system, we compare the dynamic synchrophasing control effects between systems with and without the integration of a turboprop engine mode. The experimental results demonstrate that the high-priority interrupt method effectively reduces the synchrophase angle(θ) error. These approaches reduce noise by 3.62dB at SPL, exhibit a noise variation within ±0.13dB/°, and effectively manage thrust fluctuation within 4.14%. These results indicate that the method meets the control accuracy and noise reduction requirements in a twin-engined turboprop aircraft.

Analysis of noise reduction in natural gas pressure-boosted power generation equipment

Abstract To address the issue of excessive noise from the natural gas pressure-boosted power generation equipment, this paper conducts a noise analysis on the equipment at a natural gas purification plant in the Southwest oil and gas field. It identifies the primary sources and mechanisms contributing to the noise from the pressure-boosted power generation equipment. The paper clarifies the effectiveness of the existing noise control measures. It proceeds to conduct indoor experimental research on the noise reduction effects of different combinations of sound insulation and absorption materials. It selects optimal noise reduction strategies suitable for different frequency spectra of noise sources and proposes recommendations for noise reduction measures. Additionally, it employs CadnaA software to simulate and validate the noise reduction effects.

An optimal sensor layout method based on noise reduction estimation for active road noise control

Active noise control (ANC) is an effective method to suppress vehicle road noise. The rapid selection of the reference sensor set and rational selection of its layout position are crucial for determining the vibration noise transmission paths with high contributions, which determines the noise reduction performance of the active road noise control (ARNC) system. The reference sensor set selected by the conventional multiple coherence function (MCOH) method does not always ensure the optimal noise reduction performance and the matrix singularity problem based on the Fisher information matrix (FIM) expansion method. To alleviate this problem, this paper proposes a method based on the overall level of noise reduction (OANR) and the FIM to select a set of reference sensors and determine their positions. This method uses an optimal causally constrained Wiener filter to estimate the theoretical OANR for each control area to ensure the reference sensor selected has the optimal noise reduction performance, and it expands efficiently based on the FIM method to select a set of reference sensors. Besides, the matrix singularity problems in the FIM-based expansion method are solved using noise frequency summation and matrix operation, which makes the method more universally applicable. Numerical simulations are performed to analyze and evaluate the noise reduction performance and selection efficiency of the proposed method under different conditions. In addition, a series of real vehicle ANC experiments are conducted. The results show that the reference sensor set selected based on the proposed method has an obvious advantage in noise reduction performance, and only four reference sensors are needed to achieve 4.63 dB(A) noise reduction in ARNC experiments. It fully confirms the effectiveness of the proposed method in real vehicle applications.

Accessing Ping Sonar Echosounder Produksi ROVMAKER pada Mikrokontroler dan Data Noise Reduction dengan Kalman Filter

Two-thirds of Indonesia's territory is waters that have a high risk of flooding. Bathymetric surveys are important for flood management because they provide depth and topography data to determine the need for dredging of a water area. Traditional bathymetric surveys produce less than optimal coverage due to limited ship movements. This research aims to develop an automatic bathymetry system using a ping sonar echosounder sensor integrated with a USV (Unmanned Surface Vehicle). The ping sonar echosounder produced by ROVMAKER can generally only be accessed using the built-in software from the ROVMAKER manufacturer and can now be integrated with the microcontroller system on the USV to increase the efficiency of bathymetric system performance. This research also uses the Kalman filter method to reduce sensor reading noise. Validation of the suitability of the sensor shows an average error of 2.94% at a depth of 1400mm and 2.195% at a depth of 2200mm, which shows that the sensor is classified as suitable for use. The optimal noise reduction in the Kalman filter experiment is at a variance ratio (R/Q) of 1000 with an RMSE of 5.05mm at a depth of 1400mm and 3.99mm at a depth of 2200mm. This system has been proven to increase the accuracy of bathymetric data and system access efficiency.

