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This paper presents a proof-of-concept of a versatile Power Quality (PQ) analyser for tracking the voltage supply in industrial and residential sectors. It implements 2D Higher-Order Statistics (HOS) to assess voltage quality, based more on the sinusoidal waveform than on power fluctuations. Beyond the second-order parameters and permissible deviations regulated by the norm, EN 50160, the two-dimensional traces and probability density functions, along with a previously studied differential index, manage to identify different states of the electrical grid. Waveforms were measured in the wall plugs of a public building. In regard to analysing reliability and voltage waveform, the results corroborate that incorporating skewness and kurtosis indicators improves the characterisation, as well as extracting the customers' supply behaviour under normal and anomalous operations. The instrument showed good behaviour in site characterisation, and the implemented method was considered as a probabilistic approach for the risk assessment of an installation. The prototype was tested in the facilities of a public building of the university, being able to detect deviations in 10 s traces of 3.9% in variance and 0.6% in kurtosis. [ABSTRACT FROM AUTHOR]

Abstract Background Physical exercise is an important method for both the physical and mental health of the senior population. However, excessive exertion can lead to increased risks of falls, severe injuries, and diminished quality of life. Therefore, simple and effective methods for fatigue monitoring during exercise are highly desirable, particularly in community settings. The purpose of this study was to explore the possibility of real-time detection of exercise-induced fatigue using surface Electromyogram (sEMG) features, including the kurtosis and skewness of the Probability Density Function (PDF) in the community settings to solve the issues of low sensitivity and high computational complexity of commonly used sEMG features. Methods sEMG signals from six forearm muscles were recorded during hand grip tasks at 20% maximal voluntary contraction (MVC) task-to-failure contractions from 30 healthy community-dwelling elders at their respective community centers. PDF shape features of the sEMG, namely kurtosis and skewness, were computed from 25 s of non-fatigue stable phase and 25 s of fatigue data for comparison. Statistical tests were conducted to compare and test for the significance of these features. We further proposed a novel fatigue indicator, Temporal-Mean-Kurtosis (TMK) of channel-averaged kurtosis, to detect fatigue with relatively low computational complexity and adequate sensitivity in community settings. ANOVA and post-hoc analyses were performed to examine the performance of TMK. Results Statistically significant differences were found between the non-fatigue period and the fatigue period for both kurtosis and skewness, with increasing values when approaching fatigue. TMK was shown to be sensitive in detecting fatigue with respect to time with lower computational complexity than the Sample Entropy. Conclusion This study investigated PDF shape features of sEMG signals during a handgrip exercise to identify muscle fatigue in older adults in community experiments. Results revealed significant changes in kurtosis upon fatigue, indicating that PDF shape features were suitable convenient detectors of muscle fatigue in community experiments. The proposed indicator, TMK, showed potential sensitivity in tracking muscle fatigue over time in community-based settings with limited computational complexity, highlighting the promise of sEMG’s PDF features in detecting muscle fatigue among the elderly.

Background: Physical exercise is an important method for both the physical and mental health of the senior population. However, excessive exertion can lead to increased risks of falls, severe injuries, and diminished quality of life. Therefore, simple and effective methods for fatigue monitoring during exercise are highly desirable, particularly in community settings. The purpose of this study was to explore the possibility of real-time detection of exercise-induced fatigue using surface Electromyogram (sEMG) features, including the kurtosis and skewness of the Probability Density Function (PDF) in the community settings to solve the issues of low sensitivity and high computational complexity of commonly used sEMG features. Methods: sEMG signals from six forearm muscles were recorded during hand grip tasks at 20% maximal voluntary contraction (MVC) task-to-failure contractions from 30 healthy community-dwelling elders at their respective community centers. PDF shape features of the sEMG, namely kurtosis and skewness, were computed from 25 s of non-fatigue stable phase and 25 s of fatigue data for comparison. Statistical tests were conducted to compare and test for the significance of these features. We further proposed a novel fatigue indicator, Temporal-Mean-Kurtosis (TMK) of channel-averaged kurtosis, to detect fatigue with relatively low computational complexity and adequate sensitivity in community settings. ANOVA and post-hoc analyses were performed to examine the performance of TMK. Results: Statistically significant differences were found between the non-fatigue period and the fatigue period for both kurtosis and skewness, with increasing values when approaching fatigue. TMK was shown to be sensitive in detecting fatigue with respect to time with lower computational complexity than the Sample Entropy. Conclusion: This study investigated PDF shape features of sEMG signals during a handgrip exercise to identify muscle fatigue in older adults in community experiments. Results revealed significant changes in kurtosis upon fatigue, indicating that PDF shape features were suitable convenient detectors of muscle fatigue in community experiments. The proposed indicator, TMK, showed potential sensitivity in tracking muscle fatigue over time in community-based settings with limited computational complexity, highlighting the promise of sEMG's PDF features in detecting muscle fatigue among the elderly. [ABSTRACT FROM AUTHOR]

