bluuebunny/crossref_metadata_2025 huggingface dataset:
Inducing variable pitch gratings in nematic liquid crystals using chirped surface acoustic wave transducers
Authors: Morris Rowan , Jones J Cliff , Nagaraj Mamatha
Published: May 2022

Inducing hydrodynamic flow in nematic liquid crystals (NLCs) reorients the symmetry axis of the material—the director, causing changes in the optical properties. The resulting optical gratings have potential use as switchable diffractive optical elements (DOEs). In this study, surface acoustic waves (SAWs) are applied to an NLC device to induce periodic vortices through hydrodynamic instabilities. We show a linear relationship between the wavelength of the applied SAW and induced grating period, where the coefficient is determined by the ratio of the speed of sound in the substrate to that in the NLC. This relationship is further used to design a novel device, where the period of the hydrodynamic grating can be continuously varied from 90 μm to 260 μm by utilizing a broadband (chirped) SAW transducer. These devices present great potential for creating a new class of continuously variable DOEs, overcoming current technological limitations.
Non-clustering of sp
3 fluorine adatoms on pristine graphene surface
Authors: Li Shuilin , Xu Yongjie , Liu Jiawei , Li Ziying , Feng Qian , Deng Yu , Tang Nujiang
Published: May 2022

Fluorination can change graphene’s properties, and which is theoretically relative to fluorination pattern of sp
3 fluorine adatoms on graphene surface. The common view for the pattern is that it can easily form as a large cluster for the low migration barrier of fluorine adatoms on pristine graphene surface. Here, we report that sp
3 fluorine adatoms are well-dispersed rather than clustered due to that the intensity ratio of 1.8 for C–CF/CF peaks (R) of fluorinated graphene is much higher than R ≈ 0 for clustered pattern. The low magnetic inducing efficiency of 1 µ
B/1000F adatoms indicates that the ‘nonmagnetic’ fluorine pairs rather than ‘magnetic’ fluorine ‘points’ dominate the well-dispersed sp
3 pattern. Our findings introduce a new insight into the fluorination structure properties of fluorinated and other sp
3 functionalized such as hydrogenated, chlorinated, or hydroxylated graphene and other carbon materials.
Analyzing the TiO2 surface reactivity based on oxygen vacancies computed by DFT and DFTB methods
Authors: Çetin Yarkın Aybars , Martorell Benjamí , Serratosa Francesc , Aguilera-Porta Neus , Calatayud Monica
Published: May 2022

Titanium dioxide is a key material in many fields, including technological, industrial and biomedical applications. Many of these applications are related to the surface reactivity of TiO2 and involve its reducibility properties. Recently titania has been banned as a food additive due to its (nano)toxicity, and the release of reactive oxygen species plays a crucial role in many toxicological mechanisms. Determining chemical descriptors that account for the extension of reduction is necessary to understand such processes and necessary for predicting the reactivity of an unknown system. In the present work, we compute a set of chemical descriptors for selected surfaces of anatase and rutile TiO2. The aim is twofold: we want to provide chemically meaningful information on the surface reactivity, and benchmark the descriptors for two ab initio schemes. To do so, we compute the oxygen vacancy formation energy, and the corresponding electronic structure, in four slab models with two different computational schemes (DFT+U and DFTB). In this way, we characterize the robustness of the dataset, with the purpose of scaling up to more realistic model systems such as nanoparticles or explicit solvent, which are too computationally demanding for state-of-the-art density functional theory approaches.
Robust and accurate normal estimation in 3D point clouds via entropy-based local plane voting
Authors: Bai Chun , Liu Guangshuai , Li Xurui , Yang Yuzhou , Li Zuoxin
Published: May 2022

This paper introduces a robust and accurate normal estimation method for 3D point clouds. Our proposed technique is also robust towards noise and is capable of preserving sharp features in the input model. Our method presupposes that the normal of a point can be constituted from at least one in the normal set from the planes in its neighborhood, and its core idea is based on a local plane voting strategy, where each vote takes the entropy value and plane credibility into consideration. In addition, the average fitting residuals and the plane density are designed to further effectively deal with noise and non-uniformly sampled point clouds. The validity and reliability of our approach are confirmed by contrast to the most relevant state-of-the-art methods and by comprehensive experiments on synthetic and real-world data.
A novel wind turbine fault diagnosis based on deep transfer learning of improved residual network and multi-target data
Authors: Zhang Yan , Liu Wenyi , Gu Heng , Alexisa Arinayo , Jiang Xiangyu
Published: May 2022