EMD-based noise reduction study of steel cored conveyor belt containing slag signal

This paper proposes a noise reduction technique that combines empirical mode decomposition (EMD) and wavelet thresholding to eliminate the noise interference caused by metal slag in the steel cord conveyor belt damage signal. Initially, the original signal undergoes decomposition by EMD into high- and low-frequency intrinsic mode function (IMF) components based on metal slag signal feature analysis. The wavelet threshold de-noising method is then applied to filter out the high-frequency noise while preserving the low-frequency IMF components. The IMF components containing slag noise are discarded, and the filtered signal and the low-frequency IMF components are used for signal reconstruction to obtain an optimal noise reduction effect. The study demonstrates that this method is more effective in suppressing metal slag noise, environmental noise, and other noise interferences than traditional EMD decomposition noise reduction and wavelet threshold noise reduction methods. This research provides a valuable reference for noise reduction in steel cord conveyor belt detection data.

Numerical model for noise reduction of small vertical-axis wind turbines

Abstract. Small vertical-axis wind turbines are a promising solution for affordable and clean energy, but their noise emissions present a challenge to public acceptance. Numerous blade designs have been aimed at reducing noise but often come with a decrease in wind turbine aerodynamic efficiency. In this study, the acoustic power and torque of a 5 kW vertical-axis wind turbine (VAWT) were simulated by using different mesh sizes and turbulence models. The simulated torque and noise of the turbine have significant sensitivity to the mesh size, so suitable mesh sizes were determined for the near-wall and rotating regions that can be used as a design reference for future turbines with similar operating conditions. The selection of the turbulence model was found to affect the predicted torque by about 10 % and the predicted tip noise by about 2 dB. The selected mesh size and turbulence model were then applied to simulating the effectiveness of three common noise mitigation techniques: a mask, deflector, and wall roughness. The results showed that deflectors are suitable for noise reduction of small VAWTs. This paper provides valuable information on simulating noise propagation from small VAWTs and the optimal noise reduction techniques.

Optimization Method to Predict Optimal Noise Reduction Parameters for the Non-Local Means Algorithm Based on the Scintillator Thickness in Radiography.

The resulting image obtained from an X-ray imaging system depends significantly on the characteristics of the detector. In particular, when an X-ray image is acquired by thinning the detector, a relatively large amount of noise inevitably occurs. In addition, when a thick detector is used to reduce noise in X-ray images, blurring increases and the ability to distinguish target areas deteriorates. In this study, we aimed to derive the optimal X-ray image quality by deriving the optimal noise reduction parameters based on the non-local means (NLM) algorithm. The detectors used were of two thicknesses (96 and 140 μm), and images were acquired based on the IEC 62220-1-1:2015 RQA-5 protocol. The optimal parameters were derived by calculating the edge preservation index and signal-to-noise ratio according to the sigma value of the NLM algorithm. As a result, a sigma value of the optimized NLM algorithm (0.01) was derived, and this algorithm was applied to a relatively thin X-ray detector system to obtain appropriate noise level and spatial resolution data. The no-reference-based blind/referenceless image spatial quality evaluator value, which analyzes the overall image quality, was best when using the proposed method. In conclusion, we propose an optimized NLM algorithm based on a new method that can overcome the noise amplification problem in thin X-ray detector systems and is expected to be applied in various photon imaging fields in the future.

Effect of a Control Mechanism on the Interaction between a Rectangular Jet and a Slotted Plate: Experimental Study of the Aeroacoustic Field

The aeroacoustic field of a rectangular subsonic jet impinging on a slotted plate was investigated experimentally using microphones and stereoscopic particle image velocimetry (S-PIV). The study was carried out with a Reynolds number of 6700 and an impact distance of 4 cm. The current configuration represents a benchmark standpoint, featuring high levels of generated noise. A control mechanism consisting of a thin rod was introduced downstream from the jet exit to suppress the self-sustained tones. A total of 1085 positions of the rod between the jet exit and impinging plate were tested to identify positions of optimal noise reduction. Two zones were distinguished in terms of control efficacy: a zone where the sound pressure level (SPL) dropped by up to 19 dB and another zone where the SPL increased by up to 14 dB. The velocity fields show that the presence of the rod divides the jet into two lateral secondary jets on both sides of the main jet axis. The outer part of the secondary jets expanded radially with less interaction with the plate compared to the case without the control. This behavior affected the deformation of vortices against the slot. Proper orthogonal decomposition was applied to the velocity field for a better understanding of the turbulence dynamics with and without the control rod.

