Dual-tree Complex Wavelet Packet Research Articles

Generalized Shannon entropy sparse wavelet packet transform for fault detection of traction motor bearings in high-speed trains

An effective structural health monitoring method of traction motor bearings is a powerful guarantee for the safety operation of high-speed trains. However, it is exceptionally difficult to detect bearing fault characteristics from the vibration signals of traction motor bearings operating at high rotational speeds. In this scenario, a generalized Shannon entropy sparse wavelet packet transform (GSWPT) for fault detection of motor bearings is proposed in this paper. Firstly, a generalized Shannon entropy sparse regularization method is proposed to obtain sparse wavelet reconstruction coefficients by extending the definition of the Shannon information entropy, and the non-convex sparse regularization function is minimized by synergistic swarm optimization algorithm. Then, the wavelet node coefficients are weighted according to the second-order cyclostationarity index of the wavelet packet node to further enhance the sparsity of the reconstructed signal. Moreover, the optimal decomposition level of GSWPT is adaptively selected by the maximum sparsity and cyclostationarity criterion. Particularly, in order to verify the bearing fault detection performance of GSWPT in practical engineering, a bearing fault dynamic model of traction motor in high-speed train was established based on Hertz contact theory and the fourth-order Runge-Kutta method to obtain simulated data under strong Gaussian white noise, and a corresponding test platform was constructed to collect experimental data under different operating conditions. Finally, the applications on the simulated and experimental signals of traction motor bearings in high-speed trains demonstrate that GSWPT significantly outperforms the conventional wavelet packet transform, dual-tree complex wavelet packet transform, blind deconvolution, modal decomposition, and Infogram methods to some extent for fault detection.

An adaptive reweighted-Kurtogram for bearing fault diagnosis under strong external impulse noise

In vibration-based fault diagnosis of rotating machinery, strong impulse noise constitutes a widely encountered source of external interference, thereby posing significant challenges to the effective extraction of diagnostic information. In this context, an adaptive reweighted-Kurtogram (ARKurtogram) method is proposed. The bearing transient fault signatures are accurately extracted through two aspects – that is, precise frequency band division and robust band selection indicator. Specifically, we use the dual-tree complex wavelet packet transform (DTCWPT) as the band division tool and propose the sub-bands rearranged and ensemble DTCWPT which addresses the band disorder problem, yields far richer band division patterns and maintains excellent performance of DTCWPT in signal decomposition. Meanwhile, we introduce a robust indicator characterizing periodic fault impulses to accurately select the fault band. In comparison with the classical kurtosis and the advanced reweighted-kurtosis, this indicator is insensitive to strong external impulse noise and its calculation process contains a sliding window averaging approach which makes the calculation entirely automated without human intervention. The validity of the ARKurtogram is investigated with the analysis of vibration signals measured from fault-injection experiments. The comparisons with the classical fast Kurtogram method and the advanced Autogram method highlight the advantages of the method in extracting bearing transient fault signatures from complex signals with strong external impulse noise. It shows high potential in the application of industrial bearing fault diagnosis.

Vibration-based looseness identification of bolted structures via quasi-analytic wavelet packet and optimized large margin distribution machine

Bolted joints are the most widely utilized connection types in industries, and therein looseness identification of bolted structures is of great significance to guarantee structural reliability. In this article, a comprehensive study of bolt looseness identification under random excitation is presented. To fulfill this task, this research focuses on three prominent difficulties, including nonstationary signal processing, subtle feature extraction, and robust state classification. First, a novel filter bank structure of quasi-analytic dual-tree complex wavelet packet transform is constructed to analyze the measured vibration response signals, for purpose of capturing subtle feature information. Then, multiple features are extracted from subband signals to capture the variations of dynamic characteristics, and sensitive features are selected by Laplacian score to construct the low-dimensional feature set. Subsequently, a novel classifier with better generalization performance, named large margin distribution machine, is optimized with the wavelet kernel function and the whale optimization algorithm, in order to handle the intrinsic uncertainty related to the looseness states of bolted structures. After feeding the low-dimensional feature set, the proposed classifier is trained to identify looseness states of bolted structures. Finally, experiments of a two-bolt lapped beam under random excitation are conducted to verify the effectiveness of the proposed method, and two typical loading conditions (paired-bolt looseness and single-bolt looseness) are considered. Besides, the superiority of the proposed method is demonstrated by comparing with other analogical methods. This research can provide a promising implement in practical applications of bolt looseness identification under random excitation.