In industrial production, problems such as lack of data, complex fault types, and low generalizability of deep learning models seriously affect the fault diagnosis of wind turbines. Therefore, we have developed a fault diagnosis model for wind turbines under harsh conditions to address the above problems. First, the collected one-dimensional vibration data is reshaped into two-dimensional form by using the Gramian Angular Field. The two-dimensional form not only extends the spatial structure of the data, but also effectively improves the information expression of the data. In addition, the data is classified into large-scale data, medium-scale data, small-scale data, class-imbalanced data, and heterogeneous data based on the data type. Then, the deep residual network structure is redesigned to improve the diagnostic performance of the model based on the sensitivity of the reshaped data to the size of the convolutional kernel, and the new structure of the network is employed to implement transfer learning. Finally, we adopt the developed fault diagnosis model to achieve the fault diagnosis of bearings and gears in the wind turbine gearbox. Meanwhile, an automatic hyperparameter search mechanism was added to improve the partial hyperparameter optimization in this study. It is demonstrated that the model proposed in this study has excellent diagnostic performance with multi-target data for wind turbines, and has excellent generalizability and reliability.
Membrane-cloaked polydopamine modified mesoporous silica nanoparticles for cancer therapy
Authors: Wang Zeyu , Shi Jinyan , Pan Hao , Liu Mingxia , Sang Yuli , Ai Jiao , Liu Yang , Chen Lijiang
Published: May 2022

To improve the shortcomings of narrow therapeutic range and low bioavailability of traditional preparations, a composite drug carrier that combines the advantages of biological carriers and synthetic carriers was prepared in this project. The biomimetic nano-delivery system outer membrane vesicles-polydopamine-mesoporous silica nanoparticle (OMVs-PDA-MSN-DOX) for oral administration is composed of OMVs of Escherichia coli as shell and doxorubicin-loaded MSN modified by PDA as core. Several characterization techniques thoroughly examined the nano-drug delivery system to confirm its surface morphology and chemical property. OMVs-PDA-MSN-DOX with a particle size of 150 nm showed significant cell selectivity and safety. We demonstrated that OMVs are capable of protecting pH-sensitive nanostructure from the oral route of administration in the short term. Importantly, OMVs-PDA-MSN-DOX could facilitate intestinal adhesion and improve DOX bioavailability. Overall, the OMVs-cloaked nanocarrier provides an efficient delivery platform for the oral targeting treatment of cancer with pH-sensitive nano-formulations.
Biomedical engineering aspects of nanocellulose: a review
Authors: Rai Rohit , Dhar Prodyut
Published: May 2022

Cellulose is one of the most abundant renewable biopolymer in nature and is present as major constituent in both plant cell walls as well as synthesized by some microorganisms as extracellular products. In both the systems, cellulose self-assembles into a hierarchical ordered architecture to form micro to nano-fibrillated structures, on basis of which it is classified into various forms. Nanocellulose (NCs) exist as rod-shaped highly crystalline cellulose nanocrystals to high aspect ratio cellulose nanofibers, micro-fibrillated cellulose and bacterial cellulose (BC), depending upon the origin, structural and morphological properties. Moreover, NCs have been processed into diversified products ranging from composite films, coatings, hydrogels, aerogels, xerogels, organogels, rheological modifiers, optically active birefringent colored films using traditional-to-advanced manufacturing techniques. With such versatility in structure-property, NCs have profound application in areas of healthcare, packaging, cosmetics, energy, food, electronics, bioremediation, and biomedicine with promising commercial potential. Herein this review, we highlight the recent advancements in synthesis, fabrication, processing of NCs, with strategic chemical modification routes to tailor its properties for targeted biomedical applications. We also study the basic mechanism and models for biosynthesis of cellulose in both plant and microbial systems and understand the structural insights of NC polymorphism. The kinetics study for both enzymatic/chemical modifications of NCs and microbial growth behavior of BC under various reactor configurations are studied. The challenges associated with the commercial aspects as well as industrial scale production of pristine and functionalized NCs to meet the growing demands of market are discussed and prospective strategies to mitigate them are described. Finally, post chemical modification evaluation of biological and inherent properties of NC are important to determine their efficacy for development of various products and technologies directed for biomedical applications.
NiCoSe4 nanoparticles derived from nickel–cobalt Prussian blue analogues on N-doped reduced graphene oxide for high-performance asymmetric supercapacitors
Authors: Yang Quanlu , Feng Qiaoliang , Xu Xin , Liu Yang , Yang Xuexue , Yang Fawang , Li Jiankun , Zhan Huiying , Wang Qianshan , Wu Shang
Published: May 2022

Synthesis of NiHCCo precursors via simple co-precipitation and nickel–cobalt tetraselenide composites grown on nitrogen-doped reduced graphene oxide (NiCoSe4/N-rGO) were fabricated using solvothermal method. The introduction of N-rGO used as a template effectively prevented agglomeration of NiCoSe4 nanoparticles and provided more active sites, which greatly increased the electrochemical and electrical conductivity for NiCoSe4/N-rGO. NiCoSe4/N-rGO-20 presents a remarkably elevated specific capacity of 120 mA h g−1 under current density of 1 A g−1. NiCoSe4/N-rGO-20 demonstrates an excellent cycle life and achieves a remarkable 83% retention rate over 3000 cycles with 10 A g−1. NiCoSe4/N-rGO-20//N-rGO asymmetric supercapacitor was constructed based on the NiCoSe4/N-rGO-20 as an anode, N-rGO as cathode by using 2 mol l−1 KOH as an electrolyte. NiCoSe4/N-rGO-20//N-rGO ASC demonstrates an ultra-big energy density of 14 Wh kg−1 and good circulation stability in the power density of 902 W kg−1. It is doubled in comparison to the NiCoSe4/N-rGO-20//rGO asymmetric supercapacitor (7 Wh kg−1). The NiCoSe4/N-rGO-20//N-rGO ASC capacity retention is still up to 93% over 5000 cycles (5 A g−1). The results reveal that this device would be a prospective cathode material of supercapacitors in actual applications.
A multifunctional composite membrane with photocatalytic, self-cleaning, oil/water separation and antibacterial properties
Authors: Zhang Yubo , Chen Yi , Wang Chen , Fan Zhao , Wang Yongqian
Published: May 2022

