Tunable Q-factor Wavelet Transform Research Articles

Resonance based separation and energy based classification of lung sounds using tunable wavelet transform

Background and objectiveThe locations and occurrence pattern of adventitious sounds in the respiratory cycle have critical diagnostic information. In a lung sound sample, the crackles and wheezes may exist individually or they may coexist in a successive/overlapping manner superimposed onto the breath noise. The performance of the linear time-frequency representation based signal decomposition methods has been limited in the crackle/wheeze separation problem due to the common signal components that may arise in both time and frequency domain. However, the proposed resonance based decomposition can be used to isolate crackles and wheezes which behave oppositely in time domain even if they share common frequency bands. MethodsIn the proposed study, crackle and/or wheeze containing synthetic and recorded lung-sound signals were decomposed by using the resonance information which is produced by joint application of the Tunable Q-factor Wavelet Transform and Morphological Component Analysis. The crackle localization and signal reconstruction performance of the proposed approach was compared with the previously suggested Independent Component Analysis and Empirical Mode Decomposition methods in a quantitative and qualitative manner. Additionally, the decomposition ability of the proposed approach was also used to discriminate crackle and wheeze waveforms in an unsupervised way by employing signal energy. ResultsResults have shown that the proposed approach has significant superiority over its competitors in terms of the crackle localization and signal reconstruction ability. Moreover, the calculated energy values have revealed that the transient crackles and rhythmic wheezes can be successfully decomposed into low and high resonance channels by preserving the discriminative information. ConclusionsIt is concluded that previous works suffer from deforming the waveform of the crackles whose time domain parameters are vital in computerized diagnostic classification systems. Therefore, a method should provide automatic and simultaneous decomposition ability, with smaller root mean square error and higher accuracy as demonstrated by the proposed approach.

A new fractal H-tree pattern based gun model identification method using gunshot audios

BackgroundGun model identification (GMI) is a complex issue for digital forensics examiners/professions. Because the GMI process is a highly costed process, and it is generally detected manually. A sound classification model is presented in this research to decrease the cost of the GMI and automate this process. Material and methodThe primary objective of this research is to present a new intelligent audio forensics tool. Therefore, a new gunshot dataset was collected, and the collected dataset includes 2130 audios of the 28 gun models. This dataset can be downloaded using http://web.firat.edu.tr/sdogan/Gun_S_Dogan.rar link. The presented fractal H-tree pattern-based classification method is applied to these audios to obtain results. This method has three fundamental phases, and these are feature extraction, the most informative features selection, and classification. This method uses both a fractal textural generator and statistical features. By deploying tunable q-factor wavelet transform (TQWT), a multileveled feature generation method is created to generate both low-level and high-level features. The recommended fractal H-tree pattern and statistical feature extraction functions generate features at each level. Neighborhood component analysis (NCA) chooses the most informative features. In the classification phase, the support vector machine (SVM) and k nearest neighbor (kNN) classifiers are used. ResultsThe recommended fractal H-tree pattern-based method yielded 96.10% and 90.40% by employing kNN and SVM, respectively. ConclusionThe calculated results and findings denoted the high classification capability of the presented fractal H-tree pattern-based method for gun model classification using gunshot audios. Also, this research shows that a new audio forensic tool can be developed by employing the presented method for GMI.

A new fractal pattern feature generation function based emotion recognition method using EEG

Abstract Electroencephalogram (EEG) signal analysis is one of the mostly studied research areas in biomedical signal processing, and machine learning. Emotion recognition through machine intelligence plays critical role in understanding the brain activities as well as in developing decision-making systems. In this research, an automated EEG based emotion recognition method with a novel fractal pattern feature extraction approach is presented. The presented fractal pattern is inspired by Firat University Logo and named fractal Firat pattern (FFP). By using FFP and Tunable Q-factor Wavelet Transform (TQWT) signal decomposition technique, a multilevel feature generator is presented. In the feature selection phase, an improved iterative selector is utilized. The shallow classifiers have been considered to denote the success of the presented TQWT and FFP based feature generation. This model has been tested on emotional EEG signals with 14 channels using linear discriminant (LDA), k-nearest neighborhood (k-NN), support vector machine (SVM). The proposed framework achieved 99.82% with SVM classifier.

