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The radar jammers operating in an interrupted sampling repeater jamming (ISRJ) mode can intercept transmitted signal, modulate the intercepted signal slices, and retransmit them back to the radar receiver to create coherent interference. This interference can generate false target groups at the receiving end, significantly disrupting the detection and recognition of targets. To address this issue, we propose a method for interference and target recognition based on the difference in the coefficient of variation obtained from wavelet packet transforms of jamming and target echoes, which can effectively eliminate the impact of ISRJ attacks. In the proposed method, after performing target detection on the received signals to obtain distance information of potential targets and interference, the received signals are up-converted to an intermediate frequency, and wavelet packet transforms are applied to the up-converted signals at locations of potential targets and interference to obtain the corresponding wavelet packet transform coefficients’ coefficient of variation (WPTCCV). Since the WPTCCV for targets is smaller than that for interference, the interference can be identified by comparing the obtained WPTCCV with a defined threshold. Simulation results demonstrate that the proposed method exhibits good recognition performance for the false targets generated by interrupted sampling repeater jamming.

Although the classical spectral representation method (SRM) has been widely used in the generation of spatially varying ground motions, there are still challenges in efficient simulation of the non-stationary stochastic vector process in practice. The first problem is the inherent limitation and inflexibility of the deterministic time/frequency modulation function. Another difficulty is the estimation of evolutionary power spectral density (EPSD) with quite a few samples. To tackle these problems, the wavelet packet transform (WPT) algorithm is utilized to build a time-varying spectrum of seed recording which describes the energy distribution in the time-frequency domain. The time-varying spectrum is proven to preserve the time and frequency marginal property as theoretical EPSD will do for the stationary process. For the simulation of spatially varying ground motions, the auto-EPSD for all locations is directly estimated using the time-varying spectrum of seed recording rather than matching predefined EPSD models. Then the constructed spectral matrix is incorporated in SRM to simulate spatially varying non-stationary ground motions using efficient Cholesky decomposition techniques. In addition to a good match with the target coherency model, two numerical examples indicate that the generated time histories retain the physical properties of the prescribed seed recording, including waveform, temporal/spectral non-stationarity, normalized energy buildup, and significant duration. [ABSTRACT FROM AUTHOR]

Recent advancements in the Internet of Things (IoT) wearable devices such as wearable inertial sensors have increased the demand for precise human activity recognition (HAR) with minimal computational resources. The wavelet transform, which offers excellent time-frequency localization characteristics, is well suited for HAR recognition systems. Selecting a mother wavelet function in wavelet analysis is critical, as optimal selection improves the recognition performance. The activity time signals data have different periodic patterns that can discriminate activities from each other. Therefore, selecting a mother wavelet function that closely resembles the shape of the recognized activity's sensor (inertial) signals significantly impacts recognition performance. This study uses an optimal mother wavelet selection method that combines wavelet packet transform with the energy-to-Shannon-entropy ratio and two classification algorithms: decision tree (DT) and support vector machines (SVM). We examined six different mother wavelet families with different numbers of vanishing points. Our experiments were performed on eight publicly available ADL datasets: MHEALTH, WISDM Activity Prediction, HARTH, HARsense, DaLiAc, PAMAP2, REALDISP, and HAR70+. The analysis demonstrated in this paper can be used as a guideline for optimal mother wavelet selection for human activity recognition. [ABSTRACT FROM AUTHOR]

The electrocardiogram (ECG) is a vital tool for detecting heart abnormalities, However, noise frequently disrupts the signals during recording, reducing diagnostic precision. During wireless recording and portable heart monitoring, one major source of noise is called additive white Gaussian noise (AWGN). Therefore, clean ECG signals are really important to diagnose cardic disorders. To address this concern, a novel approach is introduced that employs the Wavelet Packet Transform (WPT) for effective ECG signal denoising. WPT provides a comprehensive signal analysis, using the Symlets 8 mother wavelet function, decomposing ECG data into high and low frequency components over two levels. Subsequent to this, a soft thresholding (ST) technique is implemented to attenuate noise. Moreover, the universal threshold technique is incorporated, dynamically determining threshold values. Proposed method efficiently reduces noise through thresholding, addressing both low and high frequency noise components at each level. The retained coefficients are then utilized in the inverse WPT to reconstruct the denoised ECG signal. Comprehensive analysis highlights the robustness of our approach, demonstrating better performance compared to established denoising techniques on the MIT-BIH database. Performance metrics including Signal-to-Noise Ratio (SNR), SNR Improvement (SNRimp), correlation coefficient (CC), Percentage Root Mean Square Difference (PRD) and Mean Squared Error (MSE) are employed. Proposed WPT approach, tailored through suitable decomposition levels and mother wavelet selection, represents a substantial improvement in ECG signal denoising beyond conventional techniques. The proposed method showcases substantial improvements over EMD-DWT, with 28.32% lower RMSE, 34.99% higher SNR, and 0.25% enhanced CC. [ABSTRACT FROM AUTHOR]

