Complex Modulation Research Articles

CTRNet: An Automatic Modulation Recognition Based on Transformer-CNN Neural Network

Deep learning (DL) has brought new perspectives and methods to automatic modulation recognition (AMR), enabling AMR systems to operate more efficiently and reliably in modern wireless communication environments through its powerful feature learning and complex pattern recognition capabilities. However, convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which are used for sequence recognition tasks, face two main challenges, respectively: the ineffective utilization of global information and slow processing speeds due to sequential operations. To address these issues, this paper introduces CTRNet, a novel automatic modulation recognition network that combines a CNN with Transformer. This combination leverages Transformer’s ability to adequately capture the long-distance dependencies between global sequences and its advantages in sequence modeling, along with the CNN’s capability to extract features from local feature regions of signals. During the data preprocessing stage, the original IQ-modulated signals undergo sliding-window processing. By selecting the appropriate window sizes and strides, multiple subsequences are formed, enabling the network to effectively handle complex modulation patterns. In the embedding module, token vectors are designed to integrate information from multiple samples within each window, enhancing the model’s understanding and modeling ability of global information. In the feedforward neural network, a more effective Bilinear layer is employed for processing to capture the higher-order relationship between input features, thereby enhancing the ability of the model to capture complex patterns. Experiments conducted on the RML2016.10A public dataset demonstrate that compared with the existing algorithms, the proposed algorithm not only exhibits significant advantages in terms of parameter efficiency but also achieves higher recognition accuracy under various signal-to-noise ratio (SNR) conditions. In particular, it performs relatively well in terms of accuracy, precision, recall, and F1-score, with clearer classification of higher-order modulations and notable overall accuracy improvement.

Nicotinamide riboside modulates the reactive species interactome, bioenergetic status and proteomic landscape in a brain-region-specific manner

Nicotinamide riboside (NR), a precursor of nicotinamide adenine dinucleotide (NAD+), has robust cognitive benefits and alleviates neuroinflammation in Alzheimer's Disease (AD) mouse models without decreasing beta-amyloid plaque pathology. Such effects may be mediated by the reactive species interactome (RSI), at the metabolome level. In this study, we employed in vitro and in vivo models of oxidative stress, aging and AD to profile the effects of NR on neuronal survival, RSI, and the whole proteome characterization of cortex and hippocampus. RSI analysis yielded a complex modulation upon NR treatment. We constructed protein co-expression networks and correlated them to NR treatment and all measured reactive species. We observed brain-area specific effects of NR on co-expressed protein modules of oxidative phosphorylation, fatty acid oxidation, and neurotransmitter regulation pathways, which correlated with RSI components. The current study contributes to the understanding of modulation of the metabolome, specifically after NR treatment in AD and how it may play disease-modifying roles.

Selection for Late Reproduction Leads to Loss of Complex I Mitochondrial Capacity and Associated Increased Longevity in Seed Beetles.

Mitochondria play a key role in aging. Here, we measured integrated mitochondrial functions in experimentally evolved lines of the seed beetle Acanthoscelides obtectus that were selected for early (E) or late (L) reproduction for nearly 4 decades. The 2 lines have markedly different lifespans (8 days and 13 days in the E and L lines, respectively). The contribution of the NADH pathway to maximal flux was lower in the L compared to the E beetles at young stages, associated with increased control by complex I. In contrast, the contribution of the Succinate pathway was higher in the L than in the E line, whereas the Proline pathway showed no differences between the lines. Our data suggest that selection of age at reproduction leads to a modulation of complex I activity in mitochondria and that mitochondria are a functional link between evolutionary and mechanistic theories of aging.

Biopsychosocial factors of depression among community-dwelling geriatric population with low perceived social support; a population-based study