The global burden of atrial fibrillation (AFIB) is constantly increasing, and its early detection is still a challenge for public health and motivates researchers to improve methods for automatic AFIB prediction and management. This work proposes higher-order spectra analysis, especially the bispectrum of electrocardiogram (ECG) signals combined with the convolution neural network (CNN) for AFIB detection. Like other biomedical signals, ECG is non-stationary, non-linear, and non-Gaussian in nature, so the spectra of higher-order cumulants, in this case, bispectra, preserve valuable features. The two-dimensional (2D) bispectrum images were applied as input for the two CNN architectures with the output AFIB vs. no-AFIB: the pre-trained modified GoogLeNet and the proposed CNN called AFIB-NET. The MIT-BIH Atrial Fibrillation Database (AFDB) was used to evaluate the performance of the proposed methodology. AFIB-NET detected atrial fibrillation with a sensitivity of 95.3%, a specificity of 93.7%, and an area under the receiver operating characteristic (ROC) of 98.3%, while for GoogLeNet results for sensitivity and specificity were equal to 96.7%, 82%, respectively, and the area under ROC was equal to 96.7%. According to preliminary studies, bispectrum images as input to 2D CNN can be successfully used for AFIB rhythm detection. [ABSTRACT FROM AUTHOR]

Since a 1932 work from von Neumann, it has been considered that if two statistical mixtures are represented by the same density operator ρ, they should, in fact, be considered as the same mixture. In a 1970 paper, Zeh introduced a thought experiment with neutron spins, and suggested that, in that experiment, the density operator could not tell the whole story. Since then, no consensus has emerged yet, and controversies on the subject still presently develop. In his 1995 book, speaking of the use of the density operator, Peres spoke of a von Neumann postulate. In this paper, keeping the random variable used by von Neumann in his treatment of statistical mixtures, but also considering higher-order moments of this random variable, it is established that the two mixtures imagined by Zeh, with the same ρ, should however be distinguished. We show that the rejection of that postulate, installed on statistical mixtures for historical reasons, does not affect the general use of ρ, e.g., in quantum statistical mechanics, and the von Neumann entropy keeps its own interest and even helps clarifying that confusing consequence of the postulate identified by Peres.

In recent years, sub-synchronous oscillation accidents caused by wind power integration have received extensive attention. The recorded constant-amplitude waveforms can be induced by either linear or nonlinear oscillation mechanisms. Hence, the nonlinear behavior needs to be distinguished prior to choosing the analysis method. Since the 1960s, the higher-order statistics (HOS) theory has become a powerful tool for the detection of nonlinear behavior (DNB) in production quality control wherein it has mainly been applied to mechanical condition monitoring and fault diagnosis. This study focuses on the hard limiters of the voltage source converter (VSC) control systems in the wind farms and attempts to detect the nonlinear behavior caused by bi- or uni-Iateral saturation hard limiting using the HOS analysis. First, the conventional describing function is extended to obtain the detailed frequency domain information on the bi- and uni-Iateral saturation hard limiting. Furthermore, the bi- and tri-spectra are introduced as the HOS, which are extended into bi- and tri-coherence spectra to eliminate the effects of the linear parts on the harmonic characteristics of hard limiting in the VSC control system, respectively. The effectiveness of the HOS in the DNB and the classification of the hard-limiting types is proven, and its detailed derivation and estimation procedure is presented. Finally, the quadratic and cubic phase coupling in the signals is illustrated, and the performance of the proposed method is evaluated and discussed.