When a high impedance fault (HIF) occurs in a distribution network, the detection efficiency of traditional protection devices is strongly limited by the weak fault information. In this study, a method based on S-transform (ST) and average singular entropy (ASE) is proposed to identify HIFs. First, a wavelet packet transform (WPT) was applied to extract the feature frequency band. Thereafter, the ST was investigated in each half cycle. Afterwards, the obtained time-frequency matrix was denoised by singular value decomposition (SVD), followed by the calculation of the ASE index. Finally, an appropriate threshold was selected to detect the HIFs. The advantages of this method are the ability of fine band division, adaptive time-frequency transformation, and quantitative expression of signal complexity. The performance of the proposed method was verified by simulated and field data, and further analysis revealed that it could still achieve good results under different conditions.

Abstract The Indian Regional Navigation Satellite System provides accurate positioning service to the users within and around India, extending up to 1500 km. However, when a receiver encounters a Continuous Wave Interference, its positioning accuracy degrades, or sometimes it even fails to work. Wavelet Packet Transform (WPT) is the most widely used technique for anti-jamming in Global Navigation Satellite System receivers. But the conventional method suffers from threshold drifting and employs inflexible thresholding functions. So, to address these issues, an efficient approach using Improved Particle Swarm Optimization based Parametric Wavelet Packet Thresholding (IPSO-PWPT) is proposed. Firstly, a new parameter adaptive thresholding function is constructed. Then, a new form of inertia weight is presented to enhance the performance of PSO. Later, IPSO is used to optimize the key parameters of WPT. Finally, the implementation of the IPSO-PWPT anti-jamming algorithm is discussed. The performance of the proposed technique is evaluated for various performance metrics in four jamming environments. The evaluation results manifest the proposed method’s efficacy compared to the conventional WPT in terms of anti-jamming capability. Also, the results show the ability of the new thresholding function to process various signals effectively. Furthermore, the findings reveal that the improved PSO outperforms the variants of PSO.

Abstract This paper explores artificial intelligent training schemes based on multilayer perceptron, considering back propagation and genetic algorithm (GA). The hybrid scheme is compared with the traditional support vector machine approach in the literature to analyze both fault and normal scenarios of a centrifugal pump. A comparative analysis of the performance of the variables was carried out using both schemes. The study used features extracted for three decomposition levels based on wavelet packet transform. In order to investigate the effectiveness of the extracted features, two mother wavelets were investigated. The salient part of this work is the optimization of the hidden layers numbers using GA. Furthermore, this optimization process was extended to the multilayer perceptron neurons. The evaluation of the model system performance used for the study shows better response of the extracted features, and hidden layers variables including the selected neurons. Moreover, the applied training algorithm used in the work was able to enhance the classifications obtained considering the hybrid artificial intelligent scheme been proposed. This work has achieved a number of contributions like GA‐based selection of hidden layers and neuron, applied in neural network of centrifugal pump condition classification. Furthermore, a hybrid training method combining GA and back propagation (BP) algorithms has been applied for condition classification of a centrifugal pump. The obtained results have shown the good ability of the proposed methods and algorithms.

This paper proposes an efficient fuzzy logic-based fault detection scheme for diagnosing the inter-turn short-circuit (ITSC) faults in induction motors (IMs). The proposed approach utilizes the fast Fourier transforms (FFTs) and wavelet packet transform (WPT) for this detection of fault. To improve the efficiency and secure the operation, the proposed approach is detecting the fault in online manner. The WPT is utilized to extract the stator current signal into time-frequency domain characteristics. The variation in the amplitude of the vibration spectrum at different characteristic frequencies by FFT is utilized to identify the stator ITSC. The vibration signal is dignified by a MEMS accelerometer. The performance of the fuzzy logic fault detector (FLFD) for online condition is monitored with stator current, vibration and input speed. The performance of the proposed approach is performed at MATLAB/Simulink working site, and then the performance is compared to other existing works. The accuracy, precision, recall and specificity of the proposed approach are analyzed. Similarly, the statistical measures like root mean square error (RMSE), mean absolute percentage error (MAPE), mean bias error (MBE) and consumption time are analyzed. [ABSTRACT FROM AUTHOR]