Dual-tree complex wavelet packet transform and regularized extreme learning machine-based feature extraction and classification of power quality disturbances

Dual-tree complex wavelet packet transform and regularized extreme learning machine-based feature extraction and classification of power quality disturbances

Wavelet-based Parametric Feature Subset Selection for Speaker and Accent Recognition using Genetic Algorithm

Research on speaker and accent recognition studies using the Malay language in the field of Automatic Speech Recognition (ASR) is limited, with most studies focusing on speech recognition. This study proposes to increase the performance Malaysian speakers and accent recognition using wavelets transform, namely Wavelet Packet Transform (WPT) and Dual-Tree Complex Wavelet Packet Transform (DT-CWPT). A variety of feature extraction combinations, including conventional Mel Frequency Cepstral Coefficients (MFCC), Linear Predictive Coding (LPC) and wavelets transform, were implemented to compare the effectiveness of the proposed method. Although the proposed approach resulted in improved detection rate, it faced challenges in terms of high feature dimensionality and increased computation time. To address these issues, the Genetic Algorithm (GA) approach has been adopted to reduce the number of irrelevant features, accelerate the learning system and achieve better performance. The extracted features were trained using various classifiers, including k-Nearest Neighbors (k-NN), Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The experimental results showed that the best speaker recognition accuracy was 97.33% for English numbers using SVM classifier and 96.02% for Malay words using the ELM classifier with a combination of wavelets, LPC and MFCC features. For accent recognition, the ELM classifier yielded the best performance, achieving 95.28% accuracy for English numbers with a combination of wavelets and MFCC features and 96.72% for Malay words using combined feature extraction of wavelets, LPC and MFCC feature extraction. It can be concluded that Malay words yielded better recognition rates than English numbers. Furthermore, use of GA effectively reduced the overall number of features while maintaining high accuracy level.

Enhanced generalized nonlinear sparse spectrum based on dual-tree complex wavelet packet transform for bearing fault diagnosis

The generalized nonlinear sparse spectrum (GNSS), as an improved fast kurtogram (FK) method, effectively suppresses the interference of abnormal signals through nonlinear preprocessing and sparse enhancement. However, the GNSS method inherits the shortcoming of the traditional FK method, using finite impulse response filters to process nonstationary signals, which limits the accuracy of fault extraction. Therefore, more precise filters should be developed to further improve the performance of fault features. Inspired by this, this paper introduces the dual-tree complex wavelet packet transform (DTCWPT) into the sparse spectrum, and proposes an enhanced generalized nonlinear sparse spectrum (EGNSS) for bearing fault diagnosis. Firstly, nonlinear preprocessing is performed on the input signal to weaken the interference of abnormal impacts. Secondly, the generalized pq-mean value of each subband obtained by DTCWPT is calculated. Finally, the sparse spectrum is constructed and the signal reconstruction is performed on the frequency band where the maximum generalized pq-mean value is located for the envelope analysis. Simulation signals and experimental bearing fault signals have been applied to demonstrate the superiority of the proposed EGNSS in improving fault performance and accuracy.

Power normalized cepstral robust features of deep neural networks in a cloud computing data privacy protection scheme

Deep Neural Networks (DNNs) have developed rapidly in data privacy protection applications such as medical treatment and finance. However, DNNs require high-speed and high-memory computers in terms of computation, otherwise training can be very lengthy. Furthermore, DNNs are often not available in resource-constrained mobile devices. Therefore, training and executing DNNs are increasingly using cloud computing. In the paper, the Power Normalized Cepstrum-based Robust Feature Detector (PNC-RFD), with deep learning in the cloud computing, is proposed for data privacy protection. The proposed PNC-RFD extracts a specified number of signal segments of high robustness used to embed and extract various data. For the sake of embedding and extracting the data, a method of information hiding employing Dual-Tree Complex Wavelet Packet Transform (DT CWPT) is therefore presented. The presented scheme simultaneously embeds multiple data into coefficients of the DT CWPT of signal segments. By embedding the data into the orthogonal spaces, the proposed method ensures the independent extraction of the multiple data. In line with the performance analysis, the superiority of the presented scheme is elaborated through making the comparison with the current state-of-the-art methods.

