Nonlinear Signal Research Articles

Research on rotor fault diagnosis technology of three-phase asynchronous motor based on NA-MEMD mutual information and SVM

Aiming at the problem of mode aliasing in the adaptive decomposition of nonlinear and non-stationary current signals generated by three-phase asynchronous motor faults, and the fault features contained in signals collected by a single sensor can not be accurately and comprehensively extracted and characterized when early rotor bar breakage and air gap eccentricity faults occur, A fault diagnosis method for three-phase asynchronous motor based on noise assisted multivariate empirical mode decomposition (NA-MEMD) and mutual information is proposed. Firstly, the NA-MEMD algorithm is used to decompose the three-phase stator current signal of the asynchronous motor to obtain multi-scale intrinsic mode functions (IMFs). Then, the correlation algorithm is used to screen the IMFs containing useful information. Then, the filtered IMF components are reconstructed into new signals and their features are extracted, Finally, support vector machines (SVM) are used to identify the rotor broken bars and air gap eccentric faults of the three-phase asynchronous motor. The experimental results show that the NA-MEMD method has a higher recognition rate than the traditional empirical mode decomposition (EMD) and the ensemble empirical mode decomposition (EEMD) methods.

Extreme-value statistics in nonlinear optics.

We show that, although nonlinear optics may give rise to a vast multitude of statistics, all these statistics converge, in their extreme-value limit, to one of a few universal extreme-value statistics. Specifically, in the class of polynomial nonlinearities, such as those found in the Kerr effect, weak-field harmonic generation, and multiphoton ionization, the statistics of the nonlinear-optical output converges, in the extreme-value limit, to the exponentially tailed, Gumbel distribution. Exponentially growing nonlinear signals, on the other hand, such as those induced by parametric instabilities and stimulated scattering, are shown to reach their extreme-value limits in the class of the Fréchet statistics, giving rise to extreme-value distributions (EVDs) with heavy, manifestly nonexponential tails, thus favoring extreme-event outcomes and rogue-wave buildup.

Experimental study on opto-acoustic nonlinear frequency-mixing technique with separated basic temperature

Different from the traditional frequency-mixing technique which employs a contacting transducer, the laser-induced acoustic nonlinear frequency-mixing detection technique utilizes a laser source to instigate crack motion and generate acoustic waves. Thus, apart from the temperature oscillation induced by the pump laser, the “basic temperature” originating from the probe laser can also influence the crack. This additional variable complicates the contact state of the crack, yielding a more diverse range of nonlinear acoustic signal attributes. In light of this, our study enhances the conventional opto-acoustic nonlinear frequency mixing experimental setup by integrating an independent heating laser beam. This modification isolates the impact of the “basic temperature” on crack width while also dialing down the probe laser power to mitigate its thermal effects. To amplify the sensitivity of crack detection, we deliberated on the optimal laser source parameters for this setup. Consequently, our revamped system, paired with fine-tuned parameters, captures nonlinear acoustic signals with an enriched feature set. This investigation can provide support for the non-contact opto-acoustic nonlinear frequency mixing technique in the detection and evaluation of micro-cracks.

Intelligent fault diagnosis methods for hydraulic components based on information fusion: review and prospects

Abstract Hydraulic component faults have the characteristics of nonlinear time-varying signal, strong concealment, and difficult feature extraction, etc. Timely and accurately fault diagnosis of hydraulic components is helpful to curb economic losses and accidents, so researches have carried out a lot of research on hydraulic components. Information fusion technology can combine multi-source data from multiple dimensions to mine fault data characteristics, which effectively improves the accuracy and reliability of fault diagnosis results. However, there is currently a lack of a comprehensive and systematic review in this domain. Therefore, in this paper, the hydraulic components information fusion fault diagnosis technologies are summarized and analyzed, including the main process information fusion fault diagnosis and the research status of information fusion fault diagnosis of hydraulic system. The methods and techniques involved in the fusion process, data source and fusion method of fault diagnosis of hydraulic components information fusion are described and summarized. The problems of information fusion in fault diagnosis of hydraulic components are analyzed, the solutions are discussed, and the research ideas of improving information fusion fault diagnosis are put forward. Finally, digital twin technology is introduced, and the advantages and research status of intelligent fault diagnosis based on digital twin are summarized. On this basis, the intelligent fault diagnosis of hydraulic components based on information fusion is summarized, and the challenges and future research ideas of applying information fusion and digital twin to intelligent fault diagnosis of hydraulic components are put forward and analyzed comprehensively.

Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review.

The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures and functions that lead to age-related pathological disorders. Electroencephalographic (EEG) signals recorded during resting-state conditions have been widely used because of the significant advantage of non-invasive signal acquisition with higher temporal resolution. These advantages include the capability of a variety of linear and nonlinear signal analyses and state-of-the-art machine-learning and deep-learning techniques. Advances in artificial intelligence (AI) can not only reveal the neural mechanisms underlying aging but also enable the assessment of brain age reliably by means of the age-related characteristics of EEG signals. This paper reviews the literature on the age-related features, available analytic methods, large-scale resting-state EEG databases, interpretations of the resulting findings, and recent advances in age-related AI models.

Measuring non-linear Faraday rotation in cold atoms in presence of persistent transverse fields using tunable differential imaging

Non-linear Faraday rotation in cold atoms promises precision magnetometry due to narrower magnetic resonance linewidth compared to the linear Faraday effect. Imaging techniques based on linear Faraday effect have emerged as a tool to characterize the dynamics of ultracold atomic clouds. Using a camera instead of balanced detectors, we can obtain the spatial distribution of polarization rotation in a uniformly intense optical beam. However, the finite dynamic range of the imaging device limits the sensitivity to measure non-linear Faraday rotation at a given incident power. Here, we experimentally demonstrate a differential imaging technique in which we can tune parameters to improve contrast and the sensitivity to the non-linear Faraday rotation signal by a factor of ≈7 over existing imaging methods. The atomic cloud experiences a uniform optical field even when shifted by persistent magnetic fields making the method robust. This allows us to study the effect of transverse fields on non-linear Faraday rotation in ultra-cold atoms, paving the way toward spatially resolved vector magnetometry.

Estimation of realized volatility of cryptocurrencies using CEEMDAN-RF-LSTM

Predicting cryptocurrency volatility is crucial for investors, traders, and decision-makers but is complicated by the market’s high non-linearity, volatility, and noise. This paper presents a novel approach, the CEEMDAN-RF-LSTM hybrid model, which is the first to combine the strengths of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Random Forest (RF), and Long Short-Term Memory Network (LSTM) to predict the Realized Volatility (RV) of mainstream cryptocurrencies. The model exploits CEEMDAN’s proficiency in processing non-linear and non-stationary signals, RF’s exceptional feature selection capabilities, and LSTM’s distinctive advantages in dealing with time-series problems. Applied to actual transaction data for Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB), empirical results show the superior performance of our model in predicting actual cryptocurrency volatility. These findings contribute to the academic understanding of cryptocurrency volatility and provide practical guidance for quantitative trading strategy development, offering fresh insights and methodologies for related research fields.

Unveiling second harmonic generation from femtosecond-laser microstructured Nd:YAG crystal

In this work we apply second harmonic microscopy to the analysis of damage tracks inscribed by femtosecond laser irradiation in a Nd:YAG crystal. While second harmonic generation is not expected in the bulk of this centrosymmetric material, the 2D and 3D images obtained via second harmonic microscopy show that the induced micro-modification of the crystal structure leads to a localized generation of the nonlinear signal. The nature of this modification and its dependence on irradiation and detection parameters is discussed. These findings demonstrate the capability of second harmonic microscopy for the morphological analysis of written structures in Nd:YAG and open the door for the design and fabrication of new nonlinear structures to be integrated in novel photonic devices.

Micro-crack localization for steel strands using nonlinear vibro-acoustic modulation based on modified signal bispectrum analysis

Under continuous environmental erosion and long-time high tension, fatigue micro-cracks of bridge cables appear frequently to challenge the reliability of cables. The vibro-acoustic modulation analysis is adopted because of its sensitivity to contact-type defects, especially fatigue cracks. In this paper, using multiple sets of low-frequency vibration and high-frequency ultrasonic excitation, the propagation of the ultrasonic wave in the steel strands is studied. Then, the modulated signal bispectrum is proposed to improve the detection reliability of nonlinear modulation components in the nonlinear vibro-acoustic modulation signal of the contact-type defect. The numerical simulations and laboratory tests of straight wire, helical wire, and multi-wire strands are carried out for micro-crack detection to verify the robustness of the proposed method. Compared with the traditional method, the modulated signal bispectrum can still produce clearer peak detection under high noise conditions. The results show that it provides a promising, dynamic and rapid cable defect detection technique.

