Signal Enhancement Method Research Articles

Mutual Interference Suppression and Signal Enhancement Method for Ground-Penetrating Radar Based on Deep Learning

Mutual Interference Suppression and Signal Enhancement Method for Ground-Penetrating Radar Based on Deep Learning

Research on signal enhancement and flow rate calculation methods of multi-channel ultrasonic flowmeter

Abstract In the field of drilling, accurate measurement of mud flow helps detect potential leaks or abnormal situations, thereby improving overall safety and preventing accidents from occurring. Traditional contact flow meters require breaking the pipeline before installation, making them difficult to install in underground environments. In addition, the complex composition of underground fluids and their corrosive nature can also lead to unsatisfactory measurement results. Therefore, as a non-invasive flow measurement tool, ultrasonic flowmeter is a better choice. However, the ultrasonic echo signals of current ultrasonic flow meters are susceptible to external interference in actual testing, resulting in a decrease in signal quality and making it impossible to perform accurate detection. This article focuses on the research of ultrasound echo signal processing model, designs a four channel ultrasound transceiver model, improves the wavelet denoising algorithm, designs signal reconstruction and baseline correction processing methods, and proposes a pipeline flow calculation algorithm based on Gaussian numerical integration. This method can greatly improve the quality of ultrasound echo signals and the average accuracy of flow measurement. Finally, relevant testing experiments were conducted, and the results of the final performance measurement showed that the method proposed in this paper improved the average accuracy of the measurement by 5.07%, and the relative error remained at around 2%, with a significant improvement in error rate. Therefore, the method proposed in this article has extremely high accuracy in mud flow measurement.

A high-order time-delay difference estimation method for signal enhancement in the distorted towed hydrophone array.

In passive sonar systems, deviations from an ideal linear configuration can significantly impair the beamforming performance of towed hydrophone arrays. This paper presents a method aimed at improving the underwater acoustic signals in the presence of array distortion. The method is centered on a high-order time-delay difference estimation technique utilizing time-frequency autofocus. Initially, a detailed signal model is established that captures the distinctive characteristics of distorted arrays. Subsequently, an algorithm is introduced for high-order time-delay difference estimation to enhance signal fidelity by leveraging phase information within narrowband components originating from incidental acoustic sources. Additionally, a quality metric to evaluate these components is introduced, facilitating the practical implementation of the method. The effectiveness of the proposed approach is validated through both simulation and experimental results, demonstrating its superiority over existing techniques. Importantly, this method does not require prior knowledge of the distortion pattern, making it adaptable to various non-linear array configurations.

Quantitative Analysis of Pb in Soil Using Laser-Induced Breakdown Spectroscopy Based on Signal Enhancement of Conductive Materials.

Studying efficient and accurate soil heavy-metal detection technology is of great significance to establishing a modern system for monitoring soil pollution, early warning and risk assessment, which contributes to the continuous improvement of soil quality and the assurance of food safety. Laser-induced breakdown spectroscopy (LIBS) is considered to be an emerging and effective tool for heavy-metal detection, compared with traditional detection technologies. Limited by the soil matrix effect, the LIBS signal of target elements for soil heavy-metal detection is prone to interference, thereby compromising the accuracy of quantitative detection. Thus, a series of signal-enhancement methods are investigated. This study aims to explore the effect of conductive materials of NaCl and graphite on the quantitative detection of lead (Pb) in soil using LIBS, seeking to find a reliable signal-enhancement method of LIBS for the determination of soil heavy-metal elements. The impact of the addition amount of NaCl and graphite on spectral intensity and parameters, including the signal-to-background ratio (SBR), signal-to-noise ratio (SNR), and relative standard deviation (RSD), were investigated, and the mechanism of signal enhancement by NaCl and graphite based on the analysis of the three-dimensional profile data of ablation craters and plasma parameters (plasmatemperature and electron density) were explored. Univariate and multivariate quantitative analysis models including partial least-squares regression (PLSR), least-squares support vector machine (LS-SVM), and extreme learning machine (ELM) were developed for the quantitative detection of Pb in soil with the optimal amount of NaCl and graphite, and the performance of the models was further compared. The PLSR model with the optimal amount of graphite obtained the best prediction performance, with an Rp that reached 0.994. In addition, among the three spectral lines of Pb, the univariate model of Pb I 405.78 nm showed the best prediction performance, with an Rp of 0.984 and the lowest LOD of 26.142 mg/kg. The overall results indicated that the LIBS signal-enhancement method based on conductive materials combined with appropriate chemometric methods could be a potential tool for the accurate quantitative detection of Pb in soil and could provide a reference for environmental monitoring.