Effective fault detection and classification play essential roles in reducing the hazards such as electric shocks and fire in photovoltaic (PV) systems. However, the issues of interest in fault detection and classification for PV systems remain an open-ended challenge due to manual and time-consuming processes that require the relevant domain knowledge and experience of fault diagnoses. This paper proposes a hybrid deep-learning (DL) model-based combined architectures as the novel DL approaches to achieve the real-time automatic fault detection and classification of a PV system. This research employed the wavelet packet transform (WPT) as a data preprocessing technique to handle the PV voltage signal collected and feeding them as the inputs for combined DL architectures that consist of the equilibrium optimizer algorithm (EOA) and long short-term memory (LSTM-SAE) approaches. The combined DL architectures are able to extract the fault features automatically from the preprocessed data without requiring any previous knowledge, therefore can override the traditional shortages of manual feature extraction and manual selection of optimal features from the extracted fault features. These desirable features are anticipated to speed up the fault detection and classification capability of the proposed DL model with higher accuracy. In order to determine the performance of the proposed fault model, we carried out a comprehensive evaluation study on a 250-kW grid-connected PV system. In this paper, symmetrical and asymmetrical faults have been studied involving all the phases and ground faults such as single phase to ground, phases to phase, phase to phase to ground, and three-phase to ground. The simulation results validate the efficacy of the proposed model in terms of computation time, accuracy of fault detection, and noise robustness. Comprehensive comparisons between the simulation results and previous studies demonstrate the multidisciplinary applications of the present study.

The Indian Regional Navigation Satellite System provides accurate positioning service to the users within and around India, extending up to 1500 km. However, when a receiver encounters a Continuous Wave Interference, its positioning accuracy degrades, or sometimes it even fails to work. Wavelet Packet Transform (WPT) is the most widely used technique for anti-jamming in Global Navigation Satellite System receivers. But the conventional method suffers from threshold drifting and employs inflexible thresholding functions. So, to address these issues, an efficient approach using Improved Particle Swarm Optimization based Parametric Wavelet Packet Thresholding (IPSO-PWPT) is proposed. Firstly, a new parameter adaptive thresholding function is constructed. Then, a new form of inertia weight is presented to enhance the performance of PSO. Later, IPSO is used to optimize the key parameters of WPT. Finally, the implementation of the IPSO-PWPT anti-jamming algorithm is discussed. The performance of the proposed technique is evaluated for various performance metrics in four jamming environments. The evaluation results manifest the proposed method's efficacy compared to the conventional WPT in terms of anti-jamming capability. Also, the results show the ability of the new thresholding function to process various signals effectively. Furthermore, the findings reveal that the improved PSO outperforms the variants of PSO. [ABSTRACT FROM AUTHOR]

With the development of the Internet of things, big data, and AI leading the 4th industrial revolution, it has become possible to acquire, manage, and analyze vast and diverse condition signals from various industrial machinery facilities. In addition, it has been revealed that various and large amounts of signals acquired from the facilities can be utilized for fault diagnosis. Currently, while data-driven fault diagnosis techniques applicable to the facilities are being developed, it has been tried to apply the techniques for the development of fully autonomous ships in the shipbuilding and shipping industry. Since the autonomous ships must be able to detect and diagnose the failures on their own in real time, the overall research is required on how to acquire signals from the ship facilities and use them to diagnose their failures. In this study, a fault diagnosis framework was proposed for condition-based maintenance (CBM) of ship oil purifiers, which are an auxiliary facility in the engine system of a ship. First, an oil purifier test-bed for simulating faults was built to obtain data on the state of the equipment. After extracting features using discrete wavelet decomposition from the data, the features were visualized by using t-distributed stochastic neighbor embedding, and were used to train support vector machine-based diagnostic models. Finally, the trained models were evaluated with A c c u r a c y and F 1   s c o r e , and some models scored 0.99 or higher, confirming high diagnostic performance. This study can be used as a reference for establishing CBM system and fault diagnosis system. Furthermore, this study is expected to improve the safety and reliability of oil purifiers in Degree 4 MASS. [ABSTRACT FROM AUTHOR]

This paper explores artificial intelligent training schemes based on multilayer perceptron, considering back propagation and genetic algorithm (GA). The hybrid scheme is compared with the traditional support vector machine approach in the literature to analyze both fault and normal scenarios of a centrifugal pump. A comparative analysis of the performance of the variables was carried out using both schemes. The study used features extracted for three decomposition levels based on wavelet packet transform. In order to investigate the effectiveness of the extracted features, two mother wavelets were investigated. The salient part of this work is the optimization of the hidden layers numbers using GA. Furthermore, this optimization process was extended to the multilayer perceptron neurons. The evaluation of the model system performance used for the study shows better response of the extracted features, and hidden layers variables including the selected neurons. Moreover, the applied training algorithm used in the work was able to enhance the classifications obtained considering the hybrid artificial intelligent scheme been proposed. This work has achieved a number of contributions like GA‐based selection of hidden layers and neuron, applied in neural network of centrifugal pump condition classification. Furthermore, a hybrid training method combining GA and back propagation (BP) algorithms has been applied for condition classification of a centrifugal pump. The obtained results have shown the good ability of the proposed methods and algorithms. [ABSTRACT FROM AUTHOR]