Multifunctional composite materials have attracted wide attention because they can be applied in many fields, and have great potential for application. The superhydrophobic surface has been favored by scientists in recent years because of its excellent properties. In this paper, carbon fabric (CF) with high porosity, good electrical conductivity and excellent flexibility was selected as substrate. The CF/Zr–TiO2/PDMS composites were prepared by solvo-thermal method on CF and modified by PDMS, which can be used in the fields of self-cleaning, self-healing, oil/water separation, antibacterial, anti-icing and so on. Furthermore, the PDMS modification has no or negligible adverse effect on the important textile physical properties of the CF. The effects of different process parameters on the structure and properties of CF/Zr–TiO2 were studied, and the samples with the best conditions were obtained. The results show that the sample achieves the transformation from superhydrophilic to superhydrophobic after modification. The CF/Zr–TiO2/PDMS composite structure not only shows excellent photocatalytic activity, but also has a certain self-cleaning ability. The samples before and after modification can separate light oil/water and heavy oil/water respectively. Compared with CF/Zr–TiO2, CF/Zr–TiO2/PDMS has better anti-Escherichia coli ability.
First application of the BIANCA biophysical model to carbon-ion patient cases
Authors: Kozłowska Wioletta S , Carante Mario P , Aricò Giulia , Embriaco Alessia , Ferrari Alfredo , Magro Giuseppe , Mairani Andrea , Ramos Ricardo , Sala Paola , Georg Dietmar , Ballarini Francesca
Published: May 2022

Objective. The main objective of this work consists of applying, for the first time, the BIANCA (BIophysical ANalysis of Cell death and chromosome Aberrations) biophysical model to the RBE calculation for C-ion cancer patients, and comparing the outcomes with those obtained by the LEM I model, which is applied in clinics. Indeed, the continuous development of heavy-ion cancer therapy requires modelling of biological effects of ion beams on tumours and normal tissues. The relative biological effectiveness (RBE) of heavy ions is higher than that of protons, with a significant variation along the beam path. Therefore, it requires a precise modelling, especially for the pencil-beam scanning technique. Currently, two radiobiological models, LEM I and MKM, are in use for heavy ions in scanned pencil-beam facilities. Approach. Utilizing an interface with the FLUKA Particle Therapy Tool, BIANCA was applied to re-calculate the RBE-weighted dose distribution for carbon-ion treatment of three patients (chordoma, head-and-neck and prostate) previously irradiated at CNAO, where radiobiological optimization was based on LEM I. The predictions obtained by BIANCA were based either on chordoma cell survival (RBE

surv
), or on dicentric aberrations in peripheral blood lymphocytes (RBE

ab
), which are indicators of late normal tissue damage, including secondary tumours. The simulation outcomes were then compared with those provided by LEM I. Main results. While in the target and in the entrance channel BIANCA predictions were lower than those obtained by LEM I, the two models provided very similar results in the considered OAR. The observed differences between RBE

surv
and RBE

ab
(which were also dependent on fractional dose and LET) suggest that in normal tissues the information on cell survival should be integrated by information more closely related to the induction of late damage, such as chromosome aberrations. Significance. This work showed that BIANCA is suitable for treatment plan optimization in ion-beam therapy, especially considering that it can predict both cell survival and chromosome aberrations and has previously shown good agreement with carbon-ion experimental data.
Modeling the impact of spatial oxygen heterogeneity on radiolytic oxygen depletion during FLASH radiotherapy
Authors: Taylor Edward , Hill Richard P , Létourneau Daniel
Published: May 2022