Signal enhancement method for gearboxes fault diagnosis in robotic flexible joint

Abstract Motor Current Signal Analysis (MCSA) provides a non-intrusive approach to fault diagnosis. However, the fault impact reacting in the current is reduced due to the presence of flexible structures in the transmission path from the fault source to the motor. Therefore, this paper proposes a method to enhance the frequency domain of the current signal of a single mechanical fault through a transfer model between motor torque and link vibration. First, the joint system dynamics model was developed based on a three-inertia simplified model. The transfer model of motor torque and link vibration was defined based on the system dynamics. The link vibration is then estimated based on the transfer model and electromagnetic torque. Link vibration signal is considered as an enhancement of the torque signal. Finally, the link vibration signature analysis is performed instead of MCSA. The experimental results show that the method is effective in enhancing the features of individual mechanical faults and improving the fault diagnosis performance.

Long-Term Continuous Gas Diagnostics For Fuel Cell Systems: Development Of An Automatized Raman Metrology

Knowledge of the composition of gas flows in fuel cell systems is crucial for their understanding. In current research, this data is rarely recorded inline and in real-time due to the lack of suitable metrology. Here, we present an optical gas sensor. Green light (532 nm) from a 450 mW laser excites Raman scattering. A spectrometer analyzes the scattered light and a Python software extracts gas composition data automatized and in real-time. We designed a measurement cell that allows optical inline access to gas flow systems and is easy to implement. Therefore, common signal enhancement methods with more complex setups can not be applied. Literature data on scattering cross sections does not offer a reliable basis for calibration what constitutes the need for a gas conditioning system that allows fast preparation of well defined mixtures. We used it to present two principles of calibration. First, calibration based on relative scattering cross sections is the more stable possibility, because it is still valid in cases where laser power and integration time are changed. However, in consequence the results for one species are not independent from the other species. A poor signal, e.g. following from a low mole fraction in a single component, will introduce uncertainty in all components. We avoid that problem through direct linear correlation of signal strength of one species to its partial pressure, which is the second calibration variant. In this case, there is an influence of laser power and integration time. The additional information on overall pressure can be used in model systems, where the presence of undetected components can be ruled out. We can compare that information to pressure measurements to do plausibility checks on the Raman results in real-time. We apply the sensor in fuel cell system research, where measurement intervals of one second yield results of sufficient precision. Increasing integration time can further decrease the uncertainties where favorable. The measurement and data evaluation routines work stable and constant enough to monitor gas compositions in longterm operation. To date, the sensor can measure mixtures of nitrogen, oxygen, hydrogen and water vapor.

