bluuebunny/crossref_metadata_2025 huggingface dataset:
Parameterizations of black-hole spacetimes beyond circularity
Authors: Delaporte Héloïse , Eichhorn Astrid , Held Aaron
Published: May 2022

We discuss parameterizations of black-hole spacetimes in and beyond general relativity in view of their symmetry constraints: within the class of axisymmetric, stationary spacetimes, we propose a parameterization that includes non-circular spacetimes, both in Boyer–Lindquist as well as in horizon-penetrating coordinates. We show how existing parameterizations, which make additional symmetry assumptions (first, circularity; second, a hidden constant of motion), are included in the new parameterization. Further, we explain why horizon-penetrating coordinates may be more suitable to parameterize non-circular deviations from the Kerr geometry. Our investigation is motivated by our result that the regular, spinning black-hole spacetimes proposed in Eichhorn and Held (2021 Eur. Phys. J. C
81 933); Eichhorn and Held (2021 J. Cosmol. Astropart. Phys.
5 73) are non-circular. This particular deviation from circularity can result in cusps, a dent and an asymmetry in the photon rings surrounding the black-hole shadow. Finally, we explore a new class of non-circular deviations from Kerr black holes, which promote the spin parameter to a function, and find indications that regularity cannot be achieved in this setting. This result strengthens the case for regular black holes based on a promotion of the mass parameter to a function.
Functional composite microbeads for cell-based meat culture: effect of animal gelatin coating on cell proliferation and differentiation
Authors: Kong Yan , Ong Shujian , Liu Mei Hui , Yu Hanry , Huang Dejian
Published: May 2022

Hydrogel microbeads are promising matrices for cell-based meat culture because they can be conveniently fabricated from food-grade materials. In this study, food-grade sodium alginate was successfully applied to fabricate edible microbeads with the desired diameters (∼200 µm) using electrospray technology. Pure alginate microbeads have poor cytoaffinity. To enhance the cytoaffinity of alginate microbeads for meat culture, we applied different gelatin coatings, namely porcine gelatin, bovine gelatin, and fish gelatin, onto alginate beads. The modification is non-toxic and environmentally friendly with the use of plant-derived crosslinkers and under mild reaction conditions. As all ingredients are food grade, the modified microbeads are edible and biodegradable, with the mean diameters of 142.96 ± 35.49 μm (porcine gelatin-coated beads), 105.12 ± 80.46 μm (bovine gelatin-coated beads), and 94.81 ± 67.38 μm (bovine gelatin-coated beads). These modified microbeads have self-fluorescence and can be easily distinguished under a fluorescent microscope. The hydrogel core enables the microbeads to have a density of 1.04 g cm−3 for suspension culture. Bovine gelatin-coated microbeads have the best primary porcine myoblast and C2C12 cell adhesion, and can achieve between 16- to 18-fold proliferation of different muscle cells. With the successful differentiation of C2C12 and proliferation of 3T3-L1 adipocytes on bovine gelatin-coated microbeads, cells and microbeads can form clusters, illustrating that our edible microbeads are promising microcarriers for mammalian cell culture for cell-based meat production.
Plasma-based experimental investigation of double compression ramp shock wave/boundary layer interaction control
Authors: Zhang Chuan-Biao , Yang He-Sen , Liang Hua , Guo Shan-Guang
Published: May 2022

An experimental investigation on the control effects of the high-frequency streamwise pulsed arc discharge array (HS-PADA) on the double compression ramp shock wave/boundary layer interaction (DCR-SWBLI) was carried out at Mach 2.0. Firstly, two types of ramp configurations were designed. The base flow field and actuation flow field were investigated. The actuation frequencies were 10 kHz and 20 kHz. Fast Fourier transform and root-mean-square methods were applied based on schlieren images. The base flow field indicated that the compression effect of ramps decreased with the lengthen of the first ramp. The results of actuation flow field showed that the 20 kHz actuation was superior to the 10 kHz actuation in weakening the shock wave intensity. The HS-PADA exhibited two types of control effects: modifying the shock structure, which was closely related to the separation zone, and modifying the low-frequency unsteadiness of the shock wave, which might not be related to the separation zone. The first separation shock wave, of which the high-frequency motion was more intense under actuation, may be more sensitive to HS-PADA than the second. Finally, the control mechanism of the HS-PADA on DCR-SWBLI was extracted.
Dual-emission fluorescent nanoprobe based on Ag nanoclusters for sensitive detection of Cu(II)
Authors: Chen Ming , Ao Wei , Bai Jinna , Li Pengjun , Wei Wei , Pang Shujie , Yang Xudong
Published: May 2022

