Academic literature on the topic 'Wavelet-based functional model'
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Journal articles on the topic "Wavelet-based functional model"
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ABRAMOVICH, FELIX, ANESTIS ANTONIADIS, THEOFANIS SAPATINAS, and BRANI VIDAKOVIC. "OPTIMAL TESTING IN A FIXED-EFFECTS FUNCTIONAL ANALYSIS OF VARIANCE MODEL." International Journal of Wavelets, Multiresolution and Information Processing 02, no. 04 (December 2004): 323–49. http://dx.doi.org/10.1142/s0219691304000639.
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We consider the testing problem in a fixed-effects functional analysis of variance model. We test the null hypotheses that the functional main effects and the functional interactions are zeros against the composite nonparametric alternative hypotheses that they are separated away from zero in L2-norm and also possess some smoothness properties. We adapt the optimal (minimax) hypothesis testing procedures for testing a zero signal in a Gaussian "signal plus noise" model to derive optimal (minimax) non-adaptive and adaptive hypothesis testing procedures for the functional main effects and the functional interactions. The corresponding tests are based on the empirical wavelet coefficients of the data. Wavelet decompositions allow one to characterize different types of smoothness conditions assumed on the response function by means of its wavelet coefficients for a wide range of function classes. In order to shed some light on the theoretical results obtained, we carry out a simulation study to examine the finite sample performance of the proposed functional hypothesis testing procedures. As an illustration, we also apply these tests to a real-life data example arising from physiology. Concluding remarks and hints for possible extensions of the proposed methodology are also given.
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Lee, Gihyoun, Seung Hyun Lee, Sang Hyeon Jin, and Jinung An. "Robust functional near infrared spectroscopy denoising using multiple wavelet shrinkage based on a hemodynamic response model." Journal of Near Infrared Spectroscopy 26, no. 2 (February 13, 2018): 79–86. http://dx.doi.org/10.1177/0967033518757231.
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Functional near infrared spectroscopy can measure hemodynamic signals, and the results are similar to functional magnetic resonance imaging of blood-oxygen-level-dependent signals. Thus, functional near infrared spectroscopy can be employed to investigate brain activity by measuring the absorption of near infrared light through an intact skull. Recently, a general linear model, which is a standard method for functional magnetic resonance imaging, was applied to functional near infrared spectroscopy imaging analysis. However, the general linear model fails when functional near infrared spectroscopy signals retain noise, such as that caused by the subject's movement during measurement. Although wavelet-based denoising and hemodynamic response function smoothing are popular denoising methods for functional near infrared spectroscopy signals, these methods do not exhibit impressive performances for very noisy environments and a specific class of noise. Thus, this paper proposes a new denoising algorithm that uses multiple wavelet shrinkage and a multiple threshold function based on a hemodynamic response model. Through the experiments, the performance of the proposed algorithm is verified using graphic results and objective indexes, and it is compared with existing denoising algorithms.
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KABEER, V., and N. K. NARAYANAN. "WAVELET-BASED ARTIFICIAL LIGHT RECEPTOR MODEL FOR HUMAN FACE RECOGNITION." International Journal of Wavelets, Multiresolution and Information Processing 07, no. 05 (September 2009): 617–27. http://dx.doi.org/10.1142/s0219691309003124.
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This paper presents a novel biologically-inspired and wavelet-based model for extracting features of faces from face images. The biological knowledge about the distribution of light receptors, cones and rods, over the surface of the retina, and the way they are associated with the nerve ends for pattern vision forms the basis for the design of this model. A combination of classical wavelet decomposition and wavelet packet decomposition is used for simulating the functional model of cones and rods in pattern vision. The paper also describes the experiments performed for face recognition using the features extracted on the AT & T face database (formerly, ORL face database) containing 400 face images of 40 different individuals. In the recognition stage, we used the Artificial Neural Network Classifier. A feature vector of size 40 is formed for face images of each person and recognition accuracy is computed using the ANN classifier. Overall recognition accuracy obtained for the AT & T face database is 95.5%.