High-resolution sub-bottom profiling technology using parametric array and vector hydrophone

This paper reports a new method to achieve high-resolution sub-bottom profiling, providing a scheme for the development of Parametric Array Sub-bottom Profiler (PSBP). The high-resolution sub-bottom profiling system is integrated using parametric transmitting array and vector hydrophone, series signal enhancement and high-resolution signal processing methods are proposed to obtain clear sub-bottom profiles. Based on the parametric array, a small-aperture high directivity beam with low frequency, broadband, and without sidelobes is realized, physically achieved high spatial resolution and temporal resolution. By using the vector hydrophone, a single side frequency doubling beamforming (SSFDB) method is used to significantly suppresses the impact of marine environmental noise and sea surface acoustic scattering, the detection resolution is improved due to the frequency doubling characteristics of SSFDB at the same time. Low order bidirectional differential arithmetic unit (LOBDAU) and coherent accumulator (CA) are combined to enhance the echo signal, and finally the high-resolution layering method (HRLM) based on peak detection gives a clear sub-bottom profile of the sediment. The benefits of the combined acoustic array design scheme and the researched signal processing methods had been experimentally evaluated, a clear sub-bottom profile with a semi buried sunken ship was obtained.

Weak abnormal acoustic signal enhancement and recognition using squeeze-and-excitation attention based denoising convolutional neural network during high-dam flood discharging

Acoustic signals (particularly cavitation acoustic signals) generated during the flood discharge of high dams are highly sensitive to various abnormal situations, whereas weak abnormal signal recognition under strong discharge-noise interference is extremely challenging. Based on the prototype and model experiments, the related abnormal acoustic signals and discharge noise were recorded to construct datasets. Subsequently, using the framework of the deep neural network (DNN) speech enhancement method, a squeeze-and-excitation attention based denoising convolutional neural network (DnCNN) based method for weak abnormal acoustic signal enhancement and recognition was proposed and verified using two case studies of cavitation acoustic signal enhancement and multicategory acoustic signal enhancement and recognition. Compared with the DnCNN method and traditional signal processing methods (such as wavelet, empirical mode decomposition, least mean square, and recursive least square), the proposed method achieved excellent signal enhancement performance after training based on limited prior knowledge of signal and noise. It also demonstrated good generalization ability and robustness in multicategory tasks, which significantly improved the abnormal signal recognition accuracy. This study provides technical support for the practical application of acoustic monitoring based on DNN for safety during the flood discharge of high dams.

Sealing strip acoustic performance evaluation using WF-VMD based signal enhancement method

The sealing strip is an essential component that rubs against the sunroof supporting rod when the sunroof is opening or closing. The acoustic performance of the sealing strip, such as sound pressure level (SPL) and psychoacoustic annoyance (PA), is important for automobile comfort. However, no automated equipment is available for evaluating the acoustic performance of the sealing strip. Moreover, the weak sealing strip acoustic signal, under the strong noise of the running vehicle or machinery, is difficult to obtain, making it hard to be accurately evaluated. In this paper, a sealing strip acoustic performance detector is developed to simulate the frictional state between the sealing strip and the supporting rod, and a weak sealing strip acoustic signal enhancement method based on Wiener filtering (WF) and variational mode decomposition (VMD) is proposed. The machinery noise’s spectrum is obtained through multitaper spectrum estimation and wavelet thresholding. The sealing strip acoustic signal’s spectrum is estimated by subtracting the machinery noise’s spectrum from the spectrum of the noisy signal. Therefore, an accurate prior signal-to-noise ratio (SNR) estimation required in WF can be obtained, and distortion in the estimated sealing strip acoustic signal is reduced. The estimated acoustic signal is further decomposed into multiple intrinsic mode functions (IMF), based on which an optimized estimated signal is finally constructed, thus realizing the enhancement of weak sealing strip acoustic signal. The effectiveness and applicability of the proposed method are validated by experiments conducted with different sealing strips and compression levels. The average correlation coefficient between the optimized estimated signal and the actual sealing strip acoustic signal is 94.2 %. Three sealing strips’ acoustic performances under various compression levels and friction speeds are accurately evaluated using SPL and PA.