Application of EMD combined with wavelet algorithm for filtering slag noise in steel cord conveyor belt

This study proposes a noise reduction technique that combines empirical mode decomposition (EMD) with wavelet thresholding to remove noise interference generated by metal slag in the damage signal of steel cord conveyor belts. The initial step involves decomposing the original signal into high-frequency and low-frequency intrinsic mode function (IMF) components using empirical mode decomposition (EMD), guided by the metal slag signal characteristics analysis. The high-frequency noise is filtered away while the low-frequency IMF components are kept intact through the wavelet threshold de-noising technique. The slag noise-containing IMF components are deleted, and the filtered signal and the low-frequency IMF components are utilized for signal reconstruction to provide the optimum noise reduction result. The study demonstrates that this method is more effective in suppressing metal slag noise, environmental noise, and other noise interferences than traditional EMD decomposition noise reduction and wavelet threshold noise reduction methods. This research provides a valuable reference for noise reduction in steel cord conveyor belt detection data.

A Robust Denoised Algorithm Based on Hessian–Sparse Deconvolution for Passive Underwater Acoustic Detection

Digital beamforming techniques find wide applications in the field of underwater acoustic array signal processing. However, their azimuthal resolution has long been constrained by the Rayleigh limit, consequently limiting their detection performance. In this paper, we propose a novel two-dimensional Hessian–sparse deconvolution algorithm based on image processing techniques. This method assumes a priori that the underwater acoustic bearing time record (BTR) images exhibit sparsity, and then it first constructs partial differential equations in the beamforming domain with sparsity-norm constraints for optimal noise reduction. Subsequently, a two-dimensional deconvolution operation is applied to narrow the main lobe, aiming to achieve additional temporal gains in two-dimensional processing. The simulation and real sea trial data processing results show that the main lobe width of the proposed method is about 1.3 degrees at 0 dB. It effectively reduces the main lobe width and enhances the detection resolution of BTRs in the post-processing part, especially in low-signal-to-noise-ratio (SNR) environments. Therefore, the proposed method provides nice opportunities to further improve the target-detecting ability of hydrophone arrays.

Denoising Method of Hydro-turbine Vibration Signal Based on Joint WOA-VMD and Improved Wavelet Threshold

Comparing to the untreated signal, the traditional wavelet thresholding functions decomposed and reconstructed signal has a constant error. Variational mode decomposition (VMD) sets different penalty factor α and mode decomposition layer K during signal processing will lead to the final noise reduction results. Therefore, this paper introduces the Whale Optimization Algorithm (WOA)-VMD and improved wavelet threshold denoising for oscillation signal disposal of hydraulic turbine. Firstly, WOA was used to optimize the VMD decomposition parameters α and K with the local minimum of envelope entropy as the objective function, and the optimal noise reduction effect was obtained. Secondly, the vibration signals with noise were deconstructed into K intrinsic mode functions (IMF) after VMD, and their thresholds were set. Furthermore, the IMF component below the threshold value is denoised again by improving the wavelet threshold value. Finally, the IMF reorganization after de-noising is used to obtain the final noise reduction signal. Through the verification analysis, the combination of WOA-VMD and improved wavelet threshold function denoising method could effectively filter the noise in the signal and significantly improve the noise reduction effect of the hydraulic turbine vibration signal.

