Wavelet Package Transform Research Articles

Cross-frequency and iso-frequency estimation of functional corticomuscular coupling after stroke.

Functional corticomuscular coupling (FCMC) between the brain and muscles has been used for motor function assessment after stroke. Two types, iso-frequency coupling (IFC) and cross-frequency coupling (CFC), are existed in sensory-motor system for healthy people. However, in stroke, only a few studies focused on IFC between electroencephalogram (EEG) and electromyogram (EMG) signals, and no CFC studies have been found. Considering the intrinsic complexity and rhythmicity of the biological system, we first used the wavelet package transformation (WPT)to decompose the EEG and EMG signals into several subsignals with different frequency bands, and then applied transfer entropy (TE)to analyze the IFC and CFC relationship between each pair-wise subsignal. In this study, eight stroke patients and eight healthy people were enrolled. Results showed that both IFC and CFC still existed in stroke patients (EEG → EMG: 1:1, 3:2, 2:1; EMG → EEG: 1:1, 2:1, 2:3, 3:1). Compared with the stroke-unaffected side and healthy controls, the stroke-affected side yielded lower alpha, beta and gamma synchronization (IFC: beta; CFC: alpha, beta and gamma). Further analysis indicated that stroke patients yielded no significant difference of the FCMC between EEG → EMG and EMG → EEG directions. Our study indicated that alpha and beta bands were essential to concentrating and maintaining the motor capacities, and provided a new insight in understanding the propagation and function in the sensory-motor system.

Comparative study of wavelet based techniques for electromagnetic noise evaluation and removal

Signals acquired from an industrial environment with many sources of electromagnetic interferences may be polluted by white noise. Polluted data segments with many steady consecutive periods can be used (sometimes unsuccessful) for the estimation of a denoised period from the steady acquired data by using the mean signal method. For data segments with at least 4 periods, when only certain segments (shorter than a period) can be considered steady, hybrid algorithms can be used to automatically evaluate the power of noise and afterward to perform the noise removal by using wavelet thrashing trees. This paper deals with 2 additional denoising techniques. The 1-st one is based on the Wavelet Package Transform and allows for the separation of the noise components which pollute a data segment of at least one period. The second approached denoising technique is also addressing one period data segments and estimates firstly the power of noise by using the energies of the vectors of details from the first 2 levels of a tree used by decompositions with the Stationary Wavelet Transform. The estimated power of noise is afterward used to establish the number of levels in the wavelet thrashing trees. In this last stage, two wavelet mothers were used. Simulated and real test signals were used and performance comparisons were performed.

A weak fault feature extraction of rolling element bearing based on attenuated cosine dictionaries and sparse feature sign search

The time domain signal of bearing pitting/spalling fault always presents shock and modulation, and it is often submerged by strong noises, especially in the early stage. the conventional fault feature extraction method may have insufficient feature extraction accuracy, and even in some extreme cases, the fault feature frequency cannot be extracted because of the strong noise interference. Aiming at overcoming the noise interference problem encountered in this kind of weak fault feature extraction, a novel weak fault feature extraction algorithm termed as ACFSS of rolling bearing is proposed. The ACFSS is based on an overcomplete dictionary (or overcomplete atomic library) of Attenuated Cosines(AC) basis, which is highly matched to the bearing fault waveforms, and an improved Basis Pursuit algorithm with Feature Sign Search(FSS) is introduced into the ACFSS to improve the calculating speed. In order to select the suitable parameters of the attenuated cosine dictionary, some methods such as peak resonance frequency (PRF), power variation peak (PVK), time shift parameter (TSP), etc. are introduced. These parameters span the sparse overcomplete dictionary. Finally, the bearing fault data of Case-Western University and full life accelerated IMS bearing data are utilized to verify the validation of ACFSS. Compared with the ordinary envelope spectrum analysis(ESA) method /the ordinary Basis Pursuit Denoising(BPDN) method/ Wavelet package transform(WPT) Kurtogram method and Empirical Mode Decomposition(EMD) combining Singular Value Decomposition(SVD) method, The experiment show that the proposed method are more redundant and robust when facing strong noise interference, and it can be used to extract the weak fault feature frequency efficiently and accurately.