Hybrid Multi-Channel EEG Filtering Method for Ocular and Muscular Artifact Removal Based on the 3D Spline Interpolation Technique

Abstract The present work develops a novel hybrid method for ocular and muscular artifact removal from electroencephalography (EEG) signals, EFICA-TQWT. It is a combination of efficient fast independent component analysis (EFICA) method with the tunable Q-factor wavelet transform (TQWT). The main contribution of this paper is to apply the 3D interpolation method in the filtering system. Three EEG datasets are used in this work, two healthy and one epileptic. The choice of subjects for each dataset is made with the help of an expert in physiology. The selection criterion adopted is the presence of muscular and ocular artifacts in the processed recordings. First, a noisy channel automatic classification is performed by the support vector machine (SVM) with radial basis function in order to delete the signal(s) corresponding to the noisiest channel(s) from each EEG recording. The results of the automatic classification by the SVM were compared with those found by the expert’s classification. An accuracy of 97.45%, a sensitivity of 86.66% and a 100% specificity are provided by the SVM classification. The hybrid method of artifact removal will be applied on the rest of the EEG channels of international 10/20 system for each subject. Then, a reconstruction of the eliminated channel signal(s) will be performed in order to obtain a well-filtered signal. The proposed filtering process is evaluated by calculating the mean squared error (MSE) and the signal to noise ratio (SNR). Both for the healthy and pathological EEG datasets, a comparative study of the proposed method (EFICA-TQWT) and other filtering techniques (Fast-ICA, DWT, TQWT and EFICA) is generated. The EFICA-TQWT method gave the best results with a minimum of MSE and a maximum of SNR, more particularly in the case of the application of the 3D interpolation method. Besides, in order to optimize the computing time of the proposed system, a parallel implementation of this filtering system is developed based on graphical processing units using compute unified device architecture.

Fault Diagnosis of Gearbox based on ITD-Tunable Q-Factor Wavelet Transform

Gearboxes are an important part of the mechanical drives element that provides the several applications like automotive industry, wind turbine industry and power plant industry, etc. The condition monitoring of the gearbox reduces its operational cost, maintenance cost and avoid hazardous losses. The features selected forthe health status of the gearbox has important parameter to calculate classification accuracy. In the current study the intrinsic time-scale decomposition (ITD) and tunable Q-factor wavelet transform (TQWT) are used to diagnose the faults in the gear. The ITD method decomposed the input signal into the baseline signal with instantaneous parameters of signal and sequence of the proper rotation components (PRCs). The PRC of higher kurtosis value is the input signal for TQWT. The TQWT is a discrete wavelet transform and decomposed the vibration signals of the gearbox into sub-bands. The feature vector is calculated for each sub-band of the TQWT. The proposed approach is analyzed by the classification accuracy of the feature vector. The recommended method is evaluated using experimental data of 2009 PHM Data of gearbox under various health conditions. The SVM and KNN methods are investigated that the improved classification accuracy with ITD-TQWT model are 97.9% and 96.9% respectively.

Oscillation based permutation entropy calculation as a dynamic nonlinear feature for health monitoring of rolling element bearing

Aiming at overcoming the limitations of permutation entropy (PE) in dynamic bearing health monitoring (Healthy-initial fault inception-severe fault stage) , in this paper, PE calculation based on tunable Q factor wavelet transform (TQWT) is implemented to quantify bearing health. Firstly, weaknesses of PE in depicting bearing health are studied through numerically simulated signals. Secondly, based on the biasness of PE towards bearing fundamental signal component (FSC), oscillation based method TQWT is used to separate the FSC from the bearing signal to calculate its PE. Since, the proposed PE calculation method includes an oscillation based procedure, the resultant feature value is termed as oscillation based PE (OBPE). Selection of the parameters needed for OBPE calculation are discussed in details. Two public bearing run to failure experiments have confirmed that OBPE provides a much better result than direct application of PE in dynamic bearing health monitoring.