Purpose. It has been postulated that the delivery of radiotherapy at ultra-high dose rates (‘FLASH’) reduces normal tissue toxicities by depleting them of oxygen. The fraction of normal tissue and cancer cells surviving radiotherapy depends on dose and oxygen levels in an exponential manner and even a very small fraction of tissue at low oxygen levels can determine radiotherapy response. To quantify the differential impact of FLASH radiotherapy on normal and tumour tissues, the spatial heterogeneity of oxygenation in tissue should thus be accounted for. Methods. The effect of FLASH on radiation-induced normal and tumour tissue cell killing was studied by simulating oxygen diffusion, metabolism, and radiolytic oxygen depletion (ROD) over domains with simulated capillary architectures. To study the impact of heterogeneity, two architectural models were used: (1) randomly distributed capillaries and (2) capillaries forming a regular square lattice array. The resulting oxygen partial pressure distribution histograms were used to simulate normal and tumour tissue cell survival using the linear quadratic model of cell survival, modified to incorporate oxygen-enhancement ratio effects. The ratio (‘dose modifying factors’) of conventional low-dose-rate dose and FLASH dose at iso-cell survival was computed and compared with empirical iso-toxicity dose ratios. Results. Tumour cell survival was found to be increased by FLASH as compared to conventional radiotherapy, with a 0–1 order of magnitude increase for expected levels of tumour hypoxia, depending on the relative magnitudes of ROD and tissue oxygen metabolism. Interestingly, for the random capillary model, the impact of FLASH on well-oxygenated (normal) tissues was found to be much greater, with an estimated increase in cell survival by up to 10 orders of magnitude, even though reductions in mean tissue partial pressure were modest, less than ∼7 mmHg for the parameter values studied. The dose modifying factor for normal tissues was found to lie in the range 1.2–1.7 for a representative value of normal tissue oxygen metabolic rate, consistent with preclinical iso-toxicity results. Conclusions. The presence of very small nearly hypoxic regions in otherwise well-perfused normal tissues with high mean oxygen levels resulted in a greater proportional sparing of normal tissue than tumour cells during FLASH irradiation, possibly explaining empirical normal tissue sparing and iso-tumour control results.
Fast calculation of the tokamak vertical instability
Authors: Olofsson K E J
Published: May 2022

There has been recent interest in fast calculations of the tokamak axisymmetric vertical instability for real time feedback control purposes. It is shown that the maximum eigenvalue for the basic rigid version of this stability problem can be obtained by finding the positive root to a simple scalar function. This function can be generalized to include plasma mass and has complexity linear in the number of conductive elements. The formulation is based on standard matrix decompositions of the fixed-geometry part of the eigenproblem. The calculation bottleneck is the summary of mutual inductances from the reconstructed equilibrium current density. The with-mass spectrum can be made fully real-valued by the addition of a critical amount of damping with negligible effect on the vertical growth rate. The calculation has been implemented in the plasma control system at the DIII-D tokamak and used in experiments.
Evolution of equilibrium particle beams in plasma under external wakefields
Authors: Baistrukov M A , Lotov K V
Published: May 2022

A beam of ultrarelativistic charged particles in a plasma can reach equilibrium with its own radial wakefield and then propagate with little change in shape. If some co-moving perturbation appears ahead of the beam, it may or may not destroy the beam with its wakefield, depending on the phase and amplitude of the wakefield. We numerically study which perturbations can destroy a single short bunch or a train of many short bunches at the parameters of interest for plasma wakefield acceleration in an axisymmetric configuration, and how fast. We find that there are particularly dangerous wakefield phases in which the beam can be destroyed by perturbations of very low amplitude. We also find that perturbations with an amplitude larger than the wakefield of a single bunch in the train are always destructive.
Analysis of magnetorheological clutch with double cup-shaped gap excited by Halbach array based on finite element method and experiment
Authors: Zhang Guang , Chen Junyu , Zhang Zheng , Sun Min , Yu Yang , Wang Jiong , Cai Shibo
Published: May 2022

This work describes the magnetic analysis of an innovative double cup-shaped gap magnetorheological (MR) clutch featuring with three smart MR gels. Four kinds of Halbach array is used to excite the MR gel. The apparatus is designed by using a magneto/mechanical finite element method model, which is numerical calculated by COMSOL Multiphysics software. After describing the configuration, the transmittable torque in the designed MR clutch is derived based on the Bingham-Plastic field-dependent constitutive model of the MR gel. Considering the viscosity in the model building, such as the shear yield stress, which also various with change of magnetic flux density. The magnetic flux density distribution, the shear yield stress distribution, the dynamic viscosity distribution and the shear stress distribution inside the MR gel are obtained and carefully studied. Furthermore, the chain layer of internal cylindrical part, external cylindrical part, internal disc part and external disc part with lowest shear stress are found to calculate the transmission torque and slip torque. Then, the structure of the prototype is optimized based on multi-physics analysis. Finally, the optimal MR clutch is developed and the magneto-static torque is tested with detail analysis.
Self-confinement of low-energy cosmic rays around supernova remnants
Authors: Jacobs Hanno , Mertsch Philipp , Phan Vo Hong Minh
Published: May 2022

Supernova remnants have long been considered as a promising candidate for sources of Galactic cosmic rays. However, modelling cosmic-ray transport around these sources is complicated by the fact that the overdensity of cosmic rays close to their acceleration site can lead to self-confinement, that is the generation of turbulence upon which these particles scatter. Such a highly non-linear problem can be addressed by numerically solving the coupled differential equations describing the evolution in space and time of the escaping particles and of the turbulent plasma waves. In this work, we focus essentially on the propagation of cosmic rays from supernova remnants in the warm ionized and warm neutral phases of the interstellar medium and propose an extended framework to take into account also the effect of energy loss relevant for cosmic rays of energy below 10 GeV. Interestingly, the diffusion coefficient of low-energy cosmic rays could be suppressed by up to 2 orders of magnitude for several tens of kiloyears after the escape from the shock. The cosmic-ray spectrum outside the supernova remnant flattens below 1 GeV at a sufficiently late time reminiscient of the spectral behaviour observed by Voyager. We also find the grammage accumulated around the source to be non-negligible, with important implications for precision fitting of the cosmic-ray spectra.
On adiabatic renormalization with a physically motivated infrared cut-off
Authors: Animali Chiara , Conzinu Pietro , Marozzi Giovanni
Published: May 2022