SEMG-based Endpoint Stiffness Estimation of Human Arm using Gene Expression Programming

The endpoint stiffness of the human arm has been long recognized as a key factor in the smooth contact between humans and environment. And the endpoint stiffness of the human arm is highly correlated with the surface electromyography (sEMG) produced by the contraction of the muscles. In this paper, the Gene Expression Programming (GEP) Algorithm is proposed to estimate the endpoint stiffness of human arm based on sEMG. This paper improves the traditional decoding method of GEP. Instead of generating an expression tree, it is directly decoded by looking for the effective length of the gene. And experimental results show that nonlinear models such as GEP models in this paper have higher correlation and lower RMSE (root mean square error) than regression stiffness using linear regression models. Selecting different feature of EMG signals, the correlation coefficient and the root mean square error of the model is very different. For the GEP model in this paper, WPTSVD (Wavelet Package Transform Singular Value Decomposion) and WTSVD (Wavelet Transform Singular Value Decomposion) are selected as the feature of sEMG signals have high performances and the correlation can reach 57%±12.1%

Vibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery

This paper proposes a new signal feature extraction and fault diagnosis method for fault diagnosis of low-speed machinery. Statistic filter (SF) and wavelet package transform (WPT) are combined with moving-peak-hold method (M-PH) to extract features of a fault signal, and special bearing diagnostic symptom parameters (SSPs) in a frequency domain that are sensitive to bearing fault diagnosis are defined to recognize fault types. The SF is first used to adaptively cancel noises, and then fault detection is performed by exploiting the optimum symptom parameters in a time domain to identify a normal or fault state. For precise diagnosis, the SSPs are calculated after the signals are processed by M-PH and WPT. A decision tree is used to structure intelligent diagnosis rules in each step until the states are fully and automatically detected. The efficacy of this method was confirmed by applying it to an experimental low-speed rotation machine.

Analysis of the dynamic behavior of structures using the high-rate GNSS-PPP method combined with a wavelet-neural model: Numerical simulation and experimental tests

Recently, the high rate global navigation satellite system-precise point positioning (GNSS-PPP) technique has been used to detect the dynamic behavior of structures. This study aimed to increase the accuracy of the extraction oscillation properties of structural movements based on the high-rate (10 Hz) GNSS-PPP monitoring technique. A developmental model based on the combination of wavelet package transformation (WPT) de-noising and neural network prediction (NN) was proposed to improve the dynamic behavior of structures for GNSS-PPP method. A complicated numerical simulation involving highly noisy data and 13 experimental cases with different loads were utilized to confirm the efficiency of the proposed model design and the monitoring technique in detecting the dynamic behavior of structures. The results revealed that, when combined with the proposed model, GNSS-PPP method can be used to accurately detect the dynamic behavior of engineering structures as an alternative to relative GNSS method.

Noise Detection for Biosignals Using an Orthogonal Wavelet Packet Tree Denoising Algorithm

Abstract This article deals with the noise detection of discrete biosignals using an orthogonal wavelet packet. In specific, it compares the usefulness of Daubechies wavelets with different vanishing moments for the denoising and compression of the digitalised biosignals in case of surface electromyography (sEMG) signals. The work is based upon the discrete wavelet transform (DWT) version of wavelet package transform (WPT). A noise reducing algorithm is proposed to detect unavoidable noise in the acquired data in a model independent way. The noise of a signal sequence will be defined by a seminorm. This method was developed for a possible observation during a fracture healing period. The proposed method is general for signal processing and its design was based upon the wavelet packet.