Infection Detection in Cystic Fibrosis Patients based on Tunable Q-factor Wavelet Transform of Respiratory Sound Signal and Ensemble Decision

Most adult Cystic Fibrosis (CF) patients frequently suffer from Pseudomonas aeruginosa (PA) infection, which is strongly associated with inflammation, lung destruction, and increased mortality. Diagnosis of PA infection in the primary stage is essential to initiate the treatment and reduce the risk of chronic infection. Sputum culture is the gold standard ‎for infection detection, but it is time-consuming. The objective of this study was to suggest and examine a method to decide about PA infection status in CF patients based only on their respiratory sound. Respiratory sounds were recorded from 36 CF patients. Some features which were generated from Tunable Q-factor wavelet transform(TQWT) components, were investigated. The features were fed into Support Vector Machine and also Ensemble classifier. The proposed method achieved an accuracy of 90.3% in identifying PA infection in CF patients. Furthermore, the probability of categorizing respiratory sounds as PA CF decreased significantly after the treatment of PA infection(P-value<0.003). Moreover, the method had a satisfactory performance in the presence of noises and artifacts. The developed method represents a novel approach to the diagnosis of PA infection in CF patients based only on respiratory sound signals, which is a necessary and innovative approach for early diagnosis of PA infection.

Automatic amyotrophic lateral sclerosis detection using tunable Q-factor wavelet transform

Early diagnosis of amyotrophic lateral sclerosis (ALS) based on electromyography (EMG) is crucial. The processing of a non-stationary EMG signal requires powerful multi-resolution methods. Our study analyzes and classifies the EMG signals. In the present work, we introduce a novel flexible method for classification of EMG signals using tunable Q-factor wavelet transform (TQWT). Different sub-bands generated by the TQWT technique were served to extract useful information related to energy and then the calculated features were selected using a filter selection (FS) method. The effectiveness of the feature selection step resulted not only in the improvement of classification performance but also in reducing the computation time of the classification algorithm. The selected feature subsets were used as inputs to multiple classifier algorithms, namely, k-nearest neighbor (k-NN), least squares support vector machine (LS-SVM) and random forest (RF) for automated diagnosis. The experimental results show better classification measures with k-NN classifier compared with LS-SVM and RF. The robustness of the classification task was tested using a ten-fold cross-validation method. The outcomes of our proposed approach can be exploited to aid clinicians in neuromuscular disorders detection.

Multistage Fault Feature Extraction of Consistent Optimization for Rolling Bearings Based on Correlated Kurtosis

Fault diagnosis of rolling bearings is not a trivial task because fault-induced periodic transient impulses are always submerged in environmental noise as well as large accidental impulses and attenuated by transmission path. In most hybrid diagnostic methods available for rolling bearings, the problems lie in twofolds. First, most optimization indices used in the individual signal processing stage do not take the periodical characteristic of fault transient impulses into consideration. Second, the individual stages make use of different optimization indices resulting in inconsistent optimization directions and possibly an unsatisfied diagnosis. To solve these problems, a multistage fault feature extraction method of consistent optimization for rolling bearings based on correlated kurtosis (CK) is proposed where maximum correlated kurtosis deconvolution (MCKD) is employed to attenuate the influence of transmission path followed by tunable Q factor wavelet transform (TQWT) to further enhance fault features by decomposing the preprocessed signals into multiple subbands under different Q values. The major contribution of the proposed approach is to consistently use CK as an optimization index of both MCKD and TQWT. The subband signal with the maximum CK which is an index being able to measure the periodical transient impulses in vibration signal yields an envelope spectrum, from which fault diagnosis is implemented. Simulated and experimental signals verified the effectiveness and advantages of the proposed method.

Diagnosis of Ball Bearing Faults Using Double Decomposition Technique

The rolling element bearing is one of the most significant components of any rotating machinery. However, the foremost cause of malfunction in any rotating machine is due to defects like cracks, dents, spall, pits, etc. in ball bearings. Early diagnosis of these bearing faults is highly essential to avoid an accidental shutdown of rotating machinery. In the present work, a novel technique of bearing fault diagnosis is proposed following double decomposition of the vibration activity. The experimentally recorded vibration signals are processed through two stages of decomposition viz. Empirical Mode Decomposition and Tunable Q-factor Wavelet Transform based Time-Frequency decomposition. Subsequently, sub-bands of decomposed time-frequency activity are acquired and discriminable features are computed. Fractal Dimension (FD) based features are extracted from each decomposed sub-band as complexity measures of time-frequency sub-bands. In order to classify bearing faults, a Support Vector Machine classifier is trained with acquired features and classification performance is evaluated. The results of classification reveal that the proposed double decomposition technique is a potential candidate in extracting viable vibration signatures for fault identification. The study is conducted on Case Western Reserve University bearing datasets.