Quantum mechanics and hence quantum information processing methods widely use two types of states, namely (deterministic-coefficient) pure states and statistical mixtures. Density operators can be associated with them. We here address a third type of states, whose ket coefficients are random variables, as opposed to the deterministic coefficients of usual pure states. We therefore call them Random-Coefficient Pure States, or RCPS. We define physical setups that yield RCPS. We analyze the properties of RCPS and show that they contain much richer information than the density operator and mean of observables that we associate with them , because that operator only exploits the second-order statistics of the random state coefficients, whereas their higher-order statistics contain additional information. That information can be accessed in practice with the multiple-preparation procedure that we propose for RCPS, by using second-order and higher-order statistics of associated random probabilities of measurement outcomes. Exploiting these higher-order statistics yields a very general approach to advanced quantum information processing. We illustrate its relevance with a generic quantum parameter estimation problem related to quantum process tomography , especially considering its blind/unsupervised version. We show that this problem cannot be solved by using only the density operator ρ of an RCPS and the associated mean value T r (ρ A ^) of the operator A ^ corresponding to the considered physical quantity. We solve it by exploiting a fourth-order statistical parameter of state coefficients, in addition to second-order statistics. Numerical tests validate this result and show that the proposed method yields accurate parameter estimation for the considered number of state preparations. [ABSTRACT FROM AUTHOR]

This research studies the problem of a joint capacity/capacity reliability analysis of the multiuser multi-input multioutput (MIMO) system functioning in the presence of generalized multipath fading. The study presents the derived results of the closed-form analytical statistical description of the ergodic sum-rate capacity, the capacity reliability and the capacity's higher-order statistics in the case of complex Nakagami-m distributed channel transmission coefficients. A numerical verification of the derived expressions was performed, and it demonstrated excellent correspondence with the simulation. The system performance was evaluated with the help of a numerical analysis of the joint first- and second-order statistics description, depending on the channel and system parameters. The results demonstrated several peculiarities, e.g., the existence of a specific extremum of the capacity reliability for small-sized MIMO systems, its opposite behavior (in terms of the varying number of antenna elements) for heavy and light fading, and the existing asymptotic regions of the system and the channel parameters. [ABSTRACT FROM AUTHOR]

The goal of domain-generalized person reidentification (DG-ReID) is to train a model in the source domain and apply it directly to unknown target domains for specific pedestrian retrieval. Existing methods rely primarily on low-order statistics (such as the mean, standard deviation, or variance), thereby ensuring the stability of the source domain data distribution for model training. However, such methods underperform when the data follow a non-Gaussian distribution, thereby reducing the generalization ability of the model on unseen target domains. To address this issue, this study proposes an instance normalization-based skewness regularity (INSR) framework that uses high-order statistics (skewness and high-order moments) to measure the skewness and regularity of the data distribution. Such measures allow further learning of the morphological features (skewness degree, trait of data near the mean, etc.) of complex data distributions for DG-ReID. Specifically, the proposed framework first extracts the skewness and third-order moments from the source domains, which provide more features (high-order moments, variance, etc.) to characterize the data distribution. Subsequently, a batch normalization-like operation was implemented to project the data into a new feature space with zero mean and unit variance, enhancing model adaption and accuracy. Extensive experiments were conducted on small-scale (VIPeR, PRID, GRID, and i-LIDS) and large-scale (Market-1501, DukeMTMC-reID, CUHK03, MSMT17) public datasets using two different protocols, demonstrating that the proposed INSR framework significantly outperforms other state-of-the-art counterparts for DG-ReID. [ABSTRACT FROM AUTHOR]

This article presents a unique dataset, from a public building, of voltage data, acquired using a hybrid measurement solution that combines PythonTM for acquisition and GrafanaTM for results representation. This study aims to benefit communities, by demonstrating how to achieve more efficient energy management. The study outlines how to obtain a more realistic vision of the quality of the supply, that is oriented to the monitoring of the state of the network; this should allow for better understanding, which should in turn enable the optimization of the operation and maintenance of power systems. Our work focused on frequency and higher order statistical estimators which, combined with exploratory data analysis techniques, improved the characterization of the shape of the stress signal. These techniques and data, together with the acquisition and monitoring system, present a unique combination of low-cost measurement solutions, which have the underlying benefit of contributing to industrial benchmarking. Our study proposes an effective and versatile system, which can do acquisition, statistical analysis, database management and results representation in less than a second. The system offers a wide variety of graphs to present the results of the analysis, so that the user can observe them and identify, with relative ease, any anomalies in the supply which could damage the sensitive equipment of the correspondent installation. It is a system, therefore, that not only provides information about the power quality, but also significantly contributes to the safety and maintenance of the installation. This system can be practically realized, subject to the availability of internet access. [ABSTRACT FROM AUTHOR]