Linux-Based Embedded System for Wavelet Denoising and Monitoring of sEMG Signals Using an Axiomatic Seminorm

This paper deals with the noise detection of discrete sEMG signals using wavelets. More specifically, it compares the usefullness of the Haars and Daubechies wavelets for the denoising and compression of the digitalized biosignal. The work is based upon the Discrete Wavelet Transform (DWT) version of Wavelet Package Transform (WPT). A denoising algorithm is proposed to detect unavoidable measured noise in the acquired data. With the help of a seminorm the noise of a sequence is defined. Using this norm it is possible to rearrange the wavelet basis, which can illuminate the differences between the coherent and incoherent parts of the sequence, where incoherent refers to the part of the signal that has either no information or contradictory information. In effect, the procedure looks for the subspace characterised either by small components or by opposing components in the wavelet domain. Concerning the proposed application, an orthosis embedded with a surface electromyography (sEMG) measurement system was used to monitor the electrical activity of the forearm muscles during movement. This method was developed for the monitoring during rehabilitation. The proposed method is general, can be applied to any low frequency signal processing, and was built with wavelet algorithms from the WaveLab 850 library of the Stanford University (USA).

Chatter identification in end milling process using wavelet packets and Hilbert–Huang transform

Chatter detection is an important task to improve productivity and part quality in the machining process. Since measured signals from sensors are usually contaminated by background noise and other disturbances, it is necessary to find efficient signal processing algorithms to identify the chatter as soon as possible. This paper presents an effective chatter identification method for the end milling process based on the study of two advanced signal processing techniques, i.e., wavelet package transform (WPT) and Hilbert–Huang transform (HHT). The WPT works as a preprocessor to denoise the measured signals and hence the performance of the HHT is enhanced. The proposed method consists of four steps. First, the measured signals are decomposed by the WPT, so that the chatter signals are allocated in a certain frequency band. Secondly, wavelet packets with rich chatter information are selected and are used to reconstruct new signals. Thirdly, the reconstructed signals are analyzed with HHT to obtain a Hilbert–Huang spectrum, which is a full time–frequency–energy distribution of the signals. Finally, the mean value and standard deviation of the Hilbert–Huang spectrum are calculated to detect the chatter and identify its levels as well. The proposed method is applied to the end milling process and the experimental results prove that the method can identify the chatter effectively.

Fault Diagnosis of Rolling Bearing Based on Wavelet Package Transform and Ensemble Empirical Mode Decomposition

Rolling bearing is widely used in rotating mechanical system, and its operating state has great influence on accuracy, reliability, and the life of the whole mechanical system. Therefore, fault diagnosis of rolling bearing is indispensible for the health monitoring in rotating machinery system. Wavelet package transform (WPT) and envelope demodulation have been common methods in diagnosis of bearing fault, but the precision of diagnostic results is limited by the degree of damages on bearing. In this paper, a method based on WPT and ensemble empirical mode decomposition (EEMD) is proposed to detect the fault of rolling bearing and solve this problem. According to simulation and experimental results, it is effective in fault diagnosis of rolling bearing and is better than the method based on WPT and envelope spectrum while the faults get more serious.

Wavelet Package for the In-Process Monitoring of Gas Metal Arc Welding Mild Steel

The acoustic signals were strongly related to the behavior of the arc column, the molten pool and droplet transfer in gas metal arc welding(GMAW).In this study, all the work pieces (JIS SS400) were welded at the same speed with different currents using a gas metal arc; the purpose was to observe the relationship between these factors. Using a sound capture card and microphone we collected the audio signals generated by the welding. In this paper, wavelet package transformation (WPT) decomposes the sounds signals into 128 channels within the frequency domain. The properties of the audio signals were analyzed by the wavelet-based channel energy. The ROC (Receiver Operating Characteristic) parameters, (Ac, Se, Sp, ppv, and npv), were used for classification performance evaluation. Experimental results also show that acoustic emissions are useful in the quality control of plasma arc welding.