Early chatter detection in robotic milling under variable robot postures and cutting parameters

Chatter in robotic milling is closely related to robot postures and cutting parameters. The chatter frequency varies over a wide range compared with that in the milling process using traditional machine tools. These features pose a significant challenge to chatter detection in robotic milling. The objective of this paper is to propose a novel approach for early and reliable chatter detection in robotic milling under variable robot postures and cutting parameters. First, the inertance frequency response function under each robot posture is used to filter the corresponding vibration signals. This minimizes the influence of robot postures on the measured vibration signals. Second, based on the filtered vibration data, a dual-tree complex wavelet packet transform is applied to extract the energy of different frequency bands, from which the fractional energy entropy is obtained to characterize the chatter state. Therefore, the chatter state can be characterized as independent of the variable cutting parameters and chatter frequencies. Third, online chatter detection is achieved using the sequential probability ratio test, with which significance tests for the chatter states are conducted. Consequently, the detection threshold does not need to be manually set. Extensive case studies demonstrate that the proposed approach can realize early and reliable chatter detection in robotic milling under variable robot postures and cutting parameters.

Reweighted-Kurtogram with sub-bands rearranged and ensemble dual-tree complex wavelet packet transform for bearing fault diagnosis

The Fast Kurtogram (FK) is a widely used resonance demodulation technique for bearing fault diagnosis. In this paper, a novel method termed Reweighted-Kurtogram with sub-bands rearranged and ensemble dual-tree complex wavelet packet transform (SRE-DTCWPT) is proposed to improve the performance of the FK from the aspects of band division and optimal band selection indicator. To obtain an excellent band division, the SRE-DTCWPT is first developed. It retains the main advantages of DTCWPT and meanwhile addresses the two key issues of frequency sub-bands disorder and frequency bands leakage. Then, a new robust evaluating indicator called reweighted kurtosis is defined. It solves the problem of kurtosis being sensitive to strong impulse interferences. Furthermore, the proposed method involves a set of envelope analysis approaches developed on different cases of fault signals to realize the enhanced identification of the bearing diagnostic information. Two simulated signals and actual bearing signals regarding different practical cases are employed to investigate the effectiveness of the proposed method. In addition, the proposed method is compared with the FK, and the results verify that the proposed method shows high potentials for extracting bearing diagnostic information from complex vibration signals.

Fault Diagnosis of Rolling Bearings Using Dual-Tree Complex Wavelet Packet Transform and Time-Shifted Multiscale Range Entropy

Most existing fault diagnosis methods for rolling bearings are single-stage; these methods can only judge the fault type but cannot detect the existence of a fault. Moreover, the uncertainty in pattern recognition may lead to misclassification of healthy bearings as faulty ones. This paper proposes a multistage fault detection scheme for rolling bearings. In the first stage, the sensitivity of the range entropy to bearing failure is used to define a threshold, based on which the health status of the bearing is judged. If the unknown bearing is judged to be faulty, the next stage is implemented. In the second stage, a fault feature extraction method based on dual-tree complex wavelet packet transform (DTCWPT), time-shifted multiscale range entropy (TSMRE), and t-distributed stochastic neighbor embedding (t-SNE) is proposed, and a random forest (RF) discriminator is used for fault classification. To achieve the desired performance of fault classification, a new coarsening approach for complexity measurement called TSMRE is developed on the basis of the range entropy (RE). First, the RE value of each time-shifted coarse-grained time series is calculated, and the TSMRE is obtained by averaging the entropy values. The TSMRE improves the coarse-graining processing of the MRE and enhances the stability and reliability of the algorithm. In addition, it can obtain more information from short time series using the time-shifted coarse-grained technology. Therefore, it is less dependent on the length of the original time series. Two sets of rolling bearing data are used for this experiment. The fault recognition rate of each category of samples is 100%. Therefore, the proposed multistage fault diagnosis method can pre-screen healthy bearings and accurately identify the failure types of faulty bearings.