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Xie, Shengkun, and Sridhar Krishnan. "Wavelet-based sparse functional linear model with applications to EEGs seizure detection and epilepsy diagnosis." Medical & Biological Engineering & Computing 51, no. 1-2 (October 9, 2012): 49–60. http://dx.doi.org/10.1007/s11517-012-0967-8.
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CUI, YIBO, CHI ZHANG, LINYUAN WANG, BIN YAN, and LI TONG. "DENSE-GWP: AN IMPROVED PRIMARY VISUAL ENCODING MODEL BASED ON DENSE GABOR FEATURES." Journal of Mechanics in Medicine and Biology 21, no. 05 (April 7, 2021): 2140017. http://dx.doi.org/10.1142/s0219519421400170.
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Brain visual encoding models based on functional magnetic resonance imaging are growing increasingly popular. The Gabor wavelet pyramid model (GWP) is a classic example, exhibiting a good prediction performance for the primary visual cortex (V1, V2, and V3). However, the local variations in the visual stimulation are quite convoluted in terms of spatial frequency, orientation, and position, posing a challenge for visual encoding models. Whether the GWP model can thoroughly extract informative and effective features from visual stimulus remains unclear. To this end, this paper proposes a dense GWP visual encoding model by ameliorating the composition of the Gabor wavelet basis from three aspects: spatial frequency, orientation, and position. The improved model named Dense-GWP model could extract denser features from the image stimulus. A regularization optimization algorithm was used to select informative and effective features, which were crucial for predicting voxel activity in the region of interest. Extensive experimental results showed that the Dense-GWP model exhibits an improved prediction performance and can therefore help further understand the human visual perception mechanism.
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Jian, Zini, Xianpei Wang, Xueting Liu, Meng Tian, Quande Wang, and Jiangxi Xiao. "Research on BOLD-fMRI Data Denoising Based on Bayesian Estimation and Adaptive Wavelet Threshold." Oxidative Medicine and Cellular Longevity 2021 (February 5, 2021): 1–10. http://dx.doi.org/10.1155/2021/8819384.
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The acquisition of functional magnetic resonance imaging (fMRI) images of blood oxygen level-dependent (BOLD) effect and the signals to be analyzed is based on weak changes in the magnetic field caused by small changes in blood oxygen physiological levels, which are weak signals and complex in noise. In order to model and analyze the pathological and hemodynamic parameters of BOLD-fMRI images effectively, it is urgent to use effective signal analysis techniques to reduce the interference of noise and artifacts. In this paper, the noise characteristics of functional magnetic resonance imaging and the traditional signal denoising methods are analyzed. The Bayesian decision criterion takes into account the probability of the total occurrence of all kinds of references and the loss caused by misjudgment and has strong discriminability. So, an improved adaptive wavelet threshold denoising method based on Bayesian estimation is proposed. By using the correlation characteristics of multiscale wavelet coefficients, the corresponding wavelet components of useful signals and noises are processed differently; while retaining useful frequency information, the noise is weakened to the greatest extent. The new adaptive threshold wavelet denoising method based on Bayesian estimation is applied to the actual experiment, and the results of OEF (oxygen extraction fraction) are optimized. A series of simulation experiments are carried out to verify the effectiveness of the proposed method.
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Inoussa, Garba, Hui Peng, and Jun Wu. "Nonlinear time series modeling and prediction using functional weights wavelet neural network-based state-dependent AR model." Neurocomputing 86 (June 2012): 59–74. http://dx.doi.org/10.1016/j.neucom.2012.01.010.
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Al-Hiyali, Mohammed Isam, Norashikin Yahya, Ibrahima Faye, and Ahmed Faeq Hussein. "Identification of Autism Subtypes Based on Wavelet Coherence of BOLD FMRI Signals Using Convolutional Neural Network." Sensors 21, no. 16 (August 4, 2021): 5256. http://dx.doi.org/10.3390/s21165256.