Wet Gas Flow Regime Identification Based on WPT and PNN

A novel noninvasive approach to the online flow regime identification for wet gas flow in a horizontal pipeline is proposed. Research into the flow-induced vibration response for the wet gas flow was conducted, with the conditions of pipe diameter 50 mm, pressure from 0.25 MPa to 0.35 MPa, Lockhart-Martinelli parameter from 0.02 to 0.6, and gas Froude Number from 0.5 to 2.7. The flow-induced vibration signals were measured by a vibration transducer installed by outside wall of pipe, and then the normalized energy features from different frequency bands in the vibration signals were extracted through 4-scale wavelet package transform (WPT) with mother wavelet db7. A probabilistic neural network (PNN) classifier with the extracted features as inputs was developed to identify the three typical flow regimes including stratified wavy flow, annular mist flow, and slug flow for wet gas flow. The results show that the method can identify effectively flow regimes and its identification accuracy arrives at above 92.1%. The noninvasive measurement approach has great application prospect in online flow regime identification.

Nonlinearity Analysis and Wavelet Package Transform of Measured Chatter Vibrations in Grinding Process

In grinding process, chatter vibrations known as self-excited vibrations, become increasingly problematic and must be avoided. Firstly, experimental measurements of chatter vibrations in a numerical control (NC) grinding machine tool are introduced for the case of a special alloy workpiece being carried on. Then, frequency spectra of chatter vibrations are analyzed. The wavelet package transform technique is used to describe original chatter signals in the term of scaled energy of frequency bands. At last, fractal dimensions of the reconstructed signals in consecutive frequency bands of chatter vibrations are calculated. These results are helpful for understanding of the nonlinearity of chatter vibrations.

Measurement of yogurt internal quality through using Vis/NIR spectroscopy

Abstract A nondestructive optical method for determining the sugar content and acidity of yogurt was investigated. Three types of preprocessing, S. Golay smoothing with multiplicative scatter correction (S. Golay smoothing with MSC), S. Golay 1st-Der and wavelet package transform (WPT), were used before the data were analyzed with chemometrics methods of partial least square (PLS). Spectral data sets as the logarithms of the reflectance reciprocal were analyzed to build a best model for predicting the sugar content and acidity of yogurt. A model using preprocessing of WPT with a correlation coefficient of 0.91 and 0.90, a root mean square error of prediction (RMSEP) of 0.36 and 0.04 showed an excellent prediction performance to sugar content and acidity. S. Golay smoothing with MSC was also finer, combined with the calibration and validation results. S. Golay 1st-Der was the worse preprocessing method in this experiment. In the paper, a multivariate calibration method of principal component artificial neural network (PC-ANN) was also established. In PC-ANN models, the scores of the principal components were chosen as the input nodes for the input layer of ANN. After adjusting the number of input nodes (principal components), hidden nodes, as well as learning rate and momentum of the network, a model with a correlation coefficient of 0.92 and 0.91, a root mean square error of prediction (RMSEP) of 0.33 and 0.04 showed an excellent prediction performance on sugar content and acidity. At the same time, the sensitive wavelengths corresponding to the sugar content and acidity of yogurt were proposed on the basis of regression coefficients by PLS.

Different Discrete Wavelet Transforms Applied to Denoising Analytical Data

Discrete wavelet transform (DWT) denoising contains three steps: forward transformation of the signal to the wavelet domain, reduction of the wavelet coefficients, and inverse transformation to the native domain. Three aspects that should be considered for DWT denoising include selecting the wavelet type, selecting the threshold, and applying the threshold to the wavelet coefficients. Although there exists an infinite variety of wavelet transformations, 22 orthonormal wavelet transforms that are typically used, which include Haar, 9 daublets, 5 coiflets, and 7 symmlets, were evaluated. Four threshold selection methods have been studied: universal, minimax, Stein's unbiased estimate of risk (SURE), and minimum description length (MDL) criteria. The application of the threshold to the wavelet coefficients includes global (hard, soft, garrote, and firm), level-dependent, data-dependent, translation invariant (TI), and wavelet package transform (WPT) thresholding methods. The different DWT-based denoising m...