Control of a circular cylinder flow using attached solid/perforated splitter plates at deflection angles

Control of a circular cylinder flow by rear-attached solid/perforated splitters has been experimentally investigated using Particle Image Velocimetry (PIV) and far-field microphones, respectively. The Reynolds number is Re = 2.7 × 104–6.8 × 104 based on the cylinder diameter D, and the effects of splitter length L (L/D = 0–4.0), porosity σ (0%–22%), and deflection angle α (0°–30°) on noise and flow characteristics are reported. The literature seriously lacks the combination effects of these parameters on bluff-body wakes, especially the flow mechanism on noise variations. Acoustic results show that for a solid splitter, the optimal noise reduction of 18 dB is achieved when L/D = 1.0–1.5, whereas when 2.0 ≤ L/D ≤ 4.0, the noise increases rather than decreases, consistent with the literature. At L/D = 1.0, deflecting and/or perforating the splitter would not further reduce the noise and, on the contrary, weaken the noise control efficiency. However, at L/D = 2.0, a rear-half perforation with σ ≥ 18.2% or a deflection angle of α ≥ 30° individually reduces noise by more than 10 dB compared to the bare cylinder. Combining both methods at L/D = 2.0 eliminates vortex-shedding noise when σ ≥ 11.6% and α ≥ 20°. PIV measurements reveal that noise reduction at L/D = 1.0 results from reduced vortex strength in the flow, while noise increase at L/D = 2.0 is attributed to vortex-splitter trailing edge interaction, which is mitigated by the perforation and/or the deflection of the splitter. The study suggests that, in general, appropriately perforating and deflecting longer splitter plates (L/D ≥ 2.0) effectively reduce bluff-body noise, while solid, shorter splitter plates (0.5 ≤ L/D ≤ 1.5) positioned streamwise achieve significant noise reduction. These insights offer valuable noise control strategies for various engineering applications.

Key Mechanism Research of Top Plate Thickening of the Box-Girder Bridge for Noise Reduction Design in High-Speed Railway

In the context of noise reduction schemes for box-girder bridges (BGBs) used in high-speed railway, the thickened top plate design can effectively reduce the structural noise of the BGB, which has been widely recognized. However, it is difficult to obtain the optimum thickness of the top plate of the BGB without mastering the key mechanism of the noise reduction scheme. Therefore, this study took a 32 m simple-supported concrete BGB in the context of a high-speed railway as the research object and analyzed and compared the sound vibration characteristics of the entire thickened top plate versus the locally thickened top plate on BGB tracks, and the optimal noise reduction mechanism of the thickened top plate design scheme was studied in detail. The key issues of the thickened top plate noise reduction scheme are discussed. The results show that thickening the top plate can obviously reduce the bridge’s structural noise when subjected to severe vibration and high frequency bands because the vibration of the BGB is reduced. However, in the low frequency band, acoustic radiation can occur as a result of the small amplitude vibration, and this phenomenon is closely related to the vibrational distribution of the BGB. Therefore, it is necessary to focus on the vibrational distribution of the BGB as a priority when carrying out noise reduction using a thickened top plate. This paper points out the most significant factors affecting the acoustic radiation ability of the BGB in different frequency bands, especially the key problem of the strong acoustic radiation ability caused by small vibrations in low frequency band. The research results can provide an important theoretical basis for the optimal thickness design of the BGB.

Investigation of the sound-absorbing performances of pure coffee grounds

Various natural sound-absorbing materials such as rice by-products, coir fiber, date palm fiber, peanut husks, hardwood cross-sections, and forest by-products have been introduced to replace petroleum-based sound-absorbing materials in previous studies, and their sound-absorbing performance was significant. This study investigated the sound-absorbing performance of pure coffee grounds as an eco-friendly sound-absorbing material. After inserting coffee grounds into cylindrical holders with lengths of 20, 30, and 40 mm, the density of the coffee grounds was adjusted from 0.2 to 0.5 g/cm3. Then, the sound absorption coefficients were measured by an impedance tube. As the thickness and density increased, the sound absorption coefficient at low frequencies improved. However, the sound absorption coefficient at high frequencies decreased. The optimal noise reduction coefficient (NRC) of coffee grounds investigated in this study was 0.61 at a density of 0.3 g/cm3 and thickness of 50 mm. This result shows a sound-absorbing performance that is comparable to other natural sound-absorbing materials. This study concludes that coffee grounds have high use-value as an eco-friendly sound-absorbing material.