Statistical properties of signals play an essential role in designing algorithms for Direction of Arrival (DoA) estimation. Many times, signals are assumed to be circular, and circular moments are used thereof. Practically, non-circular signals exist and provide extra statistical information in terms of pseudo/non-circular moments/cumulants which can be used to improve the performance of any parameter estimation algorithm. This paper uses the 2qth-order non-circular cumulants along with circular cumulants to solve the underdetermined non-Gaussian non-circular system. A generalized method based on higher-order non-circularity has been proposed to design the physical array such that the corresponding virtual array provides the larger degree of freedoms than existing arrays. The weight function, an important metric, to measure the stability of the corresponding virtual array, has also been evaluated analytically. Numerical simulation demonstrates performance gain due to the proposed array structure. [ABSTRACT FROM AUTHOR]

Visual texture, defined by local image statistics, provides important information to the human visual system for perceptual segmentation. Second-order or spectral statistics (equivalent to the Fourier power spectrum) are a well-studied segmentation cue. However, the role of higher-order statistics (HOS) in segmentation remains unclear, particularly for natural images. Recent experiments indicate that, in peripheral vision, the HOS of the widely adopted Portilla-Simoncelli texture model are a weak segmentation cue compared to spectral statistics, despite the fact that both are necessary to explain other perceptual phenomena and to support high-quality texture synthesis. Here we test whether this discrepancy reflects a property of natural image statistics. First, we observe that differences in spectral statistics across segments of natural images are redundant with differences in HOS. Second, using linear and nonlinear classifiers, we show that each set of statistics individually affords high performance in natural scenes and texture segmentation tasks, but combining spectral statistics and HOS produces relatively small improvements. Third, we find that HOS improve segmentation for a subset of images, although these images are difficult to identify. We also find that different subsets of HOS improve segmentation to a different extent, in agreement with previous physiological and perceptual work. These results show that the HOS add modestly to spectral statistics for natural image segmentation. We speculate that tuning to natural image statistics under resource constraints could explain the weak contribution of HOS to perceptual segmentation in human peripheral vision. [ABSTRACT FROM AUTHOR]

This paper proposes an easy-to-implement method for detecting and assessing two of the most frequent PQ (Power Quality) problems: voltage sags and swells. These can affect sensitive equipment such as computers, programmable logic controllers, contactors, etc. Therefore, it is of great interest to implement it in any laboratory, not only for protection reasons but also as a safeguard for claims against the supply company. Thanks to the actual context, in which it is possible to manage big volumes of data, connect multiple devices with IoT (Internet of Things), etc., it is feasible and of great interest to monitor the voltage at specific points of the network. This makes it possible to detect voltage sags and swells and diagnose which points are more prone to this type of problems. For the detection of sags and swells, a program written in Python is in charge of crawling all the files in the database and target those RMS values that fall outside the established limits. Compared to LabVIEW, which might have been the most logical alternative, being the acquisition hardware from the same company (National Instruments), Python has a higher computational performance and is also free of charge, unlike LabVIEW. Thanks to the libraries available in Python, it allows a hardware control close to what is possible using LabVIEW. Implemented in MATLAB, the ITIC (Information Technology Industry Council) power acceptability curve reflects the impact of these power quality disturbances in electrical power systems. The results showed that the combined action of Python and MATLAB performed well on a conventional desktop computer.

This work is focused on deep learning methods, such as feedforward neural network (FNN) and convolutional neural network (CNN), for pathological voice detection using mel-frequency cepstral coefficients (MFCCs), linear prediction cepstrum coefficients (LPCCs), and higher-order statistics (HOSs) parameters. In total, 518 voice data samples were obtained from the publicly available Saarbruecken voice database (SVD), comprising recordings of 259 healthy and 259 pathological women and men, respectively, and using /a/, /i/, and /u/ vowels at normal pitch. Significant differences were observed between the normal and the pathological voice signals for normalized skewness (p = 0.000) and kurtosis (p = 0.000), except for normalized kurtosis (p = 0.051) that was estimated in the /u/ samples in women. These parameters are useful and meaningful for classifying pathological voice signals. The highest accuracy, 82.69%, was achieved by the CNN classifier with the LPCCs parameter in the /u/ vowel in men. The second-best performance, 80.77%, was obtained with a combination of the FNN classifier, MFCCs, and HOSs for the /i/ vowel samples in women. There was merit in combining the acoustic measures with HOS parameters for better characterization in terms of accuracy. The combination of various parameters and deep learning methods was also useful for distinguishing normal from pathological voices. [ABSTRACT FROM AUTHOR]