BackgroundAlthough significant and disabling consequences are presented due to geriatric population-related depression, an insufficient comprehension of various biological, psychological, and social factors affecting this issue has been observed. Notably, these factors can contribute to geriatric population-related depression with low social support. This study aimed to identify factors associated with depression among the community-dwelling geriatric population with low social support in Malaysia.MethodsThis study used secondary data from a population-based health survey in Malaysia, namely the National Health Morbidity Survey (NHMS) 2018: Elderly Health. The analysis included 926 community-dwelling geriatric population aged 60 and above with low social support. The primary data collection was from August to October 2018, using face-to-face interviews. This paper reported the analysis of depression as the dependent variable, while various biological, psychological and social factors, guided by established biopsychosocial models, were the independent variables. Multiple logistic regression was applied to identify the factors. Analysis was performed using the complex sampling module in the IBM SPSS version 29.ResultsThe weighted prevalence of depression among the community-dwelling geriatric population aged 60 and above with low social support was 22.5% (95% CI: 17.3–28.7). This was significantly higher than depression among the general geriatric Malaysian population. The factors associated with depression were being single, as compared to those married (aOR 2.010, 95% CI: 1.063–3.803, p: 0.031), having dementia, as opposed to the absence of the disease (aOR 3.717, 95% CI: 1.544–8.888, p: 0.003), and having a visual disability, as compared to regular visions (aOR 3.462, 95% CI: 1.504–7.972, p: 0.004). The analysis also revealed that a one-unit increase in control in life and self-realisation scores were associated with a 32.6% (aOR: 0.674, 95% CI: 0.599–0.759, p < 0.001) and 24.7% (aOR: 0.753, 95% CI: 0.671–0.846, p < 0.001) decrease in the likelihood of developing depression, respectively.ConclusionThis study suggested that conducting depression screenings for the geriatric population with low social support could potentially prevent or improve the management of depression. The outcome could be achieved by considering the identified risk factors while implementing social activities, which enhanced control and self-fulfilment.

A nonparametric point-by-point method to measure time-dependent frequency in wavelength modulation spectroscopy

A nonparametric point-by-point (NPP) method is presented for high-accuracy measurement of the time-dependent frequency (laser frequency) in tunable laser absorption spectroscopy, crucial for ensuring ultimate measurement accuracy. In wavelength modulation spectroscopy in particular, the parametric methods in current use for time-dependent frequency measurement are insufficiently accurate and are difficult to apply to complex modulation scenarios. Based on a multi-scale viewpoint, point-by-point measurement of the frequency is realized by linear superposition of the frequency information mapped from the interferometric signal on a unit scale and on a local scale. Validation experiments indicate that the measurement accuracy of the proposed NPP method is three times that of the existing parametric methods, while effectively immunizing against non-ideal tuning effects. Additionally, the NPP method is suitable for use with arbitrarily complex modulations such as square wave modulation, for which parametric methods are inapplicable.

Fast video anomaly detection via context-aware shortcut exploration and abnormal feature distance learning

Surveillance systems are essential in computer vision, with video anomaly detection (VAD) critical for real-world security. Conventional approaches anticipate abnormal frames using normal patterns learned with normal training data and determine anomalies by comparing the output and input. However, they often incur high computational costs due to pre-trained networks and complex modules. Also, they suffer from the tendency to overlook anomalies due to strong generalizing ability. To address these, we propose two innovations: Anomaly Distance Learning (ADL) and Context-Aware Skip Connection (CONASkip), maximizing the distinction between normal and abnormal samples. ADL increases the feature gap by learning to separate normal and abnormal features, while CONASkip selectively connects layers to preserve normal information and differentiate output quality. These methods operate on self-supervised signals, replacing complex and costly modules. Additionally, ADL is excluded during testing, enabling fast inference. Our model achieves 130 frames per second and superior performance on three benchmarks, addressing real-world efficiency challenges.

De novo variants in ATXN7L3 lead to developmental delay, hypotonia and distinctive facial features.

Deubiquitination is crucial for the proper functioning of numerous biological pathways, such as DNA repair, cell cycle progression, transcription, signal transduction and autophagy. Accordingly, pathogenic variants in deubiquitinating enzymes (DUBs) have been implicated in neurodevelopmental disorders and congenital abnormalities. ATXN7L3 is a component of the DUB module of the Spt-Ada-Gcn5 acetyltransferase (SAGA) complex and two other related DUB modules, and it serves as an obligate adaptor protein of three ubiquitin-specific proteases (USP22, USP27X or USP51). Through exome sequencing and by using GeneMatcher, we identified nine individuals with heterozygous variants in ATXN7L3. The core phenotype included global motor and language developmental delay, hypotonia and distinctive facial characteristics, including hypertelorism, epicanthal folds, blepharoptosis, a small nose and mouth, and low-set, posteriorly rotated ears. To assess pathogenicity, we investigated the effects of a recurrent nonsense variant [c.340C>T; p.(Arg114Ter)] in fibroblasts of an affected individual. ATXN7L3 protein levels were reduced, and deubiquitylation was impaired, as indicated by an increase in histone H2Bub1 levels. This is consistent with the previous observation of increased H2Bub1 levels in Atxn7l3-null mouse embryos, which have developmental delay and embryonic lethality. In conclusion, we present clinical information and biochemical characterization supporting ATXN7L3 variants in the pathogenesis of a rare syndromic neurodevelopmental disorder.

