Wavelet Signal Research Articles

Optimizing acoustic signal processing for localization of precise pipeline leakage using acoustic signal decomposition and wavelet analysis

Pipelines play a crucial role in today's infrastructure. However, they are easily affected by environmental conditions. Therefore, regular inspections are essential to ensure the integrity of pipelines. This study introduces a new approach that combines variation mode decomposition (VMD) and empirical wavelet transform (EWT) with continuous wavelet transform (CWT) for acoustic data to improve the signal-to-noise ratio (SNR) and accurately detect pipeline leakage location in the time domain.The acoustic data was gathered in three distinct scenarios inside an open environment, where external noise considerably influenced the original signal.VMD and EWT are employed to enhance the signal-to-noise ratio (SNR). VMD efficiently distinguishes high-frequency noise and assures distinct mode separation, whilst EWT delivers precise temporal localization for non-stationary signals. Based on the results of VMD and EWT, certain IMFs were selected for further study and SNR is computed among the respective chosen IMFs and original signals to measure the improvement. Subsequently, the CWT was utilized on the chosen IMF for feature extraction, effectively finding leakage locations in the time domain.This study illustrates the feasibility of successfully mitigating noise and extracting signals in open and noisy environments, thus enhancing the accuracy of leak detection. The results establish a robust basis for subsequent investigations in pipeline leak detection and demonstrate the applicability of advanced signal processing approach to real-time leak detection systems.

Fault Diagnosis in a Four-Arm Delta Robot Based on Wavelet Scattering Networks and Artificial Intelligence Techniques

This paper presents a comprehensive fault diagnosis approach for a delta robot utilizing advanced feature extraction and classification techniques. A four-arm delta robot prototype is designed in SolidWorks for realistic fault analysis. Two case studies investigate faults through control effort and vibration signals, with control effort detecting motor and encoder faults, while vibration signals identify bearing faults. This study compares time-domain signal features and wavelet scattering networks, applied by classification algorithms including wide neural networks (WNNs), efficient linear support vector machine (ELSVM), efficient logistic regression (ELR), and kernel naive Bayes (KNB). Results indicate that a WNN, using wavelet scattering features ranked by one-way anova, is optimal due to its consistency and reliability, while these features enhance computational efficiency by reducing classifier size. Sensitivity analysis demonstrates the classifier’s capacity to detect untrained faults, highlighting the importance of effective feature extraction and classification methods for fault diagnosis in complex robotic systems. This research significantly contributes to fault diagnosis in delta robots and lays the groundwork for future studies on fault tolerance control and predictive maintenance planning. Future work will focus on the physical implementation of the delta robot in laboratory settings, aiming to improve operational efficiency and reliability in industrial applications.

An improved method to extract the vibration frequency of torsional impactor

Abstract The torsional impactor can convert the kinetic energy of drilling fluid into high-frequency circumferential kinetic energy and provide it to the bottom drilling tools, thereby eliminating stick-slip vibration, protecting Polycrystalline Diamond Compact (PDC) bit and improving construction efficiency. As the test can not be directly performed and the working conditions of torsional impactor can not be quantified by using existing sensors due to the narrow space structure of the impactor and complex vibration conditions, a method for measuring the operating frequency of torsional impactor was designed based on the adaptive white noise complete set empirical mode decomposition of surface acceleration signal data and wavelet threshold denoising. In this paper, the data processing algorithm is used to implement and carry out the simulation test experiments, and the experimental results show that the method can accurately measure the vibration frequency of the torsional impactor under simulated working conditions, and effectively quantify its working state.

Disentangling the impact of motion artifact correction algorithms on functional near-infrared spectroscopy-based brain network analysis.