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The functional connectivity (FC) patterns of resting-state functional magnetic resonance imaging (rs-fMRI) play an essential role in the development of autism spectrum disorders (ASD) classification models. There are available methods in literature that have used FC patterns as inputs for binary classification models, but the results barely reach an accuracy of 80%. Additionally, the generalizability across multiple sites of the models has not been investigated. Due to the lack of ASD subtypes identification model, the multi-class classification is proposed in the present study. This study aims to develop automated identification of autism spectrum disorder (ASD) subtypes using convolutional neural networks (CNN) using dynamic FC as its inputs. The rs-fMRI dataset used in this study consists of 144 individuals from 8 independent sites, labeled based on three ASD subtypes, namely autistic disorder (ASD), Asperger’s disorder (APD), and pervasive developmental disorder not otherwise specified (PDD-NOS). The blood-oxygen-level-dependent (BOLD) signals from 116 brain nodes of automated anatomical labeling (AAL) atlas are used, where the top-ranked node is determined based on one-way analysis of variance (ANOVA) of the power spectral density (PSD) values. Based on the statistical analysis of the PSD values of 3-level ASD and normal control (NC), putamen_R is obtained as the top-ranked node and used for the wavelet coherence computation. With good resolution in time and frequency domain, scalograms of wavelet coherence between the top-ranked node and the rest of the nodes are used as dynamic FC feature input to the convolutional neural networks (CNN). The dynamic FC patterns of wavelet coherence scalogram represent phase synchronization between the pairs of BOLD signals. Classification algorithms are developed using CNN and the wavelet coherence scalograms for binary and multi-class identification were trained and tested using cross-validation and leave-one-out techniques. Results of binary classification (ASD vs. NC) and multi-class classification (ASD vs. APD vs. PDD-NOS vs. NC) yielded, respectively, 89.8% accuracy and 82.1% macro-average accuracy, respectively. Findings from this study have illustrated the good potential of wavelet coherence technique in representing dynamic FC between brain nodes and open possibilities for its application in computer aided diagnosis of other neuropsychiatric disorders, such as depression or schizophrenia.
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BARDET, JEAN-MARC, and PIERRE BERTRAND. "DEFINITION, PROPERTIES AND WAVELET ANALYSIS OF MULTISCALE FRACTIONAL BROWNIAN MOTION." Fractals 15, no. 01 (March 2007): 73–87. http://dx.doi.org/10.1142/s0218348x07003356.
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In some applications, for instance, finance, biomechanics, turbulence or internet traffic, it is relevant to model data with a generalization of a fractional Brownian motion for which the Hurst parameter H is dependent on the frequency. In this contribution, we describe the multiscale fractional Brownian motions which present a parameter H as a piecewise constant function of the frequency. We provide the main properties of these processes: long-memory and smoothness of the paths. Then we propose a statistical method based on wavelet analysis to estimate the different parameters and prove a functional Central Limit Theorem satisfied by the empirical variance of the wavelet coefficients.
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Fernandez Rojas, Raul, Mingyu Liao, Julio Romero, Xu Huang, and Keng-Liang Ou. "Cortical Network Response to Acupuncture and the Effect of the Hegu Point: An fNIRS Study." Sensors 19, no. 2 (January 18, 2019): 394. http://dx.doi.org/10.3390/s19020394.