TQWT-assisted statistical process control method for condition monitoring and fault diagnosis of bearings in high-speed rail

Condition monitoring and fault diagnosis of bearings in high-speed rail have attracted considerable attention in recent years, however, it’s still a hard work due to harsh environments with high speeds and high loads. A statistical condition monitoring and fault diagnosis method based on tunable Q-factor wavelet transform (TQWT) is developed in this study. The core idea of this method is that the TQWT can extract oscillatory behaviors of bearing faults. The vibration data under the normal condition are first decomposed by the TQWT into different wavelet coefficients. Two health indicators are then formulated by the dominant wavelet coefficients and the remaining coefficients for condition monitoring. The upper control limits are established using the one-sided confidence limit of the indicators by using the non-parametric bootstrap scheme. The Shewhart control charts on multiscale wavelet coefficients are constructed for fault diagnosis. We demonstrate the effectiveness of the proposed method by monitoring and diagnosing single and multiple railway axle bearing defects. Furthermore, the comparison studies show that the proposed method outperforms a traditional time-frequency method, the Wigner-Ville distribution method.

Magnetic Anomaly Feature Extraction Using the Tunable Q-factor Wavelet Transform Based on Non-convex Overlapping Group Shrinkage

Extraction of the magnetic anomaly signal is one of the difficulties in the magnetic anomaly detection as the weak features extracted are easily disturbed by strong background noise. To address this problem, a sparse feature extraction method based on the tunable Q-factor wavelet transform and overlapping group shrinkage is developed in this paper. Compared with the traditional wavelet transform, the proposed method has excellent characteristics, which can flexibly tune the Q-factor according to the oscillation characteristics of the useful features. In this way, the sparsity of extracted features can be induced more effectively. In addition, the non-convex overlapping group shrinkage can effectively extract weak features from signals with group property, enhancing the extraction accuracy of features. The practical experiment verifies the effectiveness of the proposed method in the magnetic anomaly detection.

A Novel Islanding Detection Technique for a Resilient Photovoltaic-Based Distributed Power Generation System Using a Tunable-Q Wavelet Transform and an Artificial Neural Network

Finding an appropriate technique to detect an islanding issue is one of the major challenges associated with the design of a resilient grid-linked photovoltaic-based distributed power generation (PV-DPG) system. In general, the technique used for islanding detection must be able to sense the disruptions from the electric grid and quickly disconnect PV-DPG from the grid. The quick disconnection of PV-DPG mostly avoids power quality problems, damage to power assets, voltage stability issues, and frequency instability. In this paper, a new islanding detection technique that is based on tunable Q-factor wavelet transform (TQWT) and an artificial neural network (ANN) is proposed for PV-DPG. The proposed approach consists of two steps: in the first step, the vital detection parameters are computed by performing simulations considering all possible switching transients, islanding events, and faults from the grid side. Then, the decomposition of obtained signals is done using TQWT on different levels. Using the obtained coefficients, at each level, features such as range, minimum, mean, standard deviation, maximum, energy, and log energy entropy are computed. The optimal feature set was selected as the input for the second step. The classification of the non-islanding and islanding states for PV-DPG is made using the ANN classifier in the second step, which achieved an accuracy of 98%. The results representing the efficiency of the proposed approach in noisy and non-noisy environments are also explained. Overall, it is understood that the proposed islanding detection technique would provide suitable insights to detect an islanding issue.

Rolling Bearing Fault Feature Extraction Based on Adaptive Tunable Q-Factor Wavelet Transform and Spectral Kurtosis