On diagnostics of the properties of functional-gradient composites considering rheology

Abstract The solution of inverse problems on determining the variable rheological properties of functional-gradient beams using additional information about the angle of rotation of the end section is presented within the framework of the most used models: Euler–Bernoulli and Timoshenko. Within the framework of the concept of complex modules, nonlinear operator equations are obtained that connect the given and sought functions (instantaneous and long-term module, relaxation time). The solution is based on the linearization method and the formulation of an iterative process, at each step of which it is necessary to solve complex-valued boundary value problems and invert completely continuous operators with complex-valued kernels. Functions reflecting the laws of change in long-term and instantaneous modules have been restored. The results of computational experiments in some special cases are presented.

DecouplingNet: A Stable Knowledge Distillation Decoupling Net for Fault Detection of Rotating Machines Under Varying Speeds.

Fault detection, also known as anomaly detection (AD), is at the heart of prediction and health management (PHM), which plays a vital role in ensuring the safe operation of mechanical equipment. Nonetheless, the lack of anomaly data creates a significant obstacle to the AD of the mechanical system. In particular, the complex modulation effects induced by time-varying speeds make AD much more challenging. For rapid and accurate AD, a stable knowledge distillation decoupling net (DecouplingNet) is provided to overcome these difficulties. First, an adversarial network consisting of an encoder, a decoder, and an encoder-discriminator is developed to model normal samples well by imposing constraints on the latent space. Then, a causal decoupling framework is suggested to disentangle equipment state-related information from operating conditions-related features, enabling stable condition monitoring at varying speeds. Finally, feature-based knowledge distillation is employed to boost the efficiency of AD while maintaining the detection accuracy. The proposed method is tested on two experimental scenarios and compared with some typical AD methods. The finding demonstrates that the net outperforms others in terms of accuracy and efficiency when it comes to detecting anomalies in the mechanical equipment that runs under varying speeds.

Об идентификации характеристик неоднородных вязкоупругих тел в рамках модели дробного порядка

Nowadays, the development of models of viscoelastic materials with complex inhomogeneous structure is one of the hottest problems of continuum mechanics. Along with classical models, fractional-differential models of viscoelasticity have become increasingly popular. In this paper, we present a model for describing steady-state oscillations of inhomogeneous viscoelastic bodies in terms of fractional-order differential operators. Taking into account the fractional order of the model operators, the corresponding form of the complex module describing the material properties is constructed. The module includes four characteristics: instantaneous and long-term elastic moduli (in the case of material inhomogeneity, they are the functions of coordinates), relaxation time and fractionality parameter. The properties of the complex module are studied, and the ranges of the model parameters, at which the rheological properties are most pronounced, are identified. A general formulation of the inverse problem (IP) on the identification of function-parameters of the model based on the acoustic sounding data is proposed. In the framework of this formulation, the inverse problems for specific objects , namely, an inhomogeneous rod and a round plate, are considered. In both model problems, the influence of the fractionalization parameter on the amplitude-frequency characteristics is analyzed. It has been found that in the vicinity of viscoelastic resonances the fractionality parameter affects the parameters of the oscillatory process most significantly, which is typical for such problems. A solution to the nonlinear IPs under consideration is constructed based on the linearization method, which also serve as basis for the iterative processes supplemented with the elements of the projection approach. This allows one to determine corrections to the required functions in the specified classes of functions using regularization. For both IPs, a series of computational experiments were carried out. The analysis of the experimental results made it possible to formulate recommendations for the selection of optimal sensing modes.

Oxylipin Dynamics Following A Single Bout of Yoga Exercise: A Pilot Randomized Controlled Trial Secondary Analysis.