Frequency space derivation of linear and nonlinear memory gravitational wave signals from eccentric binary orbits

Frequency space derivation of linear and nonlinear memory gravitational wave signals from eccentric binary orbits

Convolutional neural network-based classification of glaucoma using optic radiation tissue properties

BackgroundSensory changes due to aging or disease can impact brain tissue. This study aims to investigate the link between glaucoma, a leading cause of blindness, and alterations in brain connections.MethodsWe analyzed diffusion MRI measurements of white matter tissue in a large group, consisting of 905 glaucoma patients (aged 49-80) and 5292 healthy individuals (aged 45-80) from the UK Biobank. Confounds due to group differences were mitigated by matching a sub-sample of controls to glaucoma subjects. We compared classification of glaucoma using convolutional neural networks (CNNs) focusing on the optic radiations, which are the primary visual connection to the cortex, against those analyzing non-visual brain connections. As a control, we evaluated the performance of regularized linear regression models.ResultsWe showed that CNNs using information from the optic radiations exhibited higher accuracy in classifying subjects with glaucoma when contrasted with CNNs relying on information from non-visual brain connections. Regularized linear regression models were also tested, and showed significantly weaker classification performance. Additionally, the CNN was unable to generalize to the classification of age-group or of age-related macular degeneration.ConclusionsOur findings indicate a distinct and potentially non-linear signature of glaucoma in the tissue properties of optic radiations. This study enhances our understanding of how glaucoma affects brain tissue and opens avenues for further research into how diseases that affect sensory input may also affect brain aging.

On the optical wave structures to the fractional nonlinear integrable coupled Kuralay equation

This paper is mainly concerning the study of truncated M-fractional Kuralay equations that have applications in numerous fields, including nonlinear optics, ferromagnetic materials, signal processing, engineering fields and optical fibers. Due to its ability to clarify a wide range of sophisticated physical phenomena and reveal more dynamic structures of localized wave solutions, the Kuralay equation has captured a lot of attention in the research field. The newly designed integration methods, known as the modified Sardar subequation method and enhanced modified extended tanh expansion method are used as solving tools to validate the solutions. The goal of this study is to extract several kinds of optical solitons, such as mixed, dark, singular, bright-dark, bright, complex and combined solitons. Due to the many potential applications for superfast signal routing techniques and shorter light pulses in communications, the optical propagation of soliton in optical fibers is now a topic of significant interest. In nonlinear dispersive media, optical solitons are stretched electromagnetic waves that maintain their intensity due to a balance between the effects of dispersion and nonlinearity. In addition, exponential, periodic, hyperbolic solutions are generated. The applied approaches are efficient in explaining fractional nonlinear partial differential equations by providing pre-existing solutions and also producing new solutions by combining results from multiple processes. Additionally, we plot the contour, 2D, and 3D graphs with the associated parameter values to visualize the solutions. The results of this study show the effectiveness of the approaches adopted and help enhance comprehension of the nonlinear dynamical behavior of specific systems. We expect that a substantial amount of engineering model specialists will greatly benefit from our work. The findings demonstrate the efficacy, efficiency, and applicability of the computational method employed, particularly in dealing with intricate systems.

Land Use Sustainability: Assessment of the Dynamic Response of Typical Bedrock-Buried-Hill Earth Fissure Sites in the Su-Xi-Chang Area