Functional near-infrared spectroscopy (fNIRS) has been widely used to assess brain functional networks due to its superior ecological validity. Generally, fNIRS signals are sensitive to motion artifacts (MA), which can be removed by various MA correction algorithms. Yet, fNIRS signals may also undergo varying degrees of distortion due to MA correction, leading to notable alternation in functional connectivity (FC) analysis results. We aimed to investigate the effect of different MA correction algorithms on the performance of brain FC and topology analyses. We evaluated various MA correction algorithms on simulated and experimental datasets, including principal component analysis, spline interpolation, correlation-based signal improvement, Kalman filtering, wavelet filtering, and temporal derivative distribution repair (TDDR). The mean FC of each pre-defined network, receiver operating characteristic (ROC), and graph theory metrics were investigated to assess the performance of different algorithms. Although most algorithms did not differ significantly from each other, the TDDR and wavelet filtering turned out to be the most effective methods for FC and topological analysis, as evidenced by their superior denoising ability, the best ROC, and an enhanced ability to recover the original FC pattern. The findings of our study elucidate the varying impact of MA correction algorithms on brain FC analysis, which could serve as a reference for choosing the most appropriate method for future FC research. As guidance, we recommend using TDDR or wavelet filtering to minimize the impact of MA correction in brain network analysis.

A nonstationary stochastic simulator for clustered regional hydroclimatic extremes to characterize compound flood risk

Traditional approaches to flood risk management assume flood events follow an independent, identically distributed (i.i.d.) random process from which static risk measures are computed. Modern risk accounting strategies also consider nonstationarity or long-term trends in the mean and moments of the associated flood probability distributions. However, few approaches consider how extreme hydroclimatic events cluster in both space and time, compounding damage risks. Here we introduce a compound flood risk simulator that models and conditionally forecasts future variability in regional flooding events that cluster in time, given trends and oscillations in a variable climate signal. A modular, novel integration of wavelet signal processing, nonstationary time series forecasting, k-nearest neighbor (KNN) bootstrapping, multivariate copulas, and modified Neyman-Scott (NS) event clustering process provides users the ability to model interannual and sub-annual clustering of flood risk. Our semi-parametric flood generator specifically targets the clustered temporal dynamics of jointly modeled flood intensity, duration, and frequency over a finite future period of a decade or more, thereby providing a foundation for adaptation approaches that integrate temporally clustered flood risk into planning, response and recovery.

Research on a new method of islanding detection based on lifting wavelet and neural network

Abstract At present, the commonly used active and passive islanding detection methods have their own shortcomings, and the islanding detection effect is difficult to meet the requirements. Therefore, a new detection method based on wavelet signal processing and artificial intelligence to identify islanding is proposed in this paper. In this method, the feature quantities required for islanding detection are obtained by wavelet transform and signal processing, and then the feature quantities are identified by neural network to determine whether the islanding is generated in distributed generation system. Wavelet transformation has a strong ability of signal feature extraction, while the neural network has strong learning and identification abilities, the combination of the both is beneficial to improve the success rate of islanding detection. Simulation verification shows that the new islanding detection method proposed in this paper can detect islanding quickly and accurately, and the performance of islanding detection has been significantly improved.

Cross-modal and multimodal data analysis based on functional mapping of spectral descriptors and manifold regularization

Multimodal manifold modeling methods extend spectral geometry-aware data analysis to incorporate learning from multiple related and complementary modalities. However, most methods assume an equal number of homogeneous data samples in each modality and partial correspondences between modalities as prior knowledge. This work introduces two new multimodal modeling methods. The first method introduces a comprehensive framework for handling multimodal information in heterogeneous data without requiring specific prior knowledge. To achieve this, we begin by extracting local descriptors using spectral graph wavelet signatures (SGWS) to identify manifold localities. Then, we propose a manifold regularization framework that incorporates functional mapping between SGWS descriptors (FMBSD) to determine pointwise correspondences. The second method involves manifold regularized multimodal classification based on pointwise correspondences (M2CPC). This method addresses the challenge of multiclass classification in multimodal heterogeneous data by determining correspondences between modalities using the FMBSD method. Experimental results evaluating the FMBSD method on three widely used cross-modal retrieval datasets and evaluating the M2CPC method on three benchmark multimodal multiclass classification datasets demonstrate their effectiveness and superiority over state-of-the-art methods.

Enhanced Sampling in Molecular Dynamics Simulations: How Many MD Snapshots can be Needed to Reproduce the Biological Behavior?