The fault feature of the rolling bearing is difficult to extract when weak fault occurs and interference exists. The tunable Q-factor wavelet transform (TQWT) can effectively extract the weak fault characteristic of the rolling bearing, but the manual selection of the Q-factor affects the decomposition result and only using TQWT presents interference. Aiming at the above problems, an adaptive tunable Q-factor wavelet transform (ATQWT) and spectral kurtosis (SK) method is proposed in this paper. Firstly, the method applies particle swarm optimization (PSO) to seek the optimized Q-factor for avoiding manual selection, which uses the kurtosis value of the transient impact component as the particle fitness function. The rolling bearing fault signal is decomposed into continuous oscillation component and transient impact component containing fault feature by the optimized Q-factor. Then, due to the presence of interference in the decomposition result of ATQWT, the SK analysis of the transient impact component is used to determine the frequency band of periodic impact component characterizing fault feature by fast kurtogram. Finally, the band-pass filter established through the above frequency band is employed to filter the interference in the transient impact component. Simulation and experimental results indicate that the ATQWT can highlight the periodic impact component characterizing rolling bearing fault feature, and the SK can filter interference in the transient impact component, which improves feature extraction effect and has great significance to enhance fault diagnosis accuracy of the rolling bearing. Compared with EEMD-TQWT and TQWT-SK, the fault feature extracted by the proposed method is prominent and effective.

A new approach for the detection of abnormal heart sound signals using TQWT, VMD and neural networks

Phonocardiogram (PCG) plays an important role in evaluating many cardiac abnormalities, such as the valvular heart disease, congestive heart failure and anatomical defects of the heart. However, effective cardiac auscultation requires trained physicians whose work is tough, laborious and subjective. The objective of this study is to develop an automatic classification method for anomaly (normal vs. abnormal) detection of PCG recordings without any segmentation of heart sound signals. Hybrid signal processing and artificial intelligence tools, including tunable Q-factor wavelet transform (TQWT), variational mode decomposition (VMD), phase space reconstruction (PSR) and neural networks, are utilized to extract representative features in order to model, identify and detect abnormal patterns in the dynamics of PCG system caused by heart disease. First, heart sound signal is decomposed into a set of frequency subbands with a number of decomposition levels by using the TQWT method. Second, VMD is employed to decompose the subband of the heart sound signal into different intrinsic modes, in which the first four intrinsic modes contain the majority of the heart sound signal’s energy and are considered to be the predominant intrinsic modes. They are selected to construct the reference variable for analysis. Third, phase space of the reference variable is reconstructed, in which the properties associated with the nonlinear PCG system dynamics are preserved. Three-dimensional PSR together with Euclidean distance has been utilized to derive features, which demonstrate significant difference in PCG system dynamics between normal and abnormal heart sound signals. Finally, PhysioNet/CinC Challenge heart sound database is used for evaluation and the synthetic minority over-sampling technique method is applied to balance the datasets. By using the 10-fold cross-validation style, experimental results demonstrate that the proposed features with dynamical neural networks based classifier yield classification performance with sensitivity, specificity, overall score and accuracy values of 97.73 $$\%$$ , 98.05 $$\%$$ , 97.89 $$\%$$ , and 97.89 $$\%$$ , respectively. The results verify the effectiveness of the proposed method which can serve as a potential candidate for the automatic anomaly detection in the clinical application.

A novel sparse feature extraction method based on sparse signal via dual-channel self-adaptive TQWT

Sparse signal is a kind of sparsematrices which can carry fault information and simplify the signal at the same time. This can effectively reduce the cost of signal storage, improve the efficiency of data transmission, and ultimately save the cost of equipment fault diagnosis in the aviation field. At present, the existing sparse decomposition methods generally extract sparse fault characteristics signals based on orthogonal basis atoms, which limits the adaptability of sparse decomposition. In this paper, a self-adaptive atom is extracted by the improved dual-channel tunable Q-factor wavelet transform (TQWT) method to construct a self-adaptive complete dictionary. Finally, the sparse signal is obtained by the orthogonal matching pursuit (OMP) algorithm. The atoms obtained by this method are more flexible, and are no longer constrained to an orthogonal basis to reflect the oscillation characteristics of signals. Therefore, the sparse signal can better extract the fault characteristics. The simulation and experimental results show that the self-adaptive dictionary with the atom extracted from the dual-channel TQWT has a stronger decomposition freedom and signal matching ability than orthogonal basis dictionaries, such as discrete cosine transform (DCT), discrete Hartley transform (DHT) and discrete wavelet transform (DWT). In addition, the sparse signal extracted by the self-adaptive complete dictionary can reflect the time-domain characteristics of the vibration signals, and can more accurately extract the bearing fault feature frequency.