A novel drum-shaped metastructure aided weak signal enhancement method for bearing fault diagnosis

Rolling bearings, extensively utilized in rotating machinery, have critical significance for online fault diagnosis in the domains of industrial equipment maintenance and accident prevention. Presently, fault diagnosis methods heavily rely on identifying the optimal resonance band in the high-frequency range (several kHz) to achieve high signal-to-noise ratio (SNR) fault information. However, these approaches, which necessitate high sampling rate sensing systems and complex algorithm deployments, contradict the practical requirements for cost-effective sensors and edge computing in online diagnosis. To address this contradiction, this paper introduces a novel Drum-shaped Metastructure (DMS) to enhance weak bearing fault signals, thus promoting the detection performance of conventional sensors. The DMS is constructed with a drumhead metastructure consisting of a central block mass attached with four spiral beams, of which its length can be adjusted by a tunable frequency mechanism (TFM) for different frequency responses of interest. The detail of its selective frequency enhancement characteristics is first studied through numerical simulations within a frequency range of 200 Hz to 1000 Hz. Subsequently, the effectiveness of the weak signal enhancement is verified on various bearing test systems, which utilize a prototype DMS fabricated by 3D printing. The results present a significant enhancement in the SNRs of the bearing fault signal, which is achieved by demodulating the full frequency band without the need for complex algorithms. Therefore, the proposed DMS provides a new cost-efficient approach to weak bearing fault diagnosis and online monitoring.

Early Weak Fault Signal Enhancement and Recognition Method of Rudder Paddle Bearings Based on Parameter Adaptive Stochastic Resonance

Early Weak Fault Signal Enhancement and Recognition Method of Rudder Paddle Bearings Based on Parameter Adaptive Stochastic Resonance

Signal Enhancement Methods Based on Wavelet Transform, Fractional Fourier Transform and Short-time Fourier Transform

This review paper mainly focuses on different signal enhancement methods such as wavelet transform, fractional Fourier transform (FrFT) and short-time Fourier transform (STFT). First, this paper introduces the concept and importance of signal enhancement, as well as some current issues and challenges. Then, in recent years, the application of the three methods in wavelet transform, fractional Fourier transform and short-time Fourier transform is described. In terms of wavelet transform, this paper discusses the application of wavelet function to image enhancement and the specific steps, and analyzes its advantages and application in signal enhancement. In terms of fractional Fourier transform, this paper introduces its difference from traditional Fourier transform, and discusses its application in combination with adaptive filtering technology and the application of multi-level FrFT in the field of speech enhancement. Finally, in terms of short-time Fourier transform, this paper discusses its application in the fields of image enhancement and speech enhancement. The review paper finally discusses the characteristics, advantages and limitations of these three methods in signal enhancement, and looks forward to the future research directions. Through the research of this review paper, it can provide some reference and guidance for the research in the field of signal enhancement.

Motor Bearing Fault Diagnosis in an Industrial Robot Under Complex Variable Speed Conditions

Abstract Motor bearing is the key vulnerable part of the servomotor in an industrial robot, which is always arranged at the joint that is the main load area. In the movement process of the robot, motor bearing bears a great impact due to the frequent movement of joints, which is easily damaged. The fault characteristic information of a bearing in these complex conditions shows strong nonstationary characteristics. Early nonstationary fault signals are often weak and submerged in background noise. The nonstationary signal processing method using computed order analysis and the weak signal enhancement method using adaptive stochastic resonance both show good performances for the above problems. Inspired by these, a hybrid diagnosis strategy for motor bearing under these speed conditions is proposed. Firstly, the nonstationary fault signals of the motor bearing are transformed into stationary angular signals via computed order analysis. Then, the fault modes are identified via resonance demodulation and variational mode decomposition in the order spectrum. Finally, adaptive stochastic resonance is used to extract the fault features reflecting the bearing operation state. Two types of typical speed conditions are considered, which are representative of the joint. Numerical simulation analysis and experiments verify the effectiveness of the diagnosis method.