We introduce a new approach to renormalize physical quantities in curved space-time by adiabatic subtraction. We use a comoving infrared cut-off in defining the adiabatic counterpart of the physical quantity under consideration, building on the fact that the adiabatic approximation is ill-defined in the infrared tail of the spectrum.
We show how this infrared cut-off should be used to obtain a completely well-defined renormalization scheme and how it is fundamental to avoid unphysical divergences that can be generated by a pathological behavior of the adiabatic subtraction extended to the infrared tail. The infrared cut-off appears as a new degree of freedom introduced in the theory and its actual value has to be consistently fixed by a physical prescription. As an example, we show how such degree of freedom can be set to obtain the correct value of the conformal anomaly in the symptomatic case of an inflationary model with gauge fields coupled to a pseudo-scalar inflaton.
Implications of stochastic effects for primordial black hole production in ultra-slow-roll inflation
Authors: Figueroa Daniel G. , Raatikainen Sami , Räsänen Syksy , Tomberg Eemeli
Published: May 2022

We study the impact of stochastic noise on the generation of
primordial black hole (PBH) seeds in ultra-slow-roll (USR) inflation
with numerical simulations. We consider the non-linearity of the
system by consistently taking into account the noise dependence on
the inflaton perturbations, while evolving the perturbations on the
coarse-grained background affected by the noise. We capture in this
way the non-Markovian nature of the dynamics, and demonstrate that
non-Markovian effects are subleading. Using the ΔN 
formalism, we find the probability distribution P(ℛ) of
the comoving curvature perturbation ℛ. We consider
inflationary potentials that fit the CMB and lead to PBH dark matter
with i) asteroid, ii) solar, or iii) Planck mass, as well as
iv) PBHs that form the seeds of supermassive black holes. We find
that stochastic effects enhance the PBH abundance by a factor of
𝒪(10)–𝒪(108), depending on the PBH mass. We
also show that the usual approximation, where stochastic kicks
depend only on the Hubble rate, either underestimates or
overestimates the abundance by orders of magnitude, depending on the
potential. We evaluate the gauge dependence of the results, discuss
the quantum-to-classical transition, and highlight open issues of
the application of the stochastic formalism to USR inflation.
Deformation fields: a new source of information to predict brain age
Authors: Dias Maria de Fátima Machado , Carvalho Paulo , Duarte João Valente , Castelo-Branco Miguel
Published: May 2022

Objective. The modelling of healthy ageing critically requires the identification of methods that detect subtle changes in this process. In the last few years multiple machine learning models have been proposed that learn age patterns from magnetic resonance images. Current standard information sources rely on local volumetric information of brain tissues, namely white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF). Information about patterns of brain deformation remains underexplored. In this paper an assessment is performed to understand better the predictive value of the deformation fields. Approach. A shallow approach was used to compare the predictive value of deformation fields with the brain tissues (GM, WM and CSF). Images were compressed into a lower dimension space using Principal Components Analysis and then, a Relevant Vector Regression (RVR) learned the age patterns from the components. A model was trained per modality (deformation fields, GM, WM and CSF) and the performance between the models was compared. To evaluate whether the deformation fields increased the predictive power of GM, a model fusion approach was explored in which the final estimator was an RVR. Each model was validated using a cross-validation approach and was also evaluated on an external dataset. Main results. We found that models trained with deformation patterns have higher predictive value than the ones trained with WM or CSF. Furthermore, deformation fields had a significantly better performance on the test set and also yield the lower difference between the validation and test set. Moreover, the predictions based on the combination of deformation patterns with GM volume yields better results than GM volumetric information alone. Significance. These findings suggest that deformation fields have a higher predictive power than WM and CSF and are robustly invariant across a set of confounding variables. Therefore, deformation fields should be considered in BrainAge models.
Powergram: A novel auto-regressive power spectrumbasedfrequency band selection method and its applicationingearfault diagnosis
Authors: Ying Wanming , Zheng Jinde , Pan Haiyang , Ton Jinyu
Published: May 2022