Volume Fraction Detection of Long Period Stacking Ordered Phases in Mg Alloys Based on Dual-Tree Complex Wavelet Packet Transform

Volume Fraction Detection of Long Period Stacking Ordered Phases in Mg Alloys Based on Dual-Tree Complex Wavelet Packet Transform

Stratification of High-Risk Hypertensive Patients Using Hybrid Heart Rate Variability Features and Boosting Algorithms

Hypertension is a global challenge to the public health which can easily lead to life-threatening vascular diseases unless control measures are adopted. Considering the prevalence of vascular diseases and their fatality, early detection of high-risk patients is an important problem in the present world. Heart rate variability (HRV) analysis can be an effective prognostic tool to identify the characteristics of vulnerable patients, considering its reliability in predicting sudden cardiac deaths. However, challenge lies in identifying tenuous differences in HRV between the low-risk and high-risk patients at the early stage. With this motivation, we propose a hybrid approach based on dual-tree complex wavelet packet transform (DTCWPT) and linear time domain as well as nonlinear analysis of HRV signal to extract multitudinous features. A key issue before the HRV analysis of such patients is the presence of marked amount of ectopic beats, which is addressed by using time-varying auto regressive (TVAR) technique. The features extracted from TVAR edited HRV signals are shortlisted by minimum redundancy maximum relevance algorithm for an efficient classifier modeling. Furthermore in this study, we propose to use cost-sensitive RUSBoost (CS-RUSBoost) algorithm for handling the class imbalance problem of the data. A comparative performance evaluation of CS-RUSBoost with RUSBoost, SMOTEBoost, asymmetric AdaBoost algorithm shows a superior result by CS-RUSBoost with G-mean of 0.9352 and F1 score of 0.9347.

An Integrated Fault Identification Approach for Rolling Bearings Based on Dual-Tree Complex Wavelet Packet Transform and Generalized Composite Multiscale Amplitude-Aware Permutation Entropy

The health condition of rolling bearings, as a widely used part in rotating machineries, directly influences the working efficiency of the equipment. Consequently, timely detection and judgment of the current working status of the bearing is the key to improving productivity. This paper proposes an integrated fault identification technology for rolling bearings, which contains two parts: the fault predetection and the fault recognition. In the part of fault predetection, the threshold based on amplitude-aware permutation entropy (AAPE) is defined to judge whether the bearing currently has a fault. If there is a fault in the bearing, the fault feature is adequately extracted using the feature extraction method combined with dual-tree complex wavelet packet transform (DTCWPT) and generalized composite multiscale amplitude-aware permutation entropy (GCMAAPE). Firstly, the method decomposes the fault vibration signal into a set of subband components through the DTCWPT with good time-frequency decomposing capability. Secondly, the GCMAAPE values of each subband component are computed to generate the initial candidate feature. Next, a low-dimensional feature sample is established using the t-distributed stochastic neighbor embedding (t-SNE) with good nonlinear dimensionality reduction performance to choose sensitive features from the initial high-dimensional features. Afterwards, the featured specimen representing fault information is fed into the deep belief network (DBN) model to judge the fault type. In the end, the superiority of the proposed solution is verified by analyzing the collected experimental data. Detection and classification experiments indicate that the proposed solution can not only accurately detect whether there is a fault but also effectively determine the fault type of the bearing. Besides, this solution can judge the different faults more accurately compared with other ordinary methods.