Disaster prevention and the mitigation of earth fissures is a key issue in the sustainable development of urban land. Structures directly avoiding earth fissures are not conducive to the rational planning and efficient utilization of urban construction. The Su-Xi-Chang area, which consists of the cities of Suzhou, Wuxi, and Changzhou, surrounded by Taihu Lake, has developed bedrock buried-hill earth fissures that are rare in the rest of the country. Existing research results have identified the genesis mechanisms, distribution patterns, and developmental characteristics of this type of fissure. Not only does the slow-variable activity of earth fissures cause direct damage to surface and underground structures, but in addition, when an earthquake occurs, the presence of earth fissures may cause the seismic response of the site to be altered or even strengthened, leading to unknown damage or the possible destruction of structures near the fissures. However, no studies have been conducted to assess the dynamic effects of bedrock-buried-hill earth fissure sites. Therefore, in this research, based on six typical bedrock-buried-hill-type earth fissures in the Su-Xi-Chang area, and in order to accurately reveal the dynamic amplification effect law of the earth fissure sites, systematic spectral analyses and comparisons of the microtremor signals were carried out by using the linear analysis method (Direct Fourier Transform Analysis) and the nonlinear analysis method (Hilbert–Huang Transform). The results show that bedrock-buried-hill-type earth fissures have a significant amplification effect on the dynamic response of the site; the amplification effect of bedrock-buried-hill fissure sites follows the same attenuation pattern, and the furthest range of the dynamic response on the site is about 25 m, beyond which the original seismic fortification level can be maintained; the extreme value of the amplification factor of the two sides of this type of site, as derived from the Fourier and HHT methods, is about double, and the nearest earth fissure region should be considered to have a raised seismic fortification intensity of more than double the original. The Hilbert–Huang transform method has good applicability for processing microtremor data, and nonlinear signal analysis methods can be considered comprehensive for future microtremor signal processing.

Parity-time symmetry enabled ultra-efficient nonlinear optical signal processing

Nonlinear optical signal processing (NOSP) has the potential to significantly improve the throughput, flexibility, and cost-efficiency of optical communication networks by exploiting the intrinsically ultrafast optical nonlinear wave mixing. It can support digital signal processing speeds of up to terabits per second, far exceeding the line rate of the electronic counterpart. In NOSP, high-intensity light fields are used to generate nonlinear optical responses, which can be used to process optical signals. Great efforts have been devoted to developing new materials and structures for NOSP. However, one of the challenges in implementing NOSP is the requirement of high-intensity light fields, which is difficult to generate and maintain. This has been a major roadblock to realize practical NOSP systems for high-speed, high-capacity optical communications. Here, we propose using a parity-time (PT) symmetric microresonator system to significantly enhance the light intensity and support high-speed operation by relieving the bandwidth-efficiency limit imposed on conventional single resonator systems. The design concept is the co-existence of a PT symmetry broken regime for a narrow-linewidth pump wave and near-exceptional point operation for broadband signal and idler waves. This enables us to achieve a new NOSP system with two orders of magnitude improvement in efficiency compared to a single resonator. With a highly nonlinear AlGaAs-on-Insulator platform, we demonstrate an NOSP at a data rate approaching 40 gigabits per second with a record low pump power of one milliwatt. These findings pave the way for the development of fully chip-scale NOSP devices with pump light sources integrated together, potentially leading to a wide range of applications in optical communication networks and classical or quantum computation. The combination of PT symmetry and NOSP may also open up opportunities for amplification, detection, and sensing, where response speed and efficiency are equally important.

Time-resolved biphase signatures of quadratic nonlinearity observed in coupled Alfvén eigenmodes on the DIII-D tokamak

Time-resolved biphase signatures of quadratic nonlinearity observed in coupled Alfvén eigenmodes on the DIII-D tokamak

DEMNUni: cross-correlating the nonlinear ISWRS effect with CMB-lensing and galaxies in the presence of massive neutrinos

We present an analytical modelling of the angular cross-correlations between the Integrated Sachs Wolfe-Rees Sciama (ISWRS) effect and large-scale structure tracers in the presence of massive neutrinos. Our method has been validated against large N-body simulations with a massive neutrino particle component, namely the DEMNUni suite. We investigate the impact of different neutrino masses on the cross-correlations between the ISWRS effect and both the galaxy clustering and the lensing of the Cosmic Microwave Background (CMB). We also test the ability of current nonlinear matter power spectrum modellings to reproduce neutrino effects in such cross-correlations. We show that the multipole position of a characteristic sign inversion in the cross-spectra, due to nonlinear effects, is strongly related to the total neutrino mass Mν and depends almost linearly on it. While these nonlinear cross-correlation signals may not be able alone to constrain the neutrino mass, our approach paves the way to the detection of such cross-spectra on small scales for their exploitation in combination with main probes from future galaxy surveys and CMB experiments.