The objective of this study was to test higher-order statistical (HOS) parameters for the classification of young and elderly voice signals and identify gender- and age-related differences through HOS analysis. This study was based on data from 116 subjects (58 females and 58 males) extracted from the Saarbruecken voice database. In the gender analysis, the same number of voice samples were analyzed for each sex. Further, we conducted experiments on the voices of elderly people using gender analysis. Finally, we reviewed the standards and reference models to reduce sex and gender bias. The acoustic parameters were extracted from young and elderly voice signals using Praat and a time–frequency analysis program (TF32). Additionally, we investigated the gender- and age-related differences in HOS parameters. Young and elderly voice signals significantly differed in normalized skewness (p = 0.005) in women and normalized kurtosis (p = 0.011) in men. Therefore, normalized skewness is a useful parameter for distinguishing between young and elderly female voices, and normalized kurtosis is essential for distinguishing between young and elderly male voices. We will continue to investigate parameters that represent important information in elderly voice signals. [ABSTRACT FROM AUTHOR]

Underdetermined blind source separation (UBSS) is a hot and challenging problem in signal processing. In the traditional UBSS algorithm, the number of source signals is often assumed to be known, which is very inconvenient in practice. In addition, it is more difficult to obtain the accurate estimation of mixing matrix in the underdetermined case. However, this information has a great influence on the source separation results, which can easily lead to poor separation performance. In this paper, a novel UBSS algorithm is presented to carry out a combined source signal number estimation and source signal separation task. First, in the proposed algorithm, we design a gap-based detection method to detect the number of source signals by eigenvalue decomposition. Then, the estimation of the mixing matrix is processed using a higher-order cumulant-based method so that the uniqueness of the estimated mixing matrix is guaranteed. Furthermore, an improved l1 -norm minimization algorithm is proposed to estimate the source signals. Meanwhile, the pre-conditioned conjugate gradient technology is employed to accelerate the convergence rate such that the computational load is reduced. Finally, a series of simulation experiments with synthetic heart sound data and image reconstruction results demonstrate that the proposed algorithm achieves better separating property than the state-of-the-art algorithms.

This article presents a unique dataset, from a public building, of voltage data, acquired using a hybrid measurement solution that combines PythonTM for acquisition and GrafanaTM for results representation. This study aims to benefit communities, by demonstrating how to achieve more efficient energy management. The study outlines how to obtain a more realistic vision of the quality of the supply, that is oriented to the monitoring of the state of the network; this should allow for better understanding, which should in turn enable the optimization of the operation and maintenance of power systems. Our work focused on frequency and higher order statistical estimators which, combined with exploratory data analysis techniques, improved the characterization of the shape of the stress signal. These techniques and data, together with the acquisition and monitoring system, present a unique combination of low-cost measurement solutions, which have the underlying benefit of contributing to industrial benchmarking. Our study proposes an effective and versatile system, which can do acquisition, statistical analysis, database management and results representation in less than a second. The system offers a wide variety of graphs to present the results of the analysis, so that the user can observe them and identify, with relative ease, any anomalies in the supply which could damage the sensitive equipment of the correspondent installation. It is a system, therefore, that not only provides information about the power quality, but also significantly contributes to the safety and maintenance of the installation. This system can be practically realized, subject to the availability of internet access.

Summary: The target recognition for satellite communication (satcom) is generally regarded as the cutting edge of electronic countermeasure research. This work is dedicated to the investigation on the theory and experiment of satellite communication target recognition on the basis of systematical analysis of satcom signal emission, propagation, and reception. The authors elaborate on the fingerprint analysis, feature extraction, and identification of satcom emitters by utilizing the nonlinearities of high‐power microwave vacuum amplifier (HPA). The mechanism of the external subtle features of satcom signal will be also discussed in detail. To acquire the qualified features that precisely represent the individual emitter, higher‐order statistics technique is introduced to implement the feature extraction, and the supervised probabilistic neural network classifier is established to execute the recognition of testing satcom samples. In testing phase, there are a total of 4000 sampling signals with BPSK modulation and variable carrier to noise ratio (CNR) originated by eight types of satcom transmitters setting for the experiment to verify the authors' viewpoints. Thanks to the fine training data set and subsequent well‐extracted features, the PNN classifier had not fail us and finally achieved satisfactory accuracy of more than 94% at CNR level of 10 dB. Those expected results will help to enhance the ability of battlefield surveillance and situational awareness that is of paramount importance in academic research and military application. [ABSTRACT FROM AUTHOR]