Fast kurtogram (FK) is an effective method for resonance demodulation analysis, which segments the frequency spectrum of raw signal by constructing a tree-shaped band-pass filter bank based on alternate dichotomy or trisection method, then the modulation information is extracted via selecting the maximum kurtosis of components. Though FK has a high computation efficiency, it can not adaptively segment the frequency band due to its 1/3 binary tree structure. Besides, the different characteristics of frequency band filtered signal cannot be comprehensively reflected due to the use of a single kurtosis index in FK. To overcome the above shortcomings of FK, a novel frequency band selection method named Powergram is proposed based on auto-regressive (AR) power spectrum. First of all, the AR power spectrum is computed to overcome the influence of noise and irrelevant components on spectrum segmentation. Second, the minima point nearest to the midpoint of two adjacent maxima is selected as the segmentation boundary, where the maximum segmentation level is preset in advance. Third, a new fusion indicator is constructed by weighting the indexes of kurtosis, correlation coefficient, and spectral negentropy. After that, the frequency band with largest fusion indicator is selected as the optimal demodulation frequency band (ODFB). Then, the envelope spectrum of ODFB filtered signal is analyzed to identify the fault characteristic frequency and complete the fault diagnosis. Finally, Powergram is applied to the simulated and tested data of faulty gear via comparing it with existing FB-based FK and Autogram approaches. The analysis results show that the introduced approach is better than the comparison methods in ODFB selection and fault characteristic extraction.
Bearing fault severity assessment using variable-step multiscale fusion Lempel-Ziv complexity
Authors: Su Zhou , Shi Juanjuan , Huang Weiguo , Wang Jun , Shen Changqing , Jiang Xingxing , Zhu Zhongkui
Published: May 2022

Most research on bearing health condition monitoring is devoted to distinguish the defective condition from the healthy condition. In practice, the fault severity assessment is also critical for performing the prognostics and maintenance of bearings. Lempel-Ziv complexity (LZC) has been widely used for the bearing quantitative fault diagnosis. However, the original LZC extracts fault information only at the single scale and often fails to portray the fault features. Then, the multiscale LZC is proposed to more comprehensively extract the fault information. However, multiscale analysis would shorten the length of time series and lead to inaccurate calculation results as the scale factor increases. As such, this paper proposes a novel bearing fault severity assessment method using variable-step multiscale fusion Lempel-Ziv complexity (VSMFLZC) to facilitate the quantitative fault diagnosis of bearings. The variable step length strategy is developed in the proposed method to optimize the coarse-grained procedure. Then, Laplace score is applied to evaluate the features and weights at each scale to obtain the proposed VSMFLZC. By such a fusion algorithm, the sequence can be converted into a single but comprehensive evaluation indicator for the fault severity assessment. The experimental results indicate that the proposed method outperforms the original LZC and multiscale LZC, where the fault features can be more comprehensively extracted and the fault severity assessment of rolling bearing can be successfully realized.
A sparse feature extraction method based on the shiftinvariable dictionary applied in the diagnosis of gearbox with multiple faults
Authors: Hao Wang , Guangming Dong , Jin Chen , Xugang Hu , Zhibing Zhu
Published: May 2022

The vibration signals of gearbox often contain complex components, such as the gear engagement, shafts rotation, bearings, and vibration of the box, and they brings a big challenge to fault diagnosis of gearbox. Furthermore, there are many fault types of gearbox, and the fault characteristics of them are possibly the same. For example, the pass frequency of the missing tooth or root crack in the gear on the input shaft are both equal to the rotation frequency of input shaft. In thus circumstance, traditional diagnosis methods are inapplicable, because we can’t determine which type of faults. In this paper, we proposed a dictionary learning with the shift-invariant dictionary to extract the fault features in a sparse way, and afterwards hidden Markov model (HMM) is utilized to identify the fault type from extractive features. We used the proposed method to diagnose the experimental data from a benchmark two-stage gearbox and there are nine different health conditions introduced to the pinion on the input shaft, and the diagnosis result verifies the good performance of the novel scheme in the fault diagnosis of gearbox.
Early Fault Diagnosis of Gearbox Based on VWPSO-VMD
Authors: Cao Shunxin , Li Hongkun , Zhang Kongliang , Wang Zhaodong
Published: May 2022

Aiming at the problem that the characteristic of the early weak fault of the gearbox is submerged by the background noise under the low-speed operating condition, this paper proposes a gearbox fault diagnosis method based on the variable weighted particle swarm optimization variational mode decomposition (VWPSO-VMD). First, a variable weight particle swarm optimization algorithm (VWPSO) is proposed, and the effectiveness of the VWPSO is verified by the optimization calculation of test functions. Secondly, using fuzzy entropy as the fitness function, the proposed VWPSO is used to optimize the modal number K and the penalty factor α of the variational mode decomposition (VMD). Then, based on the grey relational analysis method (GRA), the optimal intrinsic mode function (IMF) is selected for envelope demodulation analysis. Finally, VWPSO-VMD and empirical mode decomposition (EMD) are used to diagnose the early crack and broken tooth fault signals of the gearbox. The results show that, compared with EMD, VWPSO-VMD can more accurately extract the early fault characteristic frequency of the gearbox. This provides a new method for the extraction of early fault features of the gearbox.
Varying-parameter time-frequency manifold for gearbox fault diagnosis
Authors: Wang Yuqi , Wang Jun , Jiang Xingxing , Huang Weiguo , Shi Juanjuan , Zhu Zhongkui
Published: May 2022