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Acupuncture is a practice of treatment based on influencing specific points on the body by inserting needles. According to traditional Chinese medicine, the aim of acupuncture treatment for pain management is to use specific acupoints to relieve excess, activate qi (or vital energy), and improve blood circulation. In this context, the Hegu point is one of the most widely-used acupoints for this purpose, and it has been linked to having an analgesic effect. However, there exists considerable debate as to its scientific validity. In this pilot study, we aim to identify the functional connectivity related to the three main types of acupuncture manipulations and also identify an analgesic effect based on the hemodynamic response as measured by functional near-infrared spectroscopy (fNIRS). The cortical response of eleven healthy subjects was obtained using fNIRS during an acupuncture procedure. A multiscale analysis based on wavelet transform coherence was employed to assess the functional connectivity of corresponding channel pairs within the left and right somatosensory region. The wavelet analysis was focused on the very-low frequency oscillations (VLFO, 0.01–0.08 Hz) and the low frequency oscillations (LFO, 0.08–0.15 Hz). A mixed model analysis of variance was used to appraise statistical differences in the wavelet domain for the different acupuncture stimuli. The hemodynamic response after the acupuncture manipulations exhibited strong activations and distinctive cortical networks in each stimulus. The results of the statistical analysis showed significant differences ( p < 0.05 ) between the tasks in both frequency bands. These results suggest the existence of different stimuli-specific cortical networks in both frequency bands and the anaesthetic effect of the Hegu point as measured by fNIRS.
Dissertations / Theses on the topic "Wavelet-based functional model"
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Tang, Man. "Statistical methods for variant discovery and functional genomic analysis using next-generation sequencing data." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/104039.
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The development of high-throughput next-generation sequencing (NGS) techniques produces massive amount of data, allowing the identification of biomarkers in early disease diagnosis and driving the transformation of most disciplines in biology and medicine. A greater concentration is needed in developing novel, powerful, and efficient tools for NGS data analysis. This dissertation focuses on modeling ``omics'' data in various NGS applications with a primary goal of developing novel statistical methods to identify sequence variants, find transcription factor (TF) binding patterns, and decode the relationship between TF and gene expression levels. Accurate and reliable identification of sequence variants, including single nucleotide polymorphisms (SNPs) and insertion-deletion polymorphisms (INDELs), plays a fundamental role in NGS applications. Existing methods for calling these variants often make simplified assumption of positional independence and fail to leverage the dependence of genotypes at nearby loci induced by linkage disequilibrium. We propose vi-HMM, a hidden Markov model (HMM)-based method for calling SNPs and INDELs in mapped short read data. Simulation experiments show that, under various sequencing depths, vi-HMM outperforms existing methods in terms of sensitivity and F1 score. When applied to the human whole genome sequencing data, vi-HMM demonstrates higher accuracy in calling SNPs and INDELs. One important NGS application is chromatin immunoprecipitation followed by sequencing (ChIP-seq), which characterizes protein-DNA relations through genome-wide mapping of TF binding sites. Multiple TFs, binding to DNA sequences, often show complex binding patterns, which indicate how TFs with similar functionalities work together to regulate the expression of target genes. To help uncover the transcriptional regulation mechanism, we propose a novel nonparametric Bayesian method to detect the clustering pattern of multiple-TF bindings from ChIP-seq datasets. Simulation study demonstrates that our method performs best with regard to precision, recall, and F1 score, in comparison to traditional methods. We also apply the method on real data and observe several TF clusters that have been recognized previously in mouse embryonic stem cells. Recent advances in ChIP-seq and RNA sequencing (RNA-Seq) technologies provides more reliable and accurate characterization of TF binding sites and gene expression measurements, which serves as a basis to study the regulatory functions of TFs on gene expression. We propose a log Gaussian cox process with wavelet-based functional model to quantify the relationship between TF binding site locations and gene expression levels. Through the simulation study, we demonstrate that our method performs well, especially with large sample size and small variance. It also shows a remarkable ability to distinguish real local feature in the function estimates.Doctor of Philosophy
The development of high-throughput next-generation sequencing (NGS) techniques produces massive amount of data and bring out innovations in biology and medicine. A greater concentration is needed in developing novel, powerful, and efficient tools for NGS data analysis. In this dissertation, we mainly focus on three problems closely related to NGS and its applications: (1) how to improve variant calling accuracy, (2) how to model transcription factor (TF) binding patterns, and (3) how to quantify of the contribution of TF binding on gene expression. We develop novel statistical methods to identify sequence variants, find TF binding patterns, and explore the relationship between TF binding and gene expressions. We expect our findings will be helpful in promoting a better understanding of disease causality and facilitating the design of personalized treatments.