OPTIMIZATION OF DUAL-TREE COMPLEX WAVELET PACKET BASED ENTROPY FEATURES FOR VOICE PATHOLOGIES DETECTION

The Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) has been successfully implemented in numerous field because it introduces limited redundancy, provides approximately shift-invariance and geometrically oriented signal in multiple dimensions where these properties are lacking in traditional wavelet transform. This paper investigates the performance of features extracted using DT-CWPT algorithms which are quantified using k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM) classifiers for detecting voice pathologies. Decomposition is done on the voice signals using Shannon and Approximate entropy (ApEn) to signify the complexity of voice signals in time and frequency domain. Feature selection methods using the ReliefF algorithm and Genetic algorithm (GA) are applied to obtain the optimum features for multiclass classification. It is observed that the best accuracies obtained using DT-CWPT with ApEn entropy are 91.15 % for k-NN and 93.90 % for SVM classifiers. The proposed work provides a promising detection rate for multiple voice disorders and is useful for the development of computer-based diagnostic tools for voice pathology screening in health care facilities.

Rolling Bearing Fault Diagnosis Based on Sensitive Feature Transfer Learning and Local Maximum Margin Criterion under Variable Working Condition

In real industrial scenarios, the working conditions of bearings are variable, and it is therefore difficult for data-driven diagnosis methods based on conventional machine-learning techniques to guarantee the desirable performance of diagnosis models, as the models assume that the distributions of both the training and testing data are the same. To enhance the performance of the fault diagnosis of bearings under different working conditions, a novel diagnosis framework inspired by feature extraction, transfer learning (TL), and feature dimensionality reduction is proposed in this work, and dual-tree complex wavelet packet transform (DTCWPT) is used for signal processing. Additionally, transferable sensitive feature selection by ReliefF and the sum of mean deviation (TSFSR) is proposed to reduce the redundant information of the original feature set, to select sensitive features for fault diagnosis, and to reduce the difference between the marginal distributions of the training and testing feature sets. Furthermore, a modified feature reduction method, the local maximum margin criterion (LMMC), is proposed to acquire low-dimensional mapping for high-dimensional feature spaces. Finally, bearing vibration signals collected from two test rigs are analyzed to demonstrate the adaptability, effectiveness, and practicability of the proposed diagnosis framework. The experimental results show that the proposed method can achieve high diagnosis accuracy and has significant potential benefits in industrial applications.

Online Handwritten Signature Verification and Recognition Based on Dual-Tree Complex Wavelet Packet Transform.

Background:With the increasing advancement of technology, it is necessary to develop more accurate, convenient, and cost-effective security systems. Handwriting signature, as one of the most popular and applicable biometrics, is widely used to register ownership in banking systems, including checks, as well as in administrative and financial applications in everyday life, all over the world. Automatic signature verification and recognition systems, especially in the case of online signatures, are potentially the most powerful and publicly accepted means for personal authentication.Methods:In this article, a novel procedure for online signature verification and recognition has been presented based on Dual-Tree Complex Wavelet Packet Transform (DT-CWPT).Results:In the presented method, three-level decomposition of DT-CWPT has been computed for three time signals of dynamic information including horizontal and vertical positions in addition to the pressure signal. Then, in order to make feature vector corresponding to each signature, log energy entropy measures have been computed for each subband of DT-CWPT decomposition. Finally, to classify the query signature, three classifiers including k-nearest neighbor, support vector machine, and Kolmogorov– Smirnov test have been examined. Experiments have been conducted using three benchmark datasets: SVC2004, MCYT-100, as two Latin online signature datasets, and NDSD as a Persian signature dataset.Conclusion:Obtained favorable experimental results, in comparison with literature, confirm the effectiveness of the presented method in both online signature verification and recognition objects.

Compound fault diagnosis for a rolling bearing using adaptive DTCWPT with higher order spectra

Fault diagnosis plays a vital role in prognostics and health management. Researchers have devoted their efforts in enhancing the accuracy of fault diagnosis. However, diagnosis of compound faults in complex systems is still a challenging task. The problem lies in the coupling of multiple signals, which may conceal the characteristics of compound faults. Taking a rolling bearing as an example, this study aims to boost the accuracy of compound fault diagnosis through a novel feature extraction approach to making the fault characteristics more discriminative. The approach proposes an adaptive dual-tree complex wavelet packet transform (DTCWPT) with higher order spectra analysis. To flexibly and best match the characteristics of the measured vibration signals under analysis, DTCWPT is first adaptively determined by the minimum singular value decomposition entropy. Then, higher order spectra analysis is performed on the decomposed frequency sensitive band for feature extraction and enhancement. The proposed approach is used to analyze experimental signals of a bearing’s compound faults and found effective.