Since its early days in the 19th century, medicinal chemistry has concentrated its efforts on the treatment of diseases, using tools from areas such as chemistry, pharmacology, and molecular biology. The understanding of biological mechanisms and signaling pathways is crucial information for the development of potential agents for the treatment of diseases mainly because they are such complex processes. Given the limitations that the experimental approach presents, computational chemistry is a valuable alternative for the study of these systems and their behavior. Thus, classical molecular dynamics, based on Newton's laws, is considered a technique of great accuracy, when appropriated force fields are used, and provides satisfactory contributions to the scientific community. However, as many configurations are generated in a large MD simulation, methods such as Statistical Inefficiency and Optimal Wavelet Signal Compression Algorithm are great tools that can reduce the number of subsequent QM calculations. Accordingly, this review aims to briefly discuss the importance and relevance of medicinal chemistry allied to computational chemistry as well as to present a case study where, through a molecular dynamics simulation of AMPK protein (50 ns) and explicit solvent (TIP3P model), a minimum number of snapshots necessary to describe the oscillation profile of the protein behavior was proposed. For this purpose, the RMSD calculation, together with the sophisticated OWSCA method was used to propose the minimum number of snapshots.

Advanced Deep Learning for microseismic events prediction for hydraulic fracture treatment via Continuous Wavelet Transform

Understanding hydraulic fracture propagation events would enable operators to estimate the fracture geometry, understand formation/fracture interactions, and estimate the total microseismic events cloud. This paper introduces a new method for understanding the dynamic fracture propagation events using a signal processing technique. Detection of the dynamic fracture event using continuous wavelet transform (CWT) is used to train a deep learning model with microseismic events to predict this cloud for every hydraulic fracture job. The deep learning model is used for the same formation. The CWT for treatment pressure is a convolution process that involves the application of a wavelet signal to the acquired pressure signal in a continuous manner. The wavelet is stretched, or compressed, and is moved along the acquired pressure signal, acting as a generic microscope to highlight small changes in the treating pressure signal. Using CWT as a mathematical microscope to discern diverse signals has been adopted by numerous engineering and medical applications since the 1990s.A normalized CWT scalogram for fracture-treating pressure is thus created as a unique representation of each fracture propagation mode in the hydraulic fracturing job. The new technique is calibrated with several fracture simulation runs and real field data using the previous techniques like moving reference point (MRP). That previous technique which has been previously validated through fracture simulation, is used as a calibration for the new normalized CWT scalogram technique. The technique was finally calibrated by comparing it with micro-seismic events recorded by the Marcellus Shale Energy and Environment Laboratory (MSEEL).The results of this comparison were exceptionally accurate. It can be used to train a deep learning model that can be used to estimate the cloud of microseismic events for hydraulic fracture jobs in Marcellus shale. The new deep learning framework uses CWT to convert the treating pressure during hydraulic fracture propagation to dimensionless representation (normalized CWT scalogram) which acts as a mathematical microscope for the fracture treating pressure on different wavelet stretch or compressions, enabling the detection of the major changes in the treating pressure then train the deep learning model to estimate microseismic events cloud.

Energy wavelet signal processed ECG and standard 12 lead ECG: Diagnosis of early diastolic dysfunction

BackgroundLeft ventricular (LV) diastolic dysfunction (LVDD) is the result of impaired LV relaxation and identifies those at risk of developing heart failure. Echocardiography has been used as the gold standard to identify early LVDD. The signal processed electrocardiogram (hsECG) has demonstrated effectiveness to detect early LVDD. Whether or not the standard 12‑lead electrocardiogram (ECG) can accurately predict early LVDD is not known. MethodsA standard 12‑lead ECG including signal processing (hsECG) was performed in 569 patients. Patients with atrial fibrillation, bundle branch block, pre-excitation, left ventricular hypertrophy or known cardiovascular disease were excluded, leaving 464 examinations for analysis. Early LVDD was diagnosed by established methods using echocardiography. Repolarization abnormalities (T wave discordance) in V1, V6, I and aVL and the hsECG were compared to the echocardiographic findings to establish diagnostic accuracy. ResultsA total of 84 (18.1%) patients were diagnosed with early LVDD. A combination of a borderline or abnormal finding on the hsECG produced the best diagnostic model (sensitivity 84.5%, specificity 47.9%). The best performing ECG lead was V1 with a sensitivity of 38.1% and specificity of 92.1%. Regression analysis demonstrated increasing age and V1 to be predictive of LVDD. ConclusionsThe hsECG displayed reasonable ability to detect early LVDD. Other than V1, repolarization abnormalities on the standard 12‑lead ECG did not. While lead V1 showed promise in detecting LVDD, whether this or any other simple ECG variable can predict future LVDD would be of further interest.