Background: Yoga may promote health via a complex modulation of inflammation. Little is known about oxylipins, a class of circulating mediators involved in inflammation resolution. Objective: To explore the acute effects of yoga exercise on systemic levels of oxylipins. Methods: This is a secondary analysis of a three-arm (high-intensity-yoga: HY, n = 10); moderate-intensity-yoga: MY, n = 10; and no-intervention-control: CON, n = 10) pilot randomized controlled trial employing a single bout of yoga exercise. Blood samples (baseline and 4-timepoint post-intervention) were used for an unbiased metabolipidomic profiling analysis. Net Areas Under the Curve per oxylipin were evaluated for each group. Results: Lipoxin(LX)B4, prostaglandin(PG)D2, and resolvin(Rv)D3 exhibited a greater magnitude of change in HY compared with MY and CON. Conclusion: Findings inform the design of future trials exploring the acute effects of yoga exercise on oxylipins' systemic levels.

Ambient Moisture-Driven Self-Powered Iontophoresis Patch for Enhanced Transdermal Drug Delivery.

Iontophoretic transdermal drug delivery (TDD) devices are known to enhance the transdermal transport of drugs. However, conventional transdermal iontophoretic devices require external power sources, wired connections, or mechanical parts, which reduce the comfort level for patients during extended use. In this work, a self-powered, wearable transdermal iontophoretic patch (TIP) is proposed by harvesting ambient humidity for energy generation, enabling controlled TDD. This patch primarily uses moist-electric generators (MEGs) as its power source, thus obviating the need for complex power management modules and mechanical components. A single MEG unit can produce an open-circuit voltage of 0.80V and a short-circuit current of 11.65µA under the condition of 80% relative humidity. Amplification of the electrical output is feasible by connecting multiple generator units in series and parallel, facilitating the powering of certain commercial electronic devices. Subsequently, the MEG array is integrated with the TDD circuit to create the wearable TIP. After 20 min of application, the depth of drug penetration through the skin is observed to increase threefold. The effective promotion effect of TIP on the transdermal delivery of ionized drugs is corroborated by simulations and experiments. This wearable TIP offers a simple, noninvasive solution for TDD.

Triadic percolation induces dynamical topological patterns in higher-order networks.

Triadic interactions are higher-order interactions which occur when a set of nodes affects the interaction between two other nodes. Examples of triadic interactions are present in the brain when glia modulate the synaptic signals among neuron pairs or when interneuron axo-axonic synapses enable presynaptic inhibition and facilitation, and in ecosystems when one or more species can affect the interaction among two other species. On random graphs, triadic percolation has been recently shown to turn percolation into a fully fledged dynamical process in which the size of the giant component undergoes a route to chaos. However, in many real cases, triadic interactions are local and occur on spatially embedded networks. Here, we show that triadic interactions in spatial networks induce a very complex spatio-temporal modulation of the giant component which gives rise to triadic percolation patterns with significantly different topology. We classify the observed patterns (stripes, octopus, and small clusters) with topological data analysis and we assess their information content (entropy and complexity). Moreover, we illustrate the multistability of the dynamics of the triadic percolation patterns, and we provide a comprehensive phase diagram of the model. These results open new perspectives in percolation as they demonstrate that in presence of spatial triadic interactions, the giant component can acquire a time-varying topology. Hence, this work provides a theoretical framework that can be applied to model realistic scenarios in which the giant component is time dependent as in neuroscience.

Intake of Dark and Milk Chocolate Differently Affects Autonomic Nervous Control of the Heart During Rest and Mental Stress

Objective Chocolate is a popular food that may affect the activity of the autonomic nervous system (ANS). The aim of this study was to determine the effect of a single dose of dark or milk chocolate on ANS cardiac control during rest and mental stress induced by the Stroop test (ST). Methods Healthy participants, divided into DARK or MILK chocolate groups, ingested corresponding type of chocolate (1 g/kg body weight). They underwent measurement of ANS during relaxation and ST before and 2 h after chocolate consumption. ANS control was assessed by determination of heart rate (HR) and heart rate variability using parameters related to complex autonomic modulation (TP, SDNN) or primary vagal modulation (HFnu, RMSSD). Results HR was always increased during ST in both groups. Relaxation HR values after chocolate ingestion were higher only in the DARK chocolate group. During ST, values of TP, SDNN and HFnu decreased before and after chocolate ingestion in the DARK group, but only before chocolate ingestion in the MILK group. RMSSD values decreased during ST before and after chocolate ingestion in both groups. Relaxation TP, RMSSD and HFnu values after chocolate ingestion were lower in the DARK but not in the MILK group. Conclusion The results suggest that even a single dose of milk chocolate attenuates changes in ANS cardiac control induced by mental stress, whereas a single dose of dark chocolate has an activating effect on the heart via modification of ANS cardiac control at rest. Different levels of sugars and cocoa biologically active compounds in the two types of chocolate could explain the observed effects.