In view of the fact that the wavelet packet transform (WPT) can only weakly detect the occurrence of fault, this paper applies a fault diagnosis algorithm including wavelet packet transform and principal component analysis (PCA) to the inverter-side fault diagnosis of multi-terminal hybrid highvoltage direct current (HVDC) network, which can significantly improve the speed and accuracy of fault diagnosis. Firstly, current amplitude and current slope are used to sample the data, and the WPT is used to extract the energy spectrum of the signal. Secondly, an energy matrix is constructed, and the PCA method is used to calculate whether the squared prediction error (SPE) statistics of various signals that can reflect the degree of deviation of the measured value from the principal component model at a certain time exceed the limit to judge the occurrence of the fault. Further, its maximum value is compared to determine the fault types. Finally, based on a large number of MATLAB/Simulink simulation results, it is shown that the PCA method using the current slope as the sampled data can detect the occurrence of a ground fault with small transition resistance within 2 ms, and identify the fault types within 10 ms, without being affected by the sampling frequency.

This article proposes a new multisensor fusion fault diagnosis method for gearbox, namely residual gated dynamic sparse network, to improve the multisensor feature learning and fusion ability. Considering that the fault sensitivity of the sensor varies with mounted location and complex transfer path modulation causes information from multisensor redundant, the lightweight channel attention unit is designed to strengthen the feature extraction ability of the network. The developed gated dynamic sparse unit is inserted into the deep architecture to eliminate ineffective components caused by high noise interference. Besides, the loss function is improved with multiple activation criteria to enhance convergence ability. The results of experiments and the engineering application show that the proposed method is more effective than other methods under varying degrees of noise interference. [ABSTRACT FROM AUTHOR]

The main challenge in the implementation of long-lasting vibration monitoring systems is to tackle the constantly evolving complexity of modern “mesoscale” structures. Thus, the design of energy-aware solutions is promoted for the joint optimization of data sampling rates, onboard storage requirements, and communication data payloads. In this context, the present work explores the feasibility of the rakeness-based compressed sensing (Rak-CS) approach to tune the sensing mechanism on the second-order statistics of measured data. In particular, a novel model-assisted variant (MRak-CS) is proposed, which is built on a synthetic derivation of the spectral profile of the structure by pivoting on numerical priors. Moreover, a signal-adapted sparsity basis relying on the wavelet packet transform operator is conceived, which aims at maximizing the signal sparsity while allowing for a precise time-frequency localization. The adopted solutions were tested with experiments performed on a sensorized pinned-pinned steel beam. Results prove that the rakeness-based compression strategies are superior to conventional eigenvalue approaches and to standard CS methods. The achieved compression ratio is equal to seven and the quality of the reconstructed structural parameters is preserved even in presence of defective configurations. [ABSTRACT FROM AUTHOR]

The use of high amounts of reclaimed asphalt pavement (RAP) in asphalt pavements has many economic and environmental benefits; however, there are concerns about brittleness and potential cracking of asphalt mixtures. One of the solutions to address this concern is through the inclusion of recycling agents (rejuvenators). The purpose of this study was to explore the effect of different types of recycling agents (biooils, vegetable oils, tall oil, aromatic extract, and paraffinic oil) on the rheological, microstructural, nanomechanical, and chemical properties of asphalt binder blends with high RAP content. Rheological properties were assessed using a dynamic shear rheometer. Atomic force microscopy was used to determine the microstructural characteristics and nanomechanical properties of the asphalt binder blends. A wavelet packet transform approach was proposed to quantify surface roughness characteristics. Fourier transform infrared spectroscopy was used to evaluate the chemical properties based on carbonyl and sulfoxide indices. Results indicated a correlation between the phases observed in the microstructure and rheological performance. Biooil recycling agents were the most effective in improving the microscopic distribution and rheological properties of binder blends, followed by vegetable oils. However, chemical analysis suggested that the addition of recycling agents did not reverse oxidative aging. Finally, the study recommended a rejuvenation index (RI) that quantified the effectiveness of recycling agents in improving blending and reducing stiffness and aging susceptibility. The RI signified that tall oil was the most susceptible to aging, followed by aromatic extract and paraffinic oil, whereas biooils and vegetable oils were the least susceptible to aging. [ABSTRACT FROM AUTHOR]