Prediction of the Freshness of Grass Carp during Storage with Electric Nose Based on Signal Sequence Merging and Wavelet Transform

In order to predict the freshness of grass carp, a novel data preprocessing method was proposed for electronic nose (E-nose) signals. The signal sequences from six sensors were selected and subsequently normalized. The direct signal sequence merging (DSSM) and reversed signal sequence merging (RSSM) modes were used for signal sequence merging. Subsequently, the genetic algorithm (GA) was used to evaluate the contribution of diverse sensors, and the merged data sequence was compressed using wavelet transform (WT). Using approximation coefficient and detail coefficient based on different scales and different signal sequence merging modes, principal component analysis (PCA) discriminated successfully storage time of chilled fish fillet. The PCA plots clearly demonstrated that all extracted feature data fully retain the signal characters. The partial least squares (PLS) and artificial neural network (ANN) models were used to establish prediction models for the freshness of grass carp during storage. The DSSM-ANN-A5 and DSSM-PLS-D4 models were chosen as the TVB-N content prediction models, while the DSSM-ANN-D5 and RSSM-PLS-A0 models were selected as the K value prediction models. The R2 values of these models are higher than 0.9, and they have a good coefficient of determination. The results of this study suggest that it using E-nose signals to predict TVB-N content and K value is an effective method for assessing the freshness of grass carp during storage.

Unmanned Surface Vehicle Thruster Fault Diagnosis via Vibration Signal Wavelet Transform and Vision Transformer under Varying Rotational Speed Conditions.

Among unmanned surface vehicle (USV) components, underwater thrusters are pivotal in their mission execution integrity. Yet, these thrusters directly interact with marine environments, making them perpetually susceptible to malfunctions. To diagnose thruster faults, a non-invasive and cost-effective vibration-based methodology that does not require altering existing systems is employed. However, the vibration data collected within the hull is influenced by propeller-fluid interactions, hull damping, and structural resonant frequencies, resulting in noise and unpredictability. Furthermore, to differentiate faults not only at fixed rotational speeds but also over the entire range of a thruster's rotational speeds, traditional frequency analysis based on the Fourier transform cannot be utilized. Hence, Continuous Wavelet Transform (CWT), known for attributions encapsulating physical characteristics in both time-frequency domain nuances, was applied to address these complications and transform vibration data into a scalogram. CWT results are diagnosed using a Vision Transformer (ViT) classifier known for its global context awareness in image processing. The effectiveness of this diagnosis approach was verified through experiments using a USV designed for field experiments. Seven cases with different fault types and severity were diagnosed and yielded average accuracy of 0.9855 and 0.9908 at different vibration points, respectively.

Spatial Localization of a Transformer Robot Based on Ultrasonic Signal Wavelet Decomposition and PHAT-β-γ Generalized Cross Correlation.

Because large oil-immersed transformers are enclosed by a metal shell, the on-site localization means it is difficult to achieve the accurate location of the patrol micro-robot inside a given transformer. To address this issue, a spatial ultrasonic localization method based on wavelet decomposition and PHAT-β-γ generalized cross correlation is proposed in this paper. The method is carried out with a five-element stereo ultrasonic array for the location of a transformer patrol robot. Firstly, the localization signal is decomposed into wavelet coefficients of different scales, which would realize the adaptive decomposition of the frequency of the localization signal from low frequencies to high frequencies. Then, the wavelet coefficients are denoised and reconstructed by using the semi-soft threshold function. Second, a modified phase transform-beta-gamma (PHAT-β-γ) method is used to calculate the exact time delay between different sensors by increasing the weights of the PHAT weighting function and introducing a correlation function. Finally, by using the proposed method, the accurate localization of the transformer patrol micro-robot is achieved with a five-element stereo ultrasonic array. The simulation and test results show that inside a transformer experimental oil tank (120 cm × 100 cm × 100 cm, L × W × H), the relative error of transformer patrol micro-robot spatial localization is within 4.1%, and the maximum localization error is less than 3 cm, which meets the requirement of engineering localization.