The vibration signals of a faulty gearbox are non-stationary and contaminated by heavy background noise. Time-frequency transform is able to present the non-stationary fault impulsive features in the time-frequency distribution (TFD). However, the time-frequency fault information is still contaminated by the noise. This paper proposes a varying-parameter time-frequency manifold (VPTFM) method with the aim to remove the noise in the TFD for accurate identification of gearbox fault. First, a high-dimensional TFD is constructed by performing short- time Fourier transform (STFT) using some variable window lengths. Then, local tangent space alignment (LTSA) algorithm is carried out on the high-dimensional TFD to extract the manifold of the fault impulsive features with two dimensions, in which Rényi entropy is employed to select the proper neighborhood size for the LTSA by evaluating the first dimensional manifold. Afterwards, a threshold is designed by exploring the characteristics of the amplitudes of the manifold at two dimensions to adaptively remove the noise survived in the first dimensional manifold. Finally, the amplitudes at the frequency possessing the largest energy in the denoised manifold are taken out for spectrum analysis to identify the fault characteristic frequency. The enhanced performance of the proposed method in extraction of fault impulses and removal of background noise is validated by a gearbox experimental vibration signal measuring when the gear has a wearing fault.
Hierarchical Diagnosis Network Based on Easy Transfer Learning and Its Application in Bearing Fault Diagnosis
Authors: Zhu Liangyu , He Shuilong , Ouyang Li , Hu Chaofan , Wang Yanxue
Published: May 2022

Aiming at the problem of inconsistent distribution of rolling bearing vibration data under variable operating conditions, insufficient diagnostic data of the target bearing affects the accuracy of fault diagnosis, and the unknown severity of rolling bearing faults, a hierarchical diagnosis network based on easy transfer learning is presented in this paper and its application in the qualitative and quantitative diagnosis of rolling bearing faults. First, the wavelet transform is used to extract the fault features conducive to identifying the rolling bearing vibration data under various working conditions. Then, input the features extracted from the vibration signals of different fault types into the first layer easy transfer learning fault type recognizer to determine whether the target bearing is faulty and the fault type. After the fault type is determined, the features extracted from the vibration signals of the known fault types and different fault sizes are input into the second layer easy transfer learning fault size recognizer to determine the fault size of the rolling bearing. The proposed method is validated by the bearing data set of Case Western Reserve University and compared with other transfer learning methods that perform the same processing. The experimental results show the effectiveness and superiority of the method.
Domain Adaptation for Intelligent Fault Diagnosis from Relation Perspective
Authors: Tang Lv , Zhang Qing , Xuan Jianping
Published: May 2022

A novel domain adaptation technology for fault diagnosis is introduced to solve the variable working condition problem. As the relationships between things are widespread according to human visual and cognitive logic, the network also learns the relation and shows its sense to classify. Thence, transfer knowledge beyond the layer features are constructed to learn a relation invariant representation. The hierarchical relation and category relation are considered, while the connection between relation alignment and feature alignment is uncovered using exceptional cases. The hierarchical relation captures the behavior of information flow in adjacent layers, and the category relation captures the similarity between bearing faults. Empirical evidence provided by the experiment with variable speed test demonstrates that the proposed model outperforms the existing method.
Attitude data-based deep transfer capsule network for intelligent fault diagnosis of delta 3D printers
Authors: Qin Y X , Hong Y , Long J Y , Yang Z , Huang Y W , Li C
Published: May 2022

In order to improve the quality of printed products and promote the application of 3D printing, it is necessary to carry out health monitoring and fault diagnosis for 3D printers. In this paper, an attitude data-based deep transfer capsule network is proposed for intelligent fault diagnosis of delta 3D printers. Based on the forward kinematic analysis, the attitude data change of the moving platform can reflect the fault information of the printers. To extract fault features from the attitude data with rich directional pose information and complete the cross-domain diagnosis task effectively, the proposed approach consists of a feature encoder with capsule layer, a fault pattern classifier, and a domain discriminator. Through the domain adversarial training, the model can minimize the difference between the source domain and the target domain data distribution, and the trained classifier can obtain better diagnosis performance in the target domain. The experiment result demonstrates the superiority and effectiveness of the proposed method for fault diagnosis problems of delta 3D printers.
Research on surface damage detection of wind turbinebladebased on machine vision
Authors: Wang Yu , Zou Li
Published: May 2022

The research of wind turbine blade surface damage identification is of great significance to reduce the risk of wind turbine operation. Aiming at the limitation of manual inspection based damage detection, this paper takes the wind turbine as the object of identification and detection, takes the advanced machine detection method as the research content. In order to realize the task of identification and detection of turbine blade damage, an image classification system of turbine blade based on convolution neural network is proposed. In this paper, the convolution neural network (CNN) based damage identification system is compared with the support vector machine (SVM) based damage identification system. The experimental results show that the CNN based damage identification system has stronger generalization ability and higher classification accuracy in the case of randomly dividing experimental sample data.
Tooth root crack detection of planet gear in RV reducer based on improved compute order tracking and angular domain synchronous averaging
Authors: Yan Y H , Guo Y , Liu X Q
Published: May 2022