To overcome the limitations of variational mode decomposition (VMD) algorithm that its frequency scales and spectrum positions cannot be flexibly adjusted to decompose signals as required, a generalized VMD (GVMD) was proposed. This article addresses the fundamental theory of GVMD. In order to highlight the local characteristics of the signal much more while considering its global data fidelity, a set of variational models is formed, where individual constrained optimization problem is constructed for each mode. The formed variational models are solved by the modified alternating direction method of multipliers approach, thus realizing the multiscale and fixed-frequency decomposition. To gain a deep insight into GVMD algorithm, its frequency band division manner is investigated. In essence, GVMD can be viewed as a bank of filters whose bandwidths and center frequencies can be flexibly adjusted by its parameters, i.e., scale parameters and prior center frequencies. The effectiveness of GVMD is verified on simulated and real signals. The preliminary results show that compared with state-of-the-art methods, GVMD can make full use of feature information to decompose original signals as desired into several narrowband modes or into several narrowband modes and a wideband mode, effectively obtaining the interested modes. [ABSTRACT FROM AUTHOR]

The fault diagnosis of the gearbox of wind turbines is a crucial task for wind turbine operation and maintenance. Although a convolutional neural network can extract the related information of adjacent sampling points using kernels, traditional deep learning methods have not leveraged related information from points with a large span of vibration signal data. In this article, a novel fast deep graph convolutional network is proposed to diagnose faults in the gearbox of wind turbines. First, the original vibration signals of the wind turbine gearbox are decomposed by wavelet packet, which presents time–frequency features as graphs. Then, graph convolutional networks are introduced to extract the features of points with a large span of the defined graph samples. Finally, the fast graph convolutional kernel and the particular pooling improvement are used to reduce the number of nodes and achieve fast classification. Experiments on two data sets are performed to verify the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

For the two shortcomings of singular value decomposition (SVD), the determination of the reconstruction order and the poor noise reduction ability, an enhanced SVD is introduced in this article. The core ideas include: first, an efficient method to determine the reconstructed order of SVD and the relative-change rate of the singular envelope kurtosis is presented, composed of improved SVD (ISVD). Then, the method to select the optimal node of wavelet packet transform (WPT) by the criterion of envelope kurtosis maximum is presented, composed of improved WPT (IWPT). The flexible filter design and superior noise reduction abilities of the IWPT and the passband denoise ability of the ISVD are organicly combined to form enhanced singular value decomposition (E-SVD) method. In addition, an indicator is introduced to evaluate the performance of the results. First, the reconstructed signal is obtained by performing ISVD on the original signal. Second, IWPT is executed on the reconstructed signal to achieve the optimal node. Finally, the filtered signal is combined with the envelope power spectrum to extract the bearing fault characteristic frequency. The method's validity and superiority are verified by the analysis of simulated data and actual cases of rolling bearing. [ABSTRACT FROM AUTHOR]

Featured Application: Smart Grid; Power Quality Monitoring Systems; Supervisory Control and Data Acquisition; Advanced Metering Infrastructure. Wavelet transform modulus (WTM) has been used to detect or localize transient signal discontinuities. A numerical analysis indicated that these power quality disturbance (PQD) events are extremely sensitive to the random phase offset due to shift-variant wavelet or wavelet packet characteristics, which have not been comprehensively discussed yet. In this paper, we define wavelet packet transform modulus (WPTM) and present a WPTM-based PQD feature detection that is robust to severe power signal channels including random phase offset and low signal-to-noise ratio (≤25 dB). The presented WPTM-based detection that exhibits an exponentially increased degrees of freedom (DoF) and has better correlation properties than existing WTM-based detection of a limited DoF (two or three). We then use a standard median filter to efficiently remove impulsive noise and add a threshold modification step to reduce the false edge detection rate under random phase offset conditions while maintaining a reasonable detection rate. The proposed scheme uses the majority voting-based indirect correlation or root-mean-square metric between wavelet packet coefficients, rather than the conventional wavelet denoising or correlation metric. For a reliable numerical analysis, the proposed scheme uses both double- and single-edge-based detection measures, and the results verify its superiority over the conventional wavelet-based, wavelet-correlation-based, or non-wavelet-based schemes. [ABSTRACT FROM AUTHOR]

To enhance the reliability of analog circuits in complex electrical systems, a novel incipient fault diagnosis method is presented in this article. The wavelet packet feature quantities, which consist of the energy, fluctuation coefficient, skewness, and margin factor, are obtained via multiscale time-frequency analysis with wavelet packet transform (WPT). Then, generalized discriminant analysis (GDA) is employed to realize the fusion of wavelet packet feature quantities because it can handle the data nonlinearity and eliminate redundant information. Furthermore, the generalized multiple kernel learning support vector machine (GMKL-SVM), which has the advantages of a strong generalization ability and high accuracy, is developed to identify the incipient fault classes of analog circuits. Moreover, a new particle swarm intelligent optimization algorithm, the sine cosine algorithm (SCA), is adopted to optimize key parameters of GMKL-SVM because of its high convergence speed and strong global optimization ability. The method is fully evaluated with the Sallen-Key bandpass filter circuit, the four-op-amp biquad high-pass filter circuit, and the leapfrog filter circuit. The experimental results demonstrate that the proposed incipient fault diagnosis method for analog circuits can produce higher diagnosis accuracy than other typical incipient fault diagnosis methods. [ABSTRACT FROM AUTHOR]