On eigenvalue shaping for two-channel decentralized active noise control systems

Recent works show that two-channel decentralized active noise control (DANC) systems are able to achieve optimal noise reduction performance with guaranteed convergence by proper matrix eigenvalue shaping for each frequency. In this paper, we study the impact of three eigenvalue shaping approaches on the performance of a time-domain two-channel DANC system, where the first two approaches are from the literature and the third one is newly proposed as an extension of one of the two approaches. By theoretical analysis and experimental investigation of the three approaches, it is found that the eigenvalues of the 2 × 2 so-called characteristic matrices in the frequency-domain should be shaped by considering two aspects. Firstly, the two eigenvalues for each matrix need to be pushed towards the positive real axis to ensure stability. Secondly, the eigenvalues inherently affect the two auxiliary filters in the time-domain. They should be shaped so that the two filters have roughly the same magnitudes to facilitate implementation. Simulation results using the measured acoustic paths demonstrate the efficacy of the proposed eigenvalue shaping approach and the adaptive filtering technique for controlling sinusoidal noise, multitone noise, white noise and traffic noise. Experimental result shows the efficacy of the proposed approach for controlling white noise in three dimensional space.

Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals

Aiming to address the problems of the high bit error rate (BER) of demodulation or low classification accuracy of modulation signals with a low signal-to-noise ratio (SNR), we propose a double-residual denoising autoencoder method with a channel attention mechanism, referred to as DRdA-CA, to improve the SNR of modulation signals. The proposed DRdA-CA consists of an encoding module and a decoding module. A squeeze-and-excitation (SE) ResNet module containing one residual connection is modified and then introduced into the autoencoder as the channel attention mechanism, to better extract the characteristics of the modulation signals and reduce the computational complexity of the model. Moreover, the other residual connection is further added inside the encoding and decoding modules to optimize the network degradation problem, which is beneficial for fully exploiting the multi-level features of modulation signals and improving the reconstruction quality of the signal. The ablation experiments prove that both the improved SE module and dual residual connections in the proposed method play an important role in improving the denoising performance. The subsequent experimental results show that the proposed DRdA-CA significantly improves the SNR values of eight modulation types in the range of -12 dB to 8 dB. Especially for 16QAM and 64QAM, the SNR is improved by 8.38 dB and 8.27 dB on average, respectively. Compared to the DnCNN denoising method, the proposed DRdA-CA makes the average classification accuracy increase by 67.59∼74.94% over the entire SNR range. When it comes to the demodulation, compared with the RLS and the DnCNN denoising algorithms, the proposed denoising method reduces the BER of 16QAM by an average of 63.5% and 40.5%, and reduces the BER of 64QAM by an average of 46.7% and 18.6%. The above results show that the proposed DRdA-CA achieves the optimal noise reduction effect.

Energy-Efficient Sparsity-Driven Speech Enhancement in Wireless Acoustic Sensor Networks

Wireless acoustic sensor network (WASN) has shown a superiority over conventional microphone arrays in many aspects. There exists an important tradeoff between the performance and power consumption, as usually the sensors are power driven with a limited amount of battery resource. Given a prescribed performance bound, in literature sensor selection (SS) and rate allocation (RA) methods can be leveraged to optimize the energy efficiency. In this work, we propose a joint rate allocation and sensor selection (RASS) approach to simultaneously optimize the sensor subset and rate distribution, which is formulated by minimizing the total transmission power in terms of selection and bit-rate variables and constraining the residual noise power. It can be shown that under a set of linear constraints on beamforming, the linearly-constrained minimum variance (LCMV) beamformer is the optimal noise reduction filter. Based on this, the RASS reduces to a mixed semi-definite and bilinear programming problem, which is then solved using a two-step algorithm. As the selection and bit-rate unknowns are bilinear, we first consider to optimize their product, resulting in an upper bound of RASS. Then, we use McCormick envelopes to relax the bilinear constraint, resulting in a linear program. The final selection and bit-rate solutions are obtained by posterior randomized rounding. It can be shown that SS and RA are special cases of the proposed RASS. Numerical results using simulated WASNs validate the power efficiency of the proposed method as well as the robustness against dynamic factors.