Nonlinear Mixture Signal Separation With the Extended Slow Feature Analysis (xSFA) in Fiber-Optic Distributed Acoustic Sensor (DAS)

Nonlinear Mixture Signal Separation With the Extended Slow Feature Analysis (xSFA) in Fiber-Optic Distributed Acoustic Sensor (DAS)

An alternative 2D ICEEMDAN-based denoising method and its application in processing magnetic anomaly data

Due to environmental noise and human factors, magnetic data collected in the field often contain various noises and interferences that significantly affect the subsequent data processing and interpretation. Empirical Mode Decomposition (EMD), an adaptive multiscale analysis method for nonlinear and non-stationary signals, is widely used in geophysical and geodetic data processing. Compared with traditional EMD, Improved Complete Ensemble EMD with Adaptive Noise (ICEEMDAN) is more effective in addressing the problem of mode mixing. Based on the principles of 1D ICEEMDAN, this paper presents an alternative algorithm for 2D ICEEMDAN, extending its application to two-dimensional scenarios. The effectiveness of the proposed approach is demonstrated through synthetic signal experiments, which show that the 2D ICEEMDAN exhibits a weaker mode mixing effect compared to the traditional bidimensional EMD (BEMD) method. Furthermore, to improve the performance of the denoising method based on 2D ICEEMDAN and preserve useful signals in high-frequency components, an improved soft thresholding technique is introduced. Synthetic magnetic anomaly data testing indicates that our denoising method effectively preserves signal continuity and outperforms traditional soft thresholding methods. To validate the practical application of this improved threshold denoising method based on 2D ICEEMDAN, it is applied to ground magnetic survey data in the Yandun area of Xinjiang. The results demonstrate the effectiveness of the method in removing noise while retaining essential information from practical magnetic anomaly data. In particular, practical applications suggest that 2D ICEEMDAN can extract trend signals more accurately than the BEMD. In conclusion, as a potential tool for multi-scale decomposition, the 2D ICEEMDAN is versatile in processing and analyzing 2D geophysical and geodetic data.

An application of machine learning for material crack diagnosis using nonlinear ultrasonics

Crack diagnosis in non-destructive testing often requires reference data from the structure before damage or a considerable amount of response data. Also, detecting compression cracks is challenging. In this study, a machine learning-based method is proposed for diagnosing cracks in structures under compression. The method consists of convolutional neural networks (CNN) and fully connected networks (FCN). The CNN extracts features from nonlinear ultrasonic signal data, and the features determine the occurrence of fatigue cracks in a target specimen. Four types of input data are defined in accordance with the number of input frequency combinations. The performance of the proposed method is investigated using each data type to secure efficiency and accuracy in diagnosing aluminum specimens under various compression conditions. As a result, the F1 score, a measure of accuracy, of the proposed method depends on the number of input frequency combinations. The method detects high-compression cracks with high accuracy compared to the present technology specialized for compression cracks in a certain data type. A high accuracy of more than 96% is achieved with less computation time. The proposed method will provide an accurate crack diagnosis for compression cracks with reduced time and effort.

Impact of mental arithmetic task on the electrical activity of the human brain

Cognitive neuroscience investigates the intricate connections between brain function and mental processing to understand the cognitive architecture. Exploring the human brain, the epicenter of cognitive activity, offers valuable insights into underlying cognitive processes. To monitor brain states corresponding to various mental activities, appropriate measurement tools are essential. Electroencephalogram (EEG) signals serve as a valuable tool for recording patterns and changes in electrical brain activities. Leveraging non-linear signal processing techniques holds promise for advancing our understanding of brain activities during cognitive tasks. In this study, we analyze the electrical activity of the brain using EEG data collected from subjects engaged in a cognitive workload task. Employing wavelet-based analysis, we capture changes in the structure of EEG signals before and during a mental arithmetic task. Additionally, spectral analysis is conducted to discern alterations in the distribution of spectral contents of EEG signals. Our findings underscore the efficacy of wavelet-based analysis and spectral entropy in quantifying the time-varying and non-stationary nature of EEG recordings, offering effective frameworks for distinguishing between different cognitive activities. Consequently, these methods afford deeper insights into the cognitive architecture by tracking changes in the distribution of the time-varying spectrum.