The integrity of the CFRP specimens is tested using acousto-ultrasonic testing method. To validate the acousto-ultrasonic test mode, the specimens are tested before and after a Barely Visible Impact Damage induced by an impactor. A special model is created to use both Wavelet Packet Transform and Empirical Mode Decomposition, for decomposing the recorded waveforms. This mode also enables the reconstruction of the decomposed waveforms, discarding the residual signal in the parent waveform, and calculates the energy associated with each frequency band of the reconstructed signal. By using the percentage of energy recovered by the receiver compared to the signal sent through the specimen, the integrity of the specimens is identified. Moreover, the properties of each specimen and the extent of its damage, albeit qualitatively along the longitudinal and transverse directions can also be assessed by using this technique. [ABSTRACT FROM AUTHOR]

Frequency band entropy (FBE) has been proved usable in the fault diagnosis of rolling bearings, but its performance is poor in the presence of non-Gaussian noise and a low signal-to-noise ratio. In order to extract the transient impulsive signals more effectively, wavelet packet transform (WPT) is considered as an alternative method for signal decomposition. Therefore, by introducing WPT into FBE, this article introduces an enhanced FBE (EFBE) adopting WPT as the filter of FBE to overcome the shortcomings of the original FBE. Then, the depth of EFBE is optimized using adaptive resonance bandwidth and power amplitude spectrum entropy (PASE). Third, a novel method based on the indicator PASE is introduced to select the optimal node of EFBE. Finally, the filtered signal is combined with the envelope power spectrum to extract the fault feature frequency. In addition, an evaluation indicator is proposed to evaluate the performance of the EFBE. The simulation and cases are used to demonstrate the effectiveness and improved performance of the EFBE compared with the original FBE and other typical methods. The results show that the EFBE can detect various rolling bearing failures and implement its fault diagnosis effectively. [ABSTRACT FROM AUTHOR]

The evaluation of pilots’ fatigue status is of substantial significance in aviation safety, which faces two major issues. They are how to get the fatigue status feature representation and how to identify the fatigue behavior status of pilots via electroencephalogram (EEG) signals. To solve the first issue, we propose a novel fatigue evaluation index via different window functions to compute the power spectrum of relative rhythms from EEG signals. Wavelet packet transform is used to decompose EEG signals from pilots to form four major rhythms, i.e., $\boldsymbol {\delta }$ wave, $\boldsymbol {\theta }$ wave, $\boldsymbol {\alpha }$ wave, and $\boldsymbol {\beta }$ wave, and the combined representation of their power spectrum curve area is the features of pilots’ mental status. To solve the second issue, we propose a new deep contractive autoencoder (AE) network with a softmax (SM) classifier to detect the multistatuses of mental fatigue workload. The recognition results of our model are also compared with that of other models such as the deep AE network with a SM classifier model. The experimental results show that our deep learning model has superior classification performance, and the recognition accuracy of fatigue mental status is up to 91.67%, which shows that the proposed method performs excellently compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Fault detection and identification (FDI) of electrical motors is crucial to ensuring smooth operation of several industrial systems. Detection and diagnosis of faults at the incipient stage allows corrective actions to be adopted so as to curb the severity of faults. However, FDI of incipient faults has proved to be elusive to traditional methods of fault diagnosis. With recent developments in statistical machine learning, new methods are proposed that can be used for FDI. In this paper, we adopt three tools (support vector machine, convolutional, and recurrent networks) from machine learning to address the challenge of FDI of incipient faults. We perform FDI of a dc motor with the most commonly and readily measured current data. Results from experimental data reveal that the convolutional network performs the best of the three methods. A comparative study of the performance of the three methods under different types of operating conditions is provided. Sensitivity of the techniques to noise in measurements is also studied. The proposed approach serves as a reliable tool for FDI of dc motor under different types of loading conditions. [ABSTRACT FROM AUTHOR]

A new approach for speaker and accent recognition based on wavelets, namely Discrete Wavelet Packet (DWPT), Dual Tree Complex Wavelet Packet Transform (DT- CWPT) and Wavelet Packet Transform (WPT) based non-linear features are investigated. The results are compared with conventional MFCC and LPC features. k-Nearest Neighbors (k-NN), Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifier are used to quantify the speaker and accent recognition rate. The database for the research was developed using English digits (0~9) and Malay words. The highest accuracy for speaker recognition obtained is 93.54% while for accent recognition; it is 95.86% using Malay words. Combination of features for speaker recognition is obtained from ELM classifier is 98.68 % and for accent recognition is 98.75 % using Malay words. [ABSTRACT FROM AUTHOR]