Subband averaging kurtogram with dual-tree complex wavelet packet transform for rotating machinery fault diagnosis

This paper presents a method called subband averaging kurtogram (SAK), incorporating with dual-tree complex wavelet packet transform (DTCWPT), to improve performance of the fast kurtogram (FK) for rotating machinery fault diagnosis. The proposed method first segments a signal into M sub-signals by a sliding window, then computes the kurtosis of subbands obtained by DTCWPT of each sub-signal. Finally, average kurtosis of corresponding subbands are calculated to obtain the SAK, which indicates the optimal frequency band for the envelope analysis. The FK is easily misled by non-Gaussian noise (e.g., sporadic impulse interferences) whereas the SAK can overcome this problem. Moreover, the DTCWPT simultaneously subdivides bands at high and low frequencies, offers the desirable property of approximate shift-invariance and meanwhile remains less computationally expensive. When the original DTCWPT iterates filter banks on the high-pass channel, the obtained subbands of a signal are not arranged in monotone order of the center frequency. This problem can be resolved by exchanging the inverted filter banks based on their band-pass properties. The proposed method provides improved performance compared to FK, in particular, for extracting periodic transients from noisy signals containing a variety of interferences. A simulation case and two applications to fault diagnosis of a planetary gearbox and a rolling bearing validate the effectiveness and improvements of the proposed method.

Acceleration signal with DTCWPT and novel optimize SNR index for diagnosis of misaligned cardan shaft in high-speed train

The cardan shaft transmits rotating motion from motor to gearbox where the axes of rotation of motor and gearbox are not in the same plane. The cardan shaft can be misaligned due to offset at the cross-shaft. The offset causes severe unbalance force on the shaft which eventually leads to catastrophic failure of the whole train. Therefore, it is essential to detect symptom of cardan shaft failure in advance. General diagnostic methods for detecting cardan shaft misalignment are through analysis of vibration signals from sensors located on the rotating system. Unfortunately, the signals are usually corrupted by other mechanical interferences and noise which make vibration analysis unreliable. Hence, it is necessary to eliminate mechanical interferences in the signal and result in improving the detection of shaft misalignment. This study aims to diagnose cardan shaft misalignment by using acceleration signal and dual tree complex wavelet packet transformation (DTCWPT) method to extract the relevant features. The acceleration response of misaligned cardan shaft is analyzed using the multibody dynamics model of Chinese high-speed rail (CRH) built in software. Both DTCWPT and Hilbert transform (HT) methods are applied to decompose the acceleration signal and an envelope of the signal is created. A novel index system is proposed to optimize the signal-to-noise ratio (SNR) index to improve the detection of shaft misalignment. The amplitude of Fourier transform (AFT) coefficient of acceleration signal is then used to assess the degree of shaft misalignment. The results from simulation study and experimental tasks confirmed the capability of the proposed technique in detecting cardan shaft misalignment. This paper presents a new approach to accurately and quantitatively diagnose cardan shaft misalignment.

Voice Pathology Analysis using DT-CWPT and ReliefF Algorithm

Voice pathology analysis has been one of the useful tools in the diagnosis of the pathological voice. This method is non-invasive, inexpensive and reduces time required for analysis. This paper investigates the feature extraction based on the Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) with entropies and energy measures tested with two classifiers, k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM). Feature selection using ReliefF algorithm is applied to reduce redundancy features set and obtain the optimum features for classification. Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database and Saarbruecken Voice Database (SVD) are used. This research was done on multiclass and by specific pathology. The experimental results automates the process of voice analysis hence produce promising results of the presence of diseases in vocal folds.