Environment-independent textile fiber identification using Wi-Fi channel state information

Textile fiber identification is a technique that can help identify the type of target textile fiber. Existing methods usually rely on expensive detection instruments, specialized researchers, and complex processing techniques. The large number of textile fibers makes it difficult for researchers to use a stable and fast method for identification. This paper introduces a textile fiber identification method based on Wi-Fi signals, and at the same time, in the actual measurement, the signal characteristics of Wi-Fi are usually interfered with by the hardware noise and multipath propagation of channel state information (CSI) measurement equipment. To eliminate the inherent noise of CSI, we designed a denoising method based on the CSI data acquisition of textile fiber samples in independent environments. Then, the features of Wi-Fi signal wavelet packet decomposition could be extracted more stably, and the principal component analysis (PCA) method was used to reduce the data dimension. Finally, the convolutional neural network (CNN) was used to classify the data features. We conducted extensive experiments to verify the effectiveness of the proposed method. The results show that the proposed method can identify all 14 kinds of common textile fibers used in the experiment, and the average accuracy is 93.25%.

Temperature and strain monitoring during thermoforming of thermoplastic composite laminates using optical frequency domain reflectometry

In this study, optical frequency domain reflectometry (OFDR) was used to monitor the thermoforming processes of carbon fiber reinforced thermoplastics (CFRTPs) to address the limitations of conventional sensors including large size and low spatial resolution. A bare single-mode fiber with a polyimide coating and a fiber encapsulated by a long metal capillary were cascaded and embedded into composite laminates to withstand the high pressure and temperature during thermoforming, and then connected to the OFDR for monitoring. A fiber encapsulated by a 2 cm short metal capillary was also embedded to demonstrate that a 1 mm resolution of the OFDR is beneficial for reflecting the local change in the composite. After processing by wavelet denoising, signal extraction, and decoupling, the frequency shift along the optical fiber sensor was successfully converted to strain and temperature. In two repeated thermoforming experiments that involved cooling from 340 °C, the average temperature difference measured by the OFDR and reference thermocouple was only 4.64 °C. The strain measured by the OFDR and reference fiber Bragg grating (FBG) decreases in the cooling stage, and has a clear knee point of 250 °C when correlated with the temperature and strain. This knee point is consistent with the liquid–liquid transition temperature of the polyetherimide and indicates the beginning of consolidation when the composite changes its properties significantly. The average strain difference measured by OFDR and the reference FBG was 69 μϵ when the total strain is approximately 1820 μϵ if only considering the consolidation process from 250 °C. The results of 1 mm spatial resolution and high accuracy demonstrate that OFDR is a promising high-resolution sensing solution for the in-situ temperature and strain monitoring of the thermoforming of CFRTPs.

Derivative spectroscopy and wavelet transform as green spectrophotometric methods for abacavir and lamivudine measurement

Herein, two different sustainable and green signal processing spectrophotometric approaches, namely, derivative spectroscopy and wavelet transform, have been utilized for effective measurement of the antiretroviral therapy abacavir and lamivudine in their pharmaceutical formulations. These methods were used to enhance the spectral data and differentiate between the absorption bands of abacavir and lamivudine in order to accurately measure their concentrations. For determining abacavir and lamivudine, the first derivative spectrophotometric method has been applied to the zero-order and ratio spectra of both drugs. The same approach has been tested using the continuous wavelet transform method where a second order 2.4 of rbio and bior wavelet families were found to be optimum for measuring both drugs. Validation of the proposed methods affirmed their reliability in terms of linearity over the concentration range 1.5–30 µg/mL and 1.5–36 µg/mL for abacavir and lamivudine, respectively, precision (RSD < 2 %), and accuracy with mean recoveries ranging between 98 % and 102 %. Additionally, these spectrophotometric methodologies were applied to real pharmaceutical preparations and yielded results congruent with a prior chromatographic method. Most prominently, the proposed methods stood out for their greenness and sustainability with 97 points as evaluated by the analytical eco-scale method and a score value of 0.79 as analyzed by AGREE method, thereby making them suitable for resource-limited settings and highlighting the potential for broader application of green analytical methods in pharmaceutical analysis.