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Lin, Shih-Di, and 林士迪. "Design of Fuzzy-Cerebellar Model Articulation Controller Based on Wavelet Function for IM Direct Torque Control System." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/fuj8bc.
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碩士國立臺北科技大學
電機工程系研究所
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In this thesis, the Fuzzy Cerebellar Model Articulation PI Controller (FCMAPIC) is proposed, which was designed with the fuzzy cerebellar model articulation controller on the basis of the wavelet function for membership functions, and the projection algorithm in adaptive theory was adopted to tune the parameters. The advantages of the FCMAPIC include on-line PI parameters adjustment, rapid learning ability, and simple structure. The drawbacks of the conventional fixed-parameter PI controller are overcome. Based on the direct torque control (DTC), the speed control algorithm was implemented for induction motors. The merits of DTC include fast system response, simple structure and less computation burden as compared with field-oriented control. In order to solve the problems of torque ripple and noise caused by the inadequately designed conventional hysteresis flux and torque control, the space voltage vector modulation (SVPWM) technique is used in this research. In addition, this research utilizes the speed estimator to realize the speed sensorless control to keep the cost-effectiveness and retain the robust of the motor structure. The experimental results show that the FCMAPIC achieved better dynamic response than NNPIC under the operation conditions that speed range from 36rpm to 1800rpm with 8-Nm load.
Book chapters on the topic "Wavelet-based functional model"
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Xin, Xiu, and Xiaoyi Xiong. "Financial Distress Prediction of Chinese-Listed Companies Based on PCA and WNNs." In Global Applications of Pervasive and Ubiquitous Computing, 212–20. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2645-4.ch023.
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The operating status of an enterprise is disclosed periodically in a financial statement. Financial distress prediction is important for business bankruptcy prevention, and various quantitative prediction methods based on financial ratios have been proposed. This paper presents a financial distress prediction model based on wavelet neural networks (WNNs). The transfer functions of the neurons in WNNs are wavelet base functions which are determined by dilation and translation factors. Back propagation algorithm was used to train the WNNs. Principal component analysis (PCA) method was used to reduce the dimension of the inputs of the WNNs. Multiple discriminate analysis (MDA), Logit, Probit, and WNNs were employed to a dataset selected from Chinese-listed companies. The results demonstrate that the proposed WNNs-based model performs well in comparison with the other three models.
Conference papers on the topic "Wavelet-based functional model"
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"Predicting Molecular Functions in Plants using Wavelet-based Motifs." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004234201400145.
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Robertson, A. N., K. C. Park, and K. F. Alvin. "Identification of Structural Dynamics Models Using Wavelet-Generated Impulse Response Data." In ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0380.
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Abstract This paper addresses the use of discrete wavelet tranforms for the identification of structural dynamics models. First, the discrete temporal impulse response functions are obtained from vibration records by the discrete wavelet transforms (DWTs), which are then utilized for system realizations. From the realized state space models, structural modes, mode shapes and damping parameters are extracted. Attention has been focused on a careful comparison of the present DWT system identification approach to the FFT-based approach and a rational criterion for truncating realized singular values. Numerical examples demonstrate that the present DWT-based structural system identification procedure outperforms the FFT-based procedure.
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Wei, Huiming, G. H. Su, S. Z. Qiu, and Xingbo Yang. "Study on the Onset of Nucleate Boiling in Narrow Annular Channel by Using Wavelet and Wavelet Neural Network." In 17th International Conference on Nuclear Engineering. ASMEDC, 2009. http://dx.doi.org/10.1115/icone17-75475.