Improved TQWT for marine moving target detection

Under the conditions of strong sea clutter and complex moving targets, it is extremely difficult to detect moving targets in the maritime surface. This paper proposes a new algorithm named improved tunable Q-factor wavelet transform (TQWT) for moving target detection. Firstly, this paper establishes a moving target model and sparsely compensates the Doppler migration of the moving target in the fractional Fourier transform (FRFT) domain. Then, TQWT is adopted to decompose the signal based on the discrimination between the sea clutter and the target's oscillation characteristics, using the basis pursuit denoising (BPDN) algorithm to get the wavelet coefficients. Furthermore, an energy selection method based on the optimal distribution of sub-bands energy is proposed to sparse the coefficients and reconstruct the target. Finally, experiments on the Council for Scientific and Industrial Research (CSIR) dataset indicate the performance of the proposed method and provide the basis for subsequent target detection.

Surface EMG signal classification using TQWT, Bagging and Boosting for hand movement recognition

Hands play a significant role in grasping and manipulating different objects. The loss of even a single hand have impact on the human activity. In this regard, a prosthetic hand is an appealing solution for the subjects who lost their hands. The surface electromyogram (sEMG) plays a vital role in the design of prosthesis hands. The ensemble classifiers achieve better performance by using a weighted combination of several classifier models. Hence, in this paper, the feasibility of the Bagging and the Boosting ensemble classifiers is assessed for the basic hand movement recognition by using sEMG signals, which were recorded during the grasping movements with various objects for the six hand motions. So, the novelty of the current study is the development of an ensemble model for hand movement recognition based on the tunable Q-factor wavelet transform (TQWT). The proposed method consists of three steps. In the first step, MSPCA is used for denoising. In the second step, a novel feature extraction method, TQWT is used for feature extraction from the sEMG signals, then, statistical values of TQWT sub-bands are calculated. In the last step, the obtained feature set is used as input to an ensemble classifier for the identification of intended hand movements. Performances of the Bagging and the Boosting ensemble classifiers are compared in terms of different performance measures. Using TQWT extracted features along with the presented the Adaboost with SVM and the Multiboost with SVM classifier results in a classification accuracy up to 100%. Hence, the results have shown that the proposed framework has achieved overall better performance and it is a potential candidate for the prosthetic hands control.

Resonance-based sparse adaptive variational mode decomposition and its application to the feature extraction of planetary gearboxes.

Due to the assumption that the VMD technique is essentially a set of adaptive Wiener filter banks and its performance depends to a large extent on the preset parameter K (the number of decomposition). A new method named resonance-based sparse adaptive variational mode decomposition (RSAVMD) is proposed for the decomposition of planetary gearbox vibration signals. Tunable Q-Factor Wavelet Transform (TQWT) and morphological component analysis (MCA) are introduced to decompose the original signal into high and low resonance components. High resonance components containing planetary gearbox signals are screened for analysis. At the same time, Quality factor is used to select the number of Variational mode decomposition (VMD) adaptively. This method was applied in fault diagnosis of planetary gearbox. Compared with VMD, RASVMD could extract fault characteristic frequency of planetary gearbox accurately, but VMD lost part of fault information, showing the superiority of RSAVMD. Simultaneously, the selection method of VMD decomposition number in literature was cited, and it was found that the decomposition number selected by the method in this paper was more accurate.

Tunable Q-factor wavelet transform of acoustic emission signals and its application on leak location in pipelines

The tunable Q-factor wavelet transform (TQWT) is a discrete wavelet transform realized by the iterative two-channel filter group and the discrete Fourier transform (DFT). In this paper, the TQWT is applied to the extraction of leakage signals of the water pipeline network. By using the maximum principle of kurtosis, the Q-factor and the number of decomposition layer were determined to overcome the disadvantages of prefixed Q-factor and number of decomposition layers. The combination of kurtosis and envelope entropy was used to select the subband with abundant information. And then, correlation analysis was conducted further to locate the leakage point. Furthermore, a laboratory water pipe and an outdoor actual water pipe system were built to study the leakage signal. By analyzing the leakage signal from both experimental system, it can be found that the leakage signal characteristics of different leakage points for actual water pipe are all in the first layer, and the positioning error of each leakage point is within 1%. It indicates that TQWT can effectively extract the leakage components from the leakage signal of low SNR and has a higher location accuracy of the leak point.