Drone audition: improved Gaussian mixture model Wiener filtering approach for audio signal enhancement

Audio signal enhancement is an integral function of drone audition systems which emerges intelligent audio applications for military and civil environments. Signal enhancement using a drone is challenging due to its emission of significant noise from drone motors and propellers resulting in an extremely low signal-to-drone noise ratio level. A method for signal enhancement from an on-board microphone array on a drone for static flight conditions in adverse noisy environments is proposed. In this paper, we present an improved Gaussian mixture model Wiener filter approach for a noisy drone platform in the presence of interfering noise sources such as wind, and traffic. The key to this method is to exploit the spatial properties of the noise sources by using a set of Gaussian components to derive an accurate estimate of the power spectral densities of both residual drone noise and interfering noise sources to obtain the enhanced target sound source signal. We demonstrate the method's general applicability for both speech, and bird calls using an extensive experimental study, including an experimental recording from a drone acoustics dataset using measured impulse responses, and an outdoor measurement from a hovering drone for a bioacoustic application, respectively.

Bacteriophage-Conjugated Fluorescent Nanoemulsion as a Novel Optical Probe for Highly Selective Bacterial Detection

Abstract Infectious disease mortality has decreased due to effective drugs and healthcare. However, global health remains threatened by infectious diseases. New methods of rapid and accurate bacterial detection have attracted considerable attention. Fluorescence detection of whole bacterial cells offers high sensitivity, quantitative analysis, and simple operation. A highly fluorescent bioconjugated probe improves sensitivity and selectivity. This study presents a novel, bright fluorescent probe comprising a bacteriophage and a fluorescent nanoemulsion (fNE) as biorecognition and signal transduction elements, respectively. We demonstrate that fluorescence microscopy imaging using the S. aureus-specific phage, S13′-fNE (phage-fNE), detects S. aureus in the presence of E. coli or S. pseudintermedius, another closely related Staphylococci, in a highly selective manner. Furthermore, fNEs with high dye loadings exhibit considerably greater brightness compared to the fluorescent dye alone, making them suitable for sensitive fluorescence imaging. Phage-fNEs can quantitatively detect S. aureus at 104–108 colony-forming units per milliliter (CFU mL−1), with a limit of detection of 8 × 104 CFU mL−1. This result is comparable to the lowest value achieved by microscopic bacterial detection, with no preconcentration or enzymatic signal enhancement methods used. Bioconjugated fNEs open new avenues for highly selective and sensitive fluorescent detection of bacteria.

Dual-Frequency Signal Enhancement Method of Moving Target Echoes for GNSS-S Radar

The GNSS-S radar utilizes the signals of a global navigation satellite system (GNSS) to carry out target detection. Due to the very low power of GNSS signals, long-term accumulation is needed to improve the gain of the echo signals. However, when it is used for moving object detection, the random movement of the target will cause residual Doppler frequency after the echoes are correlated and compressed through the direct signal. The residual Doppler frequency will cause two problems: on the one hand, the signal coherence will deteriorate, affecting the coherent accumulation gain; on the other hand, the amplitude of the signal after compression will decrease due to the sensitivity of GNSS signals to Doppler frequency. Therefore, how to increase the signal amplitude and eliminate the phase fluctuation caused by the Doppler frequency shift in the GNSS echoes of moving targets is an important issue for GNSS-S radar to detect moving targets. This paper proposes a dual-frequency GNSS echo enhancement method that uses the dual-frequency signals transmitted by the GNSS satellites to enhance and regularize the target echo. First, the phase relationship model of the GNSS dual-frequency echo is constructed, and the phase difference is made to the compressed dual-frequency echo signal to obtain the differential phase without fluctuation; then, the amplitudes of the dual-frequency echo signals are added together; and finally, a new signal with enhanced amplitude and consistent phase is constructed by using the dual-frequency additive amplitude and differential phase, and the long-term coherent accumulation of the signal is carried out, which can improve the processing gain of the weak echo signal of the moving target. The simulation and field experiments show that this method makes full use of the energy of the GNSS dual-frequency signal and eliminates the phase fluctuation in the echo signal of the moving target so that the compressed signal energy remains consistent in the slow-time dimension. After long-term coherent accumulation, the echo SNR was greatly improved, which enabled the detection of two high-speed cars by GNSS-S radar in the experiment.