MUSIC algorithm is an effective method in solving the direction-finding problems. Due to the good performance of this algorithm, many variations of it including tesnor-MUSIC for verctor-sensor arrays, have been developed. However, these MUSIC-based methods have some limitations with respect to the number of sources, modeling errors and the noise power. It has been shown that using 2qth-order (q > 1) statistics in MUSIC algorithm is very effective to overcome these drawbacks. However, the existing 2q-order MUSIC-like methods are appropriate for scalar-sensor arrays, which only measure one parameter, and have a matrix of measurements. In vector-sensor arrays, each sensor measures multiple parameters, and to keep this multidimensional structure, we should use a tensor of measurements. The contribution of this paper is to develop a new tensor-based 2q-order MUSIC-like method for vector-sensor arrays. In this regard, we define a tensor of the cumulants which will be used in the proposed algorithm. The new method is called tensor-2q-MUSIC. Computer simulations have been used to compare the performance of the proposed method with a higher-order extension of the conventional MUSIC method for the vector-sensor arrays which is called matrix-2q-MUSIC. Moreover, we compare the performance of tensor-2q-MUSIC method with the existing second-order methods for the vector-sensor arrays. The simulation results show the better performance of the proposed method. [ABSTRACT FROM AUTHOR]

The probability hypothesis density (PHD) filter, which is used for multi-target tracking based on sensor measurements, relies on the propagation of the first-order moment, or intensity function, of a point process. This algorithm assumes that targets behave independently, an hypothesis which may not hold in practice due to potential target interactions. In this paper, we construct a second-order PHD filter based on determinantal point processes which are able to model repulsion between targets. Such processes are characterized by their first- and second-order moments, which allows the algorithm to propagate variance and covariance information in addition to first-order target count estimates. Our approach relies on posterior moment formulas for the estimation of a general hidden point process after a thinning operation and a superposition with a Poisson point process, and on suitable approximation formulas in the determinantal point process setting. The repulsive properties of determinantal point processes apply to the modeling of negative correlation between distinct measurement domains. Monte Carlo simulations with correlation estimates are provided. [ABSTRACT FROM AUTHOR]

The present work proposes a methodology for the detection and classification of some transient power quality disturbances. It uses a genetic algorithm for estimating the amplitude, frequency, and phase of the fundamental component in an optimum way to suppress it from an electric signal. By using this pre-processing strategy, it is possible to remove a single frequency component instead of removing a whole frequency band as other methodologies do. Once the fundamental component is suppressed, it is possible to perform a better identification of an anomalous condition in a signal because its high energy does not hide the presence of a disturbance. After the suppression, the proposed methodology computes three higher-order statistics (variance, kurtosis, and sixth-order cumulant) from the resulting signal and uses them as inputs for a fuzzy-based classifier. Higher-order statistics are selected because they are insensible to the presence of Gaussian noise adding robustness to the proposed methodology. Experimentation is performed using both synthetic and real signals. Real signals come from a photovoltaic generation plant and a hospital facility, both located in Spain. Results prove that the proposed methodology allows enhancing the results delivered by other methodologies up to 30%. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper an unsupervised method to detect mechanical faults using support vector machines and higher-order statistics is proposed. The method extracts compact vector features – based on higher-order statistics – from vibration signals and uses the one-class support vector machine to build a closed region around the data from the health structure. The method was evaluated considering two cases: fault detection in a cantilever beam and in a three-phase induction motor. In both cases, the vibrations were collected by a 3 axis accelerometer sensor. The acquisition system was controlled by an open-source electronic prototyping ARDUINO® platform. After collecting the data, higher-order statistics-based features were extracted. These features were presented to the one-class support vector machine for fault detection. The proposed method was capable of identifying a closed region in a two-dimensional space so that events inside this region are signed as no faults and events outside this region are signed as faults. The method has two important characteristics: (i) it requires only healthy mechanical structures to be designed, and (ii) it operates in a low dimensional space (only two) constructed by the higher-order statistics features, which requires low computational cost in the operational phase. [ABSTRACT FROM AUTHOR]

It is believed that the Acoustic Emissions (AE) signal contains potentially valuable information for monitoring precision cutting processes, as well as to be employed as a control feedback signal. However, AE stress waves produced in the cutting zone are distorted by the transmission path and the measurement systems. In this article, a bicepstrum based blind deconvolution technique is proposed as a valid tool for estimating both, transmission path and sensor impulse response. Assumptions under which application of bicepstrum is valid are discussed and diamond turning experiments are presented, which demonstrate the feasibility of employing bicepstrum for AE blind deconvolution.