• The proposed method provides double layer security in the biometric systems. • The image of iris is obtained from the Iris scanning device and it is divided by the use of WPT. • The proposed work has been implemented in MATLAB 2017b. The emerging technologies in this present world is real time biometrics which recognized a specific person in a reliable manner through their distinct biological features. The most reliable biometric identification is an iris identification. The collection of iris images can be stored in the database which is hacked by the intruders. In order to prevent these databases with watermark text, a novel hybrid method is proposed which is a combination of Wavelet Packet Transform (WPT) and cryptography. This paper presents WPT for segmenting the iris image and finding the minimum energy band where the watermark text is embedded. The watermark text is the personal information of the owner of iris. Once the watermarking is done, the cryptographic key is used to encrypt the watermarked image. This way, both the image and the watermark text are prevented in an efficient manner. The quality measures of watermarked image have been analyzed and compared with other existing techniques. The proposed technique has been analyzed with blurring, salt and pepper, JPEG, cropping, Gaussian noise, rotate, speckle noise, filter, gamma, intensity and histogram equalization noises having PSNR value increased by 3.3%, 3.6%, 4.1%, 5.3%, 7.7%, 6.1%, 11.9%, 7.7%, 14.4%, 10.7% and 10.2% respectively which effectively increased the quality of image. [ABSTRACT FROM AUTHOR]

Highlights • A packet-loss robust scalable wideband speech codec is proposed. • Performance is improved using the wavelet transform instead of the MDCT. • The proposed codec outperforms the state-of-the-art codec in objective tests. • High performance of the proposed codec is confirmed in subjective tests. Abstract Most recent speech codecs employ code excited linear prediction (CELP) and transmit side information to improve speech quality under packet loss. Another approach to achieve high robustness to packet loss is to use the frame independent coding scheme based on the internet low bitrate codec (iLBC). The scalable wideband speech codec based on the iLBC was previously presented and outperformed G.729.1 at most bit rates according to the objective quality. This paper presents improvements to the previous work. Specifically, we employ the wavelet packet transform (WPT) instead of the modified discrete cosine transform (MDCT) to enhance the quality, and evaluate the proposed codec based on both the objective and subjective quality measures. The objective quality evaluation results show that clear improvement is achieved and that the proposed codec outperforms G.729.1 at the bit rate of 18 kbps or higher under clean channel conditions and has higher robustness to packet loss than G.729.1. The informal subjective test results also show similar trends. [ABSTRACT FROM AUTHOR]

Highlights • A deceit identification test (DIT) using EEG based BCI is performed. • WPT for feature extraction and LDA as classifier are applied. • A novel experiment is conducted using EEG acquisition device. Abstract Background Brain–computer interface (BCI) is a combination of hardware and software that provides a non-muscular channel to send various messages and commands to the outside world and control external devices such as computers. BCI helps severely disabled patients having neuromuscular injuries, locked-in syndrome (LiS) to lead their life as a normal person to the best extent possible. There are various applications of BCI not only in the field of medicine but also in entertainment, lie detection, gaming, etc. Methodology In this work, using BCI a Deceit Identification Test (DIT) is performed based on P300, which has a positive peak from 300 ms to 1000 ms of stimulus onset. The goal is to recognize and classify P300 signals with excellent results. The pre-processing has been performed using the band-pass filter to eliminate the artifacts. Comparison with existing methods Wavelet packet transform (WPT) is applied for feature extraction whereas linear discriminant analysis (LDA) is used as a classifier. Comparison with the other existing methods namely BCD, BAD, BPNN etc has been performed. Results A novel experiment is conducted using EEG acquisition device for the collection of data set on 20 subjects, where 10 subjects acted as guilty and 10 subjects acted as innocent. Training and testing data are in the ratio of 90:10 and the accuracy obtained is up to 91.67%. The proposed approach that uses WPT and LDA results in high accuracy, sensitivity, and specificity. Conclusion The method provided better results in comparison with the other existing methods. It is an efficient approach for deceit identification for EEG based BCI. [ABSTRACT FROM AUTHOR]