Blasting vibration effect and safety evaluation method of railway cross tunnels

The evolution characteristics of the peak particle velocity and frequency of an existing railway cross tunnel are systematically studied. The analysis results show that there is a difference in the blasting vibration response before and after the new tunnel face is built. In this paper, a set of blasting vibration response evaluation methods that can quantitatively consider the influence of frequency by means of dimensional analysis is introduced. In addition, relevant knowledge on topics such as wavelet packet analysis, signal processing, structural dynamics, and explosion mechanics is applied to research the influence of the frequency. The research results show that the influence of the frequency on the blasting vibration response can be quantitatively analyzed by quantifying the dynamic amplification factor. To resolve the insufficiency of field monitoring, the distribution of the blasting vibration response along the entire section of the existing tunnel is studied through numerical simulation. Based on the theory of distortion energy in material mechanics, a new idea for evaluating the blasting vibration response of a cross tunnel is proposed.

A New Multi-resolution Analysis Method for Electrooculography Signals.

Electrooculography (EOG) signals indicate the degree and direction of eye movements. Hence, EOG signals have been useful in eye movement controlled rehabilitation systems. Denoising and accurate identification of the type of eye movement in EOG signals are the major challenges in their analysis. The state-of-the-art techniques for EOG signal analysis concerning denoising and eye movement extraction are based on multi-resolution analysis using wavelet bases, such as Haar or Daubechies. However, these wavelets are designed for general purpose signal processing applications and hence are not optimized for the EOG signal structures. In this paper, we propose a new multi-resolution basis specific to the analysis of EOG signals. The scaling and wavelet functions for the basis are derived from the signatures of blinks and saccades respectively, and hence we name them as blinklets and saclets accordingly, thereby forming a new multi-resolution basis. These descriptors are found to be more effective than standard wavelets for EOG signals, signal denoising, and for identifying the different eye movement signatures such as saccades, blinks, smooth pursuits, and fixations, as tested on the Physiosig and Centre for Biomedical Cybernetics Eye Movement (CBC-EM) EOG Databases.

Discrete Wavelet Analysis: A Mighty Approach for Image Segmentation

This paper explores the application of Discrete Wavelet Analysis, a mathematical and signal processing technique, in the context of image segmentation, which provide a pixel-level or region-level decomposition of the image, enabling the extraction of relevant information for subsequent analysis and interpretation. Introducing the basic image segmentation techniques and the DWA, this paper discovers that DWA has found widespread application in fields such as signal processing, image analysis, and data compression. Compared with Fourier Transform, DWA is more suitable for image segmentation, having unique advantages and characteristics. Among the procedures of image segmentation, the most important point is feature selection, which determine the criteria for distinguishing different regions within the image. Despite DWA has many advantages, this technology also owns many challenges and limitations, which may be solved by lasting academic research to refine and extend Discrete Wavelet Analysis methodologies for image segmentation. In short, this research highlights the promise of Discrete Wavelet Analysis, emphasizing the use of high- quality image processing.

Cement pavement void detection algorithm based on GPR signal and continuous wavelet transform method

The dimension of the void area in pavement is crucial to its structural safety. However, there is no effective method to measure its geometric parameters. To address this issue, a void size extraction algorithm based on the continuous wavelet transform (CWT) method was proposed using ground-penetrating radar (GPR) signal. Firstly, the finite-difference time-domain (FDTD) method was used to investigate the GPR response of void areas with different shapes, sizes, and depths. Next, the GPR signal was processed using the CWT method, and a 3D image based on the CWT result was used to visualize the void area. Based on the differences between the void and normal pavement in the time and frequency domains, the signal with maximum energy from the CWT time–frequency result was extracted and combined to reconstruct the new B-scan image, where void areas have energy concentration phenomenon. Based on this, width and depth of void detection algorithm was proposed to recognize the void area. Finally, the detection algorithm was verified both in numerical model and physical lab model. The results indicated that the CWT time–frequency energy spectrum can be used to enhance the void feature, and the 3D CWT image can clearly visualize the void area with a highlighted energy area. After fully testing and validating in numerical and lab models, our proposed method achieved high accuracy in void width and depth detection, providing a precise method for estimating void dimension in pavement. This method can guide DOT departments to carry out pre-maintenance, thereby ensuring pavement safety.