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In this study, the local modulus maxima of cubic B-spline wavelet transform are introduced to determine the location of onset of nucleate boiling (ONB). Wavelet transformation has the ability of representing a function and revealing the properties of the function in the joint local regions of the time frequency space. Based on wavelet and artificial neural network, a Wavelet Neural Network (WNN) model predicting ONB for upward flow in vertical narrow annuli with bilateral heating has been developed. The WNN mode combining the properties of the wavelet transform and the advantages of Artificial Neural Networks (ANN) has some advantages of solving non-linear problem. The methods of establishing the model and training of wavelet neural network are discussed particularly in the article. The ONB prediction is investigated by WNN with distilled water flowing upward through narrow annular channels with 0.95 mm, 1.5 mm and 2.0mm gaps, respectively. The WNN prediction results have a good agreement with experimental data. At last, the main parametric trends of the ONB are analyzed by applying WNN. The influences of system pressure, mass flow velocity and wall superheat on ONB are obtained. Simulation and analysis results show that the network model can effectually predict ONB.
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Daneshmand, Farhang, Abdolaziz Abdollahi, Mehdi Liaghat, and Yousef Bazargan Lari. "Free Vibration Analysis of Frame Structures Using BSWI Method." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-68417.
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Vibration analysis for complicated structures, or for problems requiring large numbers of modes, always requires fine meshing or using higher order polynomials as shape functions in conventional finite element analysis. Since it is hard to predict the vibration mode a priori for a complex structure, a uniform fine mesh is generally used which wastes a lot of degrees of freedom to explore some local modes. By the present wavelets element approach, the structural vibration can be analyzed by coarse mesh first and the results can be improved adaptively by multi-level refining the required parts of the model. This will provide accurate data with less degrees of freedom and computation. The scaling functions of B-spline wavelet on the interval (BSWI) as trial functions that combines the versatility of the finite element method with the accuracy of B-spline functions approximation and the multiresolution strategy of wavelets is used for frame structures vibration analysis. Instead of traditional polynomial interpolation, scaling functions at the certain scale have been adopted to form the shape functions and construct wavelet-based elements. Unlike the process of wavelets added directly in the other wavelet numerical methods, the element displacement field represented by the coefficients of wavelets expansions is transformed from wavelet space to physical space via the corresponding transformation matrix. To verify the proposed method, the vibrations of a cantilever beam and a plane structures are studied in the present paper. The analyses and results of these problems display the multi-level procedure and wavelet local improvement. The formulation process is as simple as the conventional finite element method except including transfer matrices to compute the coupled effect between different resolution levels. This advantage makes the method more competitive for adaptive finite element analysis. The results also show good agreement with those obtained from the classical finite element method and analytical solutions.
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Rueda Villanoba, Sergio Alberto, and Carlos Borrás Pinilla. "Neural Network Based Fault Tolerant Control for a Semi-Active Suspension." In ASME 2019 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/imece2019-11516.
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Abstract In this study a Neural Network based fault tolerant control is proposed to accommodate oil leakages in a magnetorheological suspension system based in a half car dynamic model. This model consists of vehicle body (spring mass) connected by the MR suspension system to two lateral wheels (unsprung mass). The semi-active suspension system is a four states nonlinear model; it can be written as a state space representation. The main objectives of a suspension are: Isolate the chassis from road disturbances (passenger comfort) and maintain contact between tire and road to provide better maneuverability, safety and performance. On the other hand, component faults/failures are inevitable in all practical systems, the shock absorbers of semi-active suspensions are prone to fail due to fluid leakage but quickly detect and diagnose this fault in the system, avoid major damage to the system and ensure the safety of the driver. To successfully achieve desirable control performance, it is necessary to have a damping force model which can accurately represent the highly nonlinear and hysteretic dynamic of the MR damper. To simulate parameters of the damper, a quasi-static model was applied, quasi-static approaches are based on non-newtonian yield stress fluids flow by using the Bingham MR Damper Model, relating the relative displacement of the piston, the frictional force, a damping constant, the stiffness of the elastic element of the damper and an offset force. The Fault detection and isolation module is based on residual generation algorithms. The residua r is computed as the difference between the displacement signal of functional and faulty model, when the residual is close to zero, the process is free of faults, while any change in r represents a faulty scheme then a wavelet transform, (Morlet wave function) is used to determine the natural frequencies and amplitudes of displacement and acceleration signal during the failure, this module provides parameters to the neural network controller in order to accommodate the failure using compensation forces from the remaining healthy damper. The neural network uses the error between the plant output and the neural network plant for computing the required electric current to correct the malfunction using the inverse dynamics function of the MR damper model. Consequently, a bump condition, and a random profile road (ISO 8608) described by the power spectral density (PSD) of its vertical displacement, is used as disturbance of control system. The performance of the proposed FTC structure is demonstrated trough simulation. Results shows that the control system could reduce the effect of the partial fault of the MR Damper on system performance.