BRD7 as key factor in PBAF complex assembly and CD8+ T cell differentiation.

Upon infection, naive CD8+ T cells differentiate into cytotoxic effector cells to eliminate the pathogen-infected cells. Although many mechanisms underlying this process have been demonstrated, the regulatory role of chromatin remodeling system in this process remains largely unknown. Here we show that BRD7, a component of the polybromo-associated BAF complex (PBAF), was required for naive CD8+ T cells to differentiate into functional short-lived effector cells (SLECs) in response to acute infections caused by influenza virus or lymphocytic choriomeningitis virus (LCMV). BRD7 deficiency in CD8+ T cells resulted in profound defects in effector population and functions, thereby impairing viral clearance and host recovery. Further mechanical studies indicate that the expression of BRD7 significantly turned to high from naive CD8+ T cells to effector cells, which bridged BRG1 and PBRM1 to the core module of PBAF complex, consequently facilitating the assembly of PBAF complex rather than BAF complex in the effector cells. The PBAF complex changed the chromatin accessibility at the loci of Tbx21 gene and upregulated its expression, leading to the maturation of effector T cells. Our research demonstrates that BRD7 and the PBAF complex are key in CD8+ T cell development and present a significant target for advancing immune therapies.

Programmable meta-holography dynamics enabled by grating-modulation

Towards next-generation intelligent display devices, it is urgent to find a cheap and convenient dynamic optical control method. Conventional gratings are widely used as cheap diffractive elements due to their effective optical control capabilities. However, they are limited within multi-function or complex optical modulation due to the lack of accurate control of the amplitude/phase at pixel-level. Here, a metasurface-assisted grating-modulation system is originally proposed to achieve switchable multi-fold meta-holographic dynamics. By incorporating metasurfaces with traditional gratings and tuning their relative coupling positions, the modulation system gains the optical modulation capability to realize complex optical functionalities. Specifically, by varying the grating periods/positions relative to the metasurface, 2–8 holographic image channels are programmed to be dynamically exhibited and switched. The proposed metasurface-assisted grating-modulation approach enjoys cost-effective convenience, strong encoding freedom, and facile operation, which promises programmable optical displays, optical sensors, optical information encryption/storage, etc.

Radiological image analysis using effective channel extension and fusion network based on COVID CT images

Attention mechanisms demonstrate significant capabilities in improving the performance of COVID CT image classification networks. Existing strategies often employ global pooling to acquire feature vectors, overlooking local feature information. Complex attention modules are then applied to enhance network performance, inevitably leading to increased model complexity. To address these issues, this paper introduces an Effective Channel Expansion and Fusion (ECEF) module specifically designed to enhance the performance of medical COVID CT image classification. The module represents a simple yet effective convolutional neural network module. In the channel direction of the given feature map, the module performs segmentation, conducts global pooling operations separately on the segmented and overall feature maps to obtain corresponding attention vectors, and merges them to produce high-dimensional attention vectors. Channel attention feature vectors are derived along the two pooling operation directions and dynamically fused. The resulting attention vectors are multiplied by the input feature map for adaptive feature refinement. Through these operations, the ECEF module adeptly captures local features in COVID CT images, enhancing the classification performance of medical COVID CT images. As a lightweight and versatile module, ECEF seamlessly integrates into any CNN architecture with negligible overhead. It can undergo end-to-end training alongside basic CNNs. ResNet is selected as the backbone network for empirical studies conducted on COVID CT images, including the COVID-CT dataset. Experimental results indicate that, compared to current advanced channel attention mechanisms, the ECEF module exhibits superior performance in COVID CT image classification tasks. The successful application of the ECEF module establishes a foundation for its widespread use in areas such as COVID CT medical image segmentation and object detection.