This paper proposes a supervised sparsity-based wavelet feature (SSWF) for the detection of bearing fault, which combines wavelet packet transform (WPT) and sparse coding. SSWF is extracted from vibration signals by four main steps: (1) construct a WPT vector using the fault-related WPT coefficients; (2) design a structured dictionary that combines the signal characteristics and class information; (3) use the dictionary to implement the sparse coding of the WPT vectors, which can be solved by basis pursuit (BP) and (4) calculate the SSWF from the sparse coefficients. During the process, WPT can detect the fault occurrence of the bearing signal. Sparse coding based on a structured dictionary can find a robust representation of the signal and at the same time, integrate the class information. Therefore, SSWF is able to stably and discriminatively reflect different fault types, which indicates its potential in bearing fault diagnosis. Experiments on two bearing cases are conducted to verify the advantages of SSWF in the detection of bearing faults. [ABSTRACT FROM AUTHOR]

In industry loading on bearing and its fault severity in an induction motor is unpredictable. Hence, in the present article wide range of vibration data from induction motor bearing surface has been taken for extraction of fault features and classified for the detection of mechanical faults presents in the bearing so that condition based monitoring possible. The vibration data which is selected in this paper includes four different kinds of loading and three different types of fault with three different fault sizes. Firstly, Wavelet Packet Transform (WPT) is applied to decompose the vibration signal and develop Bearing Damage Index (BDI) from the decomposed signal to select the useful signal from the original recorded signal. This BDI based useful signal is further applied for extraction of statistical features and fed to the classifier. Total eleven time domain features has been calculated and Principal Component Analysis (PCA) is applied for the selection of significant features. The selected features further used as input to the Dendogram Support Vector Machine (DSVM) classifier to identify the faults. This proposed method shows significant improvement in classification rate as compared to conventional method, which is quite promising and encouraging. [ABSTRACT FROM AUTHOR]

The main difficulty encountered in interpretation of cardiac sound is interference of noise. The contaminated noise obscures the relevant information, which are useful for recognition of heart diseases. The unwanted signals are produced mainly by lungs and surrounding environment. In this paper, a novel heart sound denoising technique has been introduced based on a combined framework of wavelet packet transform and singular value decomposition (SVD). The most informative node of the wavelet tree is selected on the criteria of mutual information measurement. Next, the coefficient corresponding to the selected node is processed by the SVD technique to suppress noisy component from heart sound signal. To justify the efficacy of the proposed technique, several experiments have been conducted with heart sound dataset, including normal and pathological cases at different signal to noise ratios. The significance of the method is validated by statistical analysis of the results. The biological information preserved in denoised heart sound signal is evaluated by the k-means clustering algorithm. The overall results show that the proposed method is superior than the baseline methods. [ABSTRACT FROM PUBLISHER]

Shearing machines are the key pieces of equipment for spent–fuel reprocessing in commercial reactors. Once a failure happens and is not detected in time, serious consequences will arise. It is very important to monitor the shearing machine and to diagnose the faults immediately for spent–fuel reprocessing. In this study, an intelligent condition monitoring approach for spent nuclear fuel shearing machines based on noise signals was proposed. The approach consists of a feature extraction based on wavelet packet transform (WPT) and a hybrid fault diagnosis model, the latter combines the advantage on dynamic–modeling of hidden Markov model (HMM) and pattern recognition of artificial neural network (ANN). The verification results showed that the approach is more effective and accurate than that of the isolated HMM or ANN. [ABSTRACT FROM AUTHOR]

Extracting reliable features from vibration signals is a key problem in machinery fault recognition. This study proposes a novel sparse wavelet reconstruction residual (SWRR) feature for rolling element bearing diagnosis based on wavelet packet transform (WPT) and sparse representation theory. WPT has obtained huge success in machine fault diagnosis, which demonstrates its potential for extracting discriminative features. Sparse representation is an increasingly popular algorithm in signal processing and can find concise, high-level representations of signals that well matches the structure of analyzed data by using a learned dictionary. If sparse coding is conducted with a discriminative dictionary for different type signals, the pattern laying in each class will drive the generation of a unique residual. Inspired by this, sparse representation is introduced to help the feature extraction from WPT-based results in a novel manner: (1) learn a dictionary for each fault-related WPT subband; (2) solve the coefficients of each subband for different classes using the learned dictionaries and (3) calculate the reconstruction residual to form the SWRR feature. The effectiveness and advantages of the SWRR feature are confirmed by the practical fault pattern recognition of two bearing cases. [ABSTRACT FROM AUTHOR]

This paper propose a method for security threw hiding the image inside the speech signal by replacing the high frequency components of the speech signal with the data of the image where the high frequency speech components are separated and analyzed using the Wavelet Packet Transform (WPT) where the new signal will be remixed to create a new speech signal with an embedded image. The algorithm is implemented on MATLAB 15 and is designed to achieve best image hiding where the reconstruction rate was more than 94% while trying to maintain the same size of the speech signal to overcome the need for a powerful channel to handle the task. Best results were achieved with higher speech resolution (higher number of bits per sample) and longer periods (higher number of samples in the media file). [ABSTRACT FROM AUTHOR]