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Miao, Qiang, and Dong Wang. "Fast Bayesian Inference on Optimal Wavelet Parameters for Bearing Fault Diagnosis." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-51394.
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Rolling element bearings are widely used in various machinery to support rotation shaft and they are prone to failures. Once a bearing fails, it accelerates failures of other adjacent components and results in unexpected machine breakdown. To prevent machine breakdown and reduce unnecessary economic loss, bearing fault must be detected as early as possible. Besides spectral kurtosis, empirical mode decomposition, cyclostationarity, etc., wavelet transform has proven to be an effective method for identification of different bearing faults because it aims to highlight the inner product between an artificial wavelet function and a signal to be analyzed. In the application of wavelet transform, optimization of wavelet parameters attracts much attention because proper selection of wavelet parameters can maximize performance of wavelet transform and extract impulses caused by bearing faults in the case of interruption from other strong low-frequency vibration components and heavy noises. Compared with other optimization methods, such as genetic algorithm, particle swarm optimization, etc., an analytic and fast Bayesian inference on optimal wavelet parameters for an optimal wavelet filtering for bearing fault diagnosis is proposed in this paper. Prior to Bayesian inference, a state space model of wavelet parameters should be constructed to reflect the relationship between wavelet parameters and measurements. Here, measurements are monotonically increasing kurtosis values, which are able to quantify bearing fault signals. The first kurtosis value and initial wavelet parameters are provided by the fast kurtogram, which is a fast algorithm that can be used to locate one of resonant frequency bands for further demodulation with envelope analysis. For other measurements, they are generated by artificial extrapolations of the first kurtosis value. To iteratively infer posterior probability density functions of wavelet parameters and track the artificial measurements, an unscented transform based Bayesian method is introduced. As the iteration number increases, posterior probability density functions of wavelet parameters converge. Then, the optimal wavelet parameters can be found to conduct an optimal wavelet filtering so as to isolate bearing fault signals from other strong low-frequency vibration components. At last, squared envelope analysis and Fourier transform are utilized to demodulate bearing fault signals enhanced by the proposed method and to identify bearing fault characteristic frequencies, respectively. One real case study is used to illustrate how the proposed method works and to demonstrate that the proposed method can be effectively and efficiently used to extract bearing fault signatures. Additionally, a comparison with the fast kurtogram is conducted to show the proposed method is better than the fast kurtogram for bearing fault diagnosis.
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Zuo, Hao, Xuefeng Chen, Zhibo Yang, and Laihao Yang. "Modeling of Lamb Wave Propagation in Beam-Like Structures via Wavelet Finite Element Method." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-65366.