TRLS-10. CLINICAL TRIAL IN PROGRESS - CONNECT2007: PHASE I/II STUDY OF LUTATHERA IN PATIENTS WITH RECURRENT AND/OR PROGRESSIVE HIGH-GRADE CENTRAL NERVOUS SYSTEM (CNS) TUMORS AND MENINGIOMAS THAT DEMONSTRATE UPTAKE ON DOTATATE PET

Abstract BACKGROUND Somatostatin type-2A receptors (SST2A) regulate cell growth through complex downstream modulation of proliferation and apoptosis signaling, thereby representing a potential therapeutic target. Lutetium (177Lu-DOTATATE), a radionuclide therapy which binds SST2A and delivers local radiation via beta particle emission, gained FDA approval for gastroenteropancreatic neuroendocrine tumors, a disease characterized by SST2A expression. Potential expansion of Lutathera into pediatric neuro-oncology is supported by evidence that medulloblastoma, other embryonal tumors, and meningiomas consistently express membranous SST2A, exhibit corresponding uptake on SST2A-radiolabeled nuclear imaging (DOTATATE PET), and have demonstrated radiographic response to SST2A-targeted therapy, suggesting sufficient CNS penetration to achieve therapeutic benefit. METHODS CONNECT2007 (NCT05278208) is an international, multicenter phase I/II study investigating safety and efficacy of Lutathera in children and adults with SST2A-expressing CNS tumors. Patients with recurrent/progressive high-grade CNS tumors and meningiomas can undergo screening, consisting of DOTATATE PET imaging for functional confirmation of SST2A expression, requiring adequate uptake (Krenning score ≥2) by central review. The phase I cohort will enroll patients aged 4-&amp;lt;12 years to determine the pediatric recommended phase II dose (RP2D) following a Rolling Six design, with three potential dose levels, starting at the adult RP2D (200mCi), scaled by body surface area. The phase II cohort will enroll patients aged ≥12 years to assess anti-tumor activity through evaluation of 6-month progression-free survival in medulloblastoma/embryonal tumors (descriptive in other histologies). Lutathera is administered intravenously, with concurrent amino acids for nephroprotection, dosed every 8 weeks for up to 4 cycles. Longitudinal correlative studies include 1) evaluating prevalence, heterogeneity, and clinical, histologic molecular, and radiographic predictors of SST2A expression, 2) characterizing radiation dosimetry of Lutathera in organs at risk of toxicity and within CNS to assess tumor penetration, and 3) identifying imaging and molecular biomarkers of response. Enrollment began in January 2023; both cohorts remain open to accrual.

Regulatory role of Mycobacterium tuberculosis MtrA on dormancy/resuscitation revealed by a novel target gene-mining strategy.

The unique dormancy of Mycobacterium tuberculosis plays a significant role in the major clinical treatment challenge of tuberculosis, such as its long treatment cycle, antibiotic resistance, immune escape, and high latent infection rate. To determine the function of MtrA, the only essential response regulator, one strategy was developed to establish its regulatory network according to high-quality genome-wide binding sites. The complex modulation mechanisms were implied by the strong bias distribution of MtrA binding sites in the noncoding regions, and 32.7% of the binding sites were located inside the target genes. The functions of 288 potential MtrA target genes predicted according to 294 confirmed binding sites were highly diverse, and DNA replication and damage repair, lipid metabolism, cell wall component biosynthesis, cell wall assembly, and cell division were the predominant pathways. Among the 53 pathways shared between dormancy/resuscitation and persistence, which accounted for 81.5% and 93.0% of the total number of pathways, respectively, MtrA regulatory genes were identified not only in 73.6% of their mutual pathways, but also in 75.4% of the pathways related to dormancy/resuscitation and persistence respectively. These results suggested the pivotal roles of MtrA in regulating dormancy/resuscitation and the apparent relationship between dormancy/resuscitation and persistence. Furthermore, the finding that 32.6% of the MtrA regulons were essential in vivo and/or in vitro for M. tuberculosis provided new insight into its indispensability. The findings mentioned above indicated that MtrA is a novel promising therapeutic target for tuberculosis treatment since the crucial function of MtrA may be a point of weakness for M. tuberculosis.

A novel generalized Vold-Kalman filtering for wind turbine fault diagnosis

Fault diagnosis of offshore wind turbines (WTs) is vital to the reliability and efficiency of wind power generation. Generalized demodulation has been demonstrated to be effective in revealing the fault-related frequencies, but there are intricate interfering components in the demodulation spectra due to the iterative demodulation operation and the complex modulation nature of vibration signals of WT. This paper develops a novel generalized Vold-Kalman filtering approach. To separate the signal components, Vold-Kalman filter is adopted to the amplitude envelope of vibration signal by exploiting its merit in non-component decomposition, in which a frequency set is constructed by the modulation nature of machinery vibration. The phase functions of the frequencies in the frequency set are constructed, by which the variable components are transformed into constant ones. The proposed method is tested via the data of rolling bearings and a WT planetary gearbox.