Optimization of impulsive noise filtering method for rolling bearing signal enhancement

Optimization of impulsive noise filtering method for rolling bearing signal enhancement

Enhancement of LIBS signal in lithium solution at the interface between liquid and an aluminum substrate

Detecting lithium (Li) in seawater in situ is of great significance for searching deep-sea hydrothermal activities and prospecting oceanic lithium reservoirs. Underwater LIBS (Laser-induced breakdown spectroscopy) has been proven to be an attractive technique for in situ element analysis, but its detecting sensitivity is still hard to satisfy the needs of less rich element analysis. Here, we propose an effective approach to enhance the LIBS signal of Li in bulk water with an immersed metal Al substrate. In this work, a laser beam focused on the substrate's surface with focal offsets between −2.5 mm to 1.5 mm from the surface. A 24 times enhancement factor of the Li signal was achieved with a typical pulse energy of 22 mJ at the optimal focal offset of −0.5 mm. The required minimum pulse energy for 100% breakdown was reduced by 5 times, and the plasma lifetime was 3 times longer, compared with the case without substrate assistance. The quantitative results show that both the LOD and analytical prediction capability of Li can be improved by twice compared to the direct measurement. Moreover, using the background as an internal reference, the average RSD at different concentrations can be further reduced from 45% to 18%. This study not only demonstrates the existence of a spectral enhancement effect at the liquid-solid interface of underwater LIBS, but also reveals an easy-to-implement signal enhancement method for upgrading the development of underwater in situ LIBS sensors.

Micro-seismic monitoring using sparse planar array and a weak signal enhancement method

Abstract Traditional ground micro-seismic monitoring is performed by laying long survey lines. This is expensive and difficult to implement in complex mountainous areas and deep marine shale gas reservoirs in China. To address these challenges, this study proposes a ground micro-seismic monitoring method using a sparse planar array that offers greater flexibility in implementation. This study presents a weak signal enhancement method based on a broadband-array adaptive beamforming algorithm to improve the signal-to-noise ratio (SNR) of micro-seismic data collected by sparse planar arrays and to suppress coherent noise. The proposed method involves establishing a signal model for a broadband planar array, estimating the direction of arrival (DOA) of broadband signals using a grid search method, and setting constraint conditions and objective functions based on the DOA results. The optimal weight vector is then calculated by solving the objective function to obtain the desired signal and suppress noise. This study demonstrates that the proposed method effectively improved the SNR and suppressed coherent noise in synthetic and real data. It also highlights the effectiveness of the sparse planar array as a ground micro-seismic monitoring method and the adaptive broadband beamforming method as a practical weak signal enhancement technology.

A Novel Adversarial Learning Framework for Passive Bistatic Radar Signal Enhancement

Passive Bistatic Radar (PBR) has significant civilian and military applications due to its ability to detect low-altitude targets. However, the uncontrollable characteristics of the transmitter often lead to subpar target detection performance, primarily due to a low signal-to-noise ratio (SNR). Coherent accumulation typically has limited ability to improve SNR in the presence of strong noise and clutter. In this paper, we propose an adversarial learning-based radar signal enhancement method, called radar signal enhancement generative adversarial network (RSEGAN), to overcome this challenge. On one hand, an encoder-decoder structure is designed to map noisy signals to clean ones without intervention in the adversarial training stage. On the other hand, a hybrid loss constrained by L1 regularization, L2 regularization, and gradient penalty is proposed to ensure effective training of RSEGAN. Experimental results demonstrate that RSEGAN can reliably remove noise from target information, providing an SNR gain higher than 5 dB for the basic coherent integration method even under low SNR conditions.