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Beam-like structure is known as one of crucial engineering structures in practical application of aerospace, vessel, civil and machinery. The damages have a great influence on machine performance and may cause a serious threat for security of mechanical structures and systems. Thus it is very significant to identify the damage of beam-like structures for security of mechanical structures and systems. This paper presents a novel application of wavelet finite element method (WFEM) in Lamb wave propagation of beam-like structures. The WFEM, adopting excellent B-spline wavelet on interval (BSWI) basis as approximating functions, has been verified to possess some superiorities for structural dynamic analysis and damage detection. The motion equations of Lamb wave propagation are derived according to Hamilton’s principle and two-dimensional wavelet-based element is constructed by adopting BSWI scaling functions. The damage, which is modeled as open crack with duplicate nodes, is considered in beam-like structures and corresponding damage model is also added in proposed wavelet finite element model. Then central difference method in time domain is employed for wave propagation simulation. Firstly, the validity and accuracy of proposed WFEM are demonstrated on a beam-like structure without crack by comparing with traditional finite element method (FEM) using 2D plane element. What’s more, the obtained velocities of fundamental S0 and A0 mode waves are also compared with Lamb theoretical results to verify the validity and accuracy of proposed model once more. Then the wave propagation in beam-like structures with crack are performed and the process and interaction between Lamb wave and damage are analyzed and discussed in detail. The reflected mode wave and converted mode wave for incident wave interacting with crack are also observed in wave motion snapshots. In summary, this paper presents an accurate but simple and effective numerical method for wave propagation of beam-like structures.
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Romeo, Francesco, Vincenzo Gattulli, and Francesco Benedettini. "Nonlinear Parametric Identification of Oscillating Cables Using Wavelets." In ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/detc2001/vib-21405.
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Abstract In the present study a wavelet-based procedure is applied for the parametric identification of a nonlinear discrete cable model on the basis of experimental data obtained from free-vibration dynamic tests. The 3D cable dynamics is described by two partial integro-differential equations in the transversal planar and nonplanar displacement components. By expanding such variables in series of linear eigenfunctions, a discrete model is obtained by applying a classical Galerkin procedure. The resulting set of nonlinear ODEs contains quadratic and cubic coupling terms. The core of the identification technique is the discretization of such nonlinear differential equations by means of orthogonal Daubechies scaling functions. The Wavelet-Galerkin solution of the nonlinear equation involving the scaling function expansion of nonlinear terms leads to an algebraic problem that has to be inverted in order to solve for the vector of unknown parameters. A sdof model able to describe the in-plane unimodal oscillations is considered. Numerical and experimental identification tests are presented. Accurate estimates of the linear and quadratic parameters are achieved from the moderate oscillation amplitude response of an experimental discrete cable-mass model. The accuracy of the cubic parameter estimate is shown to depend on the level of both the oscillation amplitude and noise.
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Hu, Dinghan, Jiuwen Cao, Xiaoping Lai, and Junbiao Liu. "Epileptic State Classification based on Intrinsic Mode Function and Wavelet Packet Decomposition." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8856282.
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Wang, Likun, Jian Li, Ke Peng, Shijiu Jin, and Zhuang Li. "Petroleum Pipe Leakage Detection and Location Embeded in SCADA." In 2004 International Pipeline Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/ipc2004-0717.
Full textAbstract:
With the increase of the age of the transport oil pipeline and the man-made destruction to pipeline, leaks are often found. The system for pipeline leakage detection and location must be established to find leakage and locate the leak positions to reduce serious environmental pollution and economic loss caused by leakage. The negative pressure wave method is an effective way to locate the leak position, because over 98 percent pipe leakage in China is paroxysmal. There is a SCADA (supervisory control and data acquisition) system to monitor operation for long transport petroleum pipe, but the function of leakage detection and location is not included in existing SCADA system in China. This paper used Dynamic Data Exchange (DDE) method to obtain pipe operation parameters such as pressure, flow rate, temperature, bump current, valve position and so on from the SCADA system. That takes full advantage of the abundant data collection function of the SCADA system to provide data for leakage detection and location. The wavelet packet analysis-based fault diagnosis method can directly use the change of parameters such as energy of frequency component to detect faults without system model. In the paper, a wavelet packet analysis-based characteristic extraction method is used to extract the characteristic information of leak pressure signals. The eigenvector indexes along with the parameters obtained from the SCADA system can be used to avoid false alarms. Wavelet analysis was used to locate leak positions accurately in this paper. Such a wavelet analysis-based leakage detection and location scheme embedded in the SCADA system has been successfully applied to a pipeline in PetroChina. Practical run demonstrated its well effect.