Rotate vector (RV) reducers are widely used as the joints of industrial robots. Due to RV reducers often work in a reciprocating condition, it makes the corresponding vibration for fault detection is incomplete since the incompletely rotation of the output shaft. Thus, traditional methods such as the vibration separation (VS) cannot be used to detect the tooth fault of planet gear in RV reducer. Moreover, few literatures reported the tooth fault detection of RV reducer without disassembly. To address this issue, a method for detecting the planet gear tooth root crack fault of RV reducers is proposed in this paper. It combines the improved compute order tracking (ICOT), the signal extraction and reconstruction of planet gear, and the synchronous averaging (SA). The ICOT is utilized to obtain data blocks of each tooth of the gear on the reference shaft while eliminating the inevitable speed fluctuations. Subsequently, the tooth mapping relationship between the planet gear and sun gear can be calculated by the planetary meshing structure. Then, the data block of each tooth of the planet gear can be extracted from the resampled vibration. Next, data blocks of the planet gear can be constructed into a synthetic signal according to the tooth mapping relationship. Furthermore, the SA is applied to enhance the signal-noise ratio (SNR) of the synthetic signal. Finally, spectral analysis is utilized to expose the fault characteristics. The comparative experiment with the normal and faulty planet gear in an RV reducer test rig validated the effectiveness of the proposed method.
Fault Feature Extraction of Automobile Wheel Hub Bearing Based on RSK and RSSD
Authors: Xue H T , Zhang Z M , Wu M
Published: May 2022

Wheel hub bearing not only bears axial load but also radial load, and then its running status directly affects the performance and safety of the automobile. In particular, complicated and variable driving conditions of the automobile are not only liable to aggravate the occurrence of mechanical fault such as localized wear, but also often cause powerful intermittent interference noise. It is difficult to extract the vibration characteristics of the transient impact and harmonic components of the wheel bearings in the event of a local fault. An extraction method of fault feature based on ration of smooth and kurtosis (RSK) index and resonance-based signal sparse de-composition (RSSD) is proposed for automobile wheel hub bearings, which aims at the minimum value of RSK index, sequentially optimizes the Q-factor of the resonance sparse decomposition, and obtains its optimal value adaptively to achieve the decomposition of low resonance components with transient impact components and high resonance components with harmonic components. The effect of the method has been verified by powerful intermittent interference simulation signals and experiment signals from local fault of wheel hub bearing with periodic dynamic radial loading.
Rotor crack and bearing fault diagnosis based onVMD-ICA
Authors: Wang Rui , Lv Lumei , Yang Funing , Zhang Wei , Hua Chunrong , Dong Dawei
Published: May 2022

The rotor-bearing system is vulnerable to rotor cracks, bearing failures, or even multiple failures at the same time during long-term service, and it is more difficult to diagnose the faults under rotor crack and bearing fault coupling conditions. This paper proposed a signal reconstruction method to classify the rotor crack, the bearing outer ring fault and their coupling faults based on Variational Mode Decomposition (VMD) and Independent Component Analysis (ICA). Firstly, the original vibration signal collected on a rotor-bearing test bench is decomposed with VMD, and several intrinsic mode functions (IMF) are obtained. Then, the correlation kurtosis CK

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which introduces the characteristics of periodic correlation function is to evaluate the decomposed IMF component, and screen the rotor crack information component. Finally, the rotor crack information component is input into a virtual channel signal and separated with the original vibration signal with ICA, and then the separated two sets of signals are superimposed to obtain a vibration signal that simultaneously highlights the information of the rotor crack and the bearing outer ring fault. In addition, the time domain signal is directly input into a LightGBM to diagnose different faults. The research results show that this method can simultaneously diagnose the rotor crack, the bearing outer ring fault and the coupling fault.
An auto-correlation-based method for structural damage identification using displacement information
Authors: Li Y J , Lei C , Li J T , Yu F J , Wang Z P , Zhang M Y
Published: May 2022

Vibration-based damage identification methods have been received widespread attention these days for its property of global damage identification. This paper presents a damage identification method using the Auto correlation function at Maximum point value Vector (AMV) of vision-based displacement response of the structure. Sensitivity analysis for the normalized AMV to the local stiffness shows there is a sharp change of the damage index around the damage location, which means it is a good indicator for the damage. The influence of the excitation frequency on the detect ability of this proposed method is verified by the numerical simulation and non-contact damage identification experiment of a shear frame.
A method to assess the value of monitoring an SHM system
Authors: Giordano Pier Francesco , Quqa Said , Limongelli Maria Pina
Published: May 2022

Aging structural components, together with the increasing transportation needs and limited budgets, are challenging aspects that typically concern decision-makers and infrastructure owners. Although Structural Health Monitoring (SHM) has been a powerful tool to optimize maintenance-related activities and post-disaster emergency management, the sensor readout and, therefore, the outcome of the monitoring system is susceptible to errors due to malfunctioning. For years, the Value of Information (VoI) has been studied to quantify the long- term benefit of SHM systems against the initial investment in sensing instrumentation without considering the eventuality of faulty sensing nodes. However, these are very common in field applications. This paper proposes a new framework to calculate the benefit of using Sensor Validation Tools (SVTs) before calculating the damage-sensitive features that drive the SHM process. The novel approach extends the traditional Vol to consider multiple “health” states of the SHM system, associate the outcome of the SHM system with the state of both the structure and the SHM system, and quantify the additional value obtained from SVTs.