Noble metal nanoclusters have attracted much attention because of their excellent fluorescence properties. In this work, we demonstrated a dual-emission fluorescent nanocomposite based on silver nanoclusters. First, we synthesized positively charged His-AgNCs, which emits intense blue light, and then Ag nanoclusters with stable red emission were synthesized using DHLA as the ligand. Thus a dual-emission fluorescent nanoprobe was successfully obtained through electrostatic self-assembly, with the advantages of good water solubility and excellent stability. Based on the intensity ratio of the two emission peaks, the nanoprobe can be used for selective and sensitive detection of copper ions, and presents a good linear relationship within a certain concentration range. In addition, we also designed a polymer film, and our dual-emission nanoprobe was successfully loaded onto it, which means that the visual detection of copper ions is possible. This indicates that our dual-emission fluorescent nanoprobe has potential application prospects in environmental analysis, medical diagnosis, biological detection, etc.
Triple-band electromagnetically induced transparency effects enabled by two sets of arc-ring-type resonators at terahertz frequency
Authors: Xu Wei , Yang Zhuchuang , Zhou Haiquan , Wu Yangkuan , Zhu Huaxin , Zhang Xiangyang , Wang Ben-Xin
Published: May 2022

A new type of terahertz metamaterial resonance device with triple-band electromagnetically induced transparent (EIT) is introduced in this paper. Its unit cell consists of two sets of arc-ring-type resonators placed on a dielectric sheet, which can generate three EIT transparency peaks at terahertz region. The generation of these transparent peaks is mainly attributed to the coupling effect between the resonant modes of the arc-ring-type resonators. Influence of the arc length and radius of the arc-ring-type resonators and the width of the air layer between the inner arc-ring-type resonators and outer arc-ring-type resonators on the transmission spectrum is studied. Furthermore, active tunability of the transparent peaks is achieved by incorporating photosensitive silicon with tunable conductivity in the metamaterial structure. The results show that depending on the change of conductivity as well as the position of photosensitive silicon, the EIT effect can be shown as an adjustable switch from triple-band to dual-band or from triple-band to single-band. In addition, the refractive index sensing performance of the metamaterial device is discussed. The metamaterial device has the excellent characteristics of simple structure design and active modulation, which are important and useful for the development of photoelectric switches, sensors and filter devices.
Expansion history-dependent oscillations in the scalar-induced gravitational wave background
Authors: Witkowski Lukas T. , Domènech Guillem , Fumagalli Jacopo , Renaux-Petel Sébastien
Published: May 2022

Oscillations in the frequency profile of the scalar-induced contribution to the stochastic gravitational wave background are a characteristic signal for small-scale features during inflation. We investigate how this oscillatory frequency profile is affected by the expansion history of the post-inflationary universe. Our results are applicable as long as the equation of state of the universe can be taken as constant during the period in which the gravitational waves are produced, and we compute the spectrum of gravitational waves induced by both sharp and resonant features, associated with oscillations in k and log(k), respectively. For resonant features, the frequencies of the oscillatory contributions to the gravitational wave spectrum are unaffected by the equation of state, but not their relative amplitudes, allowing one to extract information about both inflationary physics and the post-inflationary expansion history from the oscillatory pattern. For sharp features we find that the gravitational wave spectrum only exhibits prominent modulations as long as the propagation speed of density fluctuations is cs
< 1, with a frequency larger by a factor cs

-1 than that of the scalar power spectrum. We find that the stiffer the equation of state, the larger the relative amplitude of the oscillations. In particular, a relative amplitude significantly higher than 20 % is not achievable for the `standard' case of radiation domination, and would be a smoking-gun signal of both nontrivial inflationary dynamics on small scales, and a post-inflationary universe not dominated by radiation.
Geometric structure of multi-form-field isotropic inflation and primordial fluctuations
Authors: Chen Chong-Bin , Soda Jiro
Published: May 2022

An inflationary scenario is expected to be embedded into an ultraviolet (UV)
complete theory such as string theory. The effect of UV complete theories may appear as nontrivial kinetic terms in the low energy effective field theory, which provides a nontrivial
geometry in field space. In this paper, we study the effect of the geometry
of multi-form-field space on an inflationary scenario.
In particular, we focus on the geometric destabilization mechanism
which induces the phase transition from the conventional slow-roll inflation
to a novel inflationary scenario. Anisotropic inflation is a typical example of the new phase. To conform to observations, we restrict us to isotropic configuration of form fields.
We clarify the conditions for the onset of the destabilization and
reveal the geometric structure of attractors after the destabilization.
We classify the viable models from the observational point of view.
We also investigate the features of the primordial fluctuations and
find the similarity to hyperbolic inflation.
By calculating the power spectrum, we make several
phenomenological predictions which are useful to discriminate our models from others inflation models. We found the scalar-to-tensor r will be suppressed by large one-form gauge fields, while it has the same order as the slow roll parameter r ∼ 𝒪(1)ϵ for large two-from gauge fields.
Comment on ‘Effects of thermal radiation and slip mechanism on mixed convection flow of Williamson nanofluid over an inclined stretching cylinder’ (Commun. Theor. Phys.

71 1405–15)
Authors: Pantokratoras Asterios
Published: May 2022

Some errors exist in the above paper.
Noninvasively recorded high-gamma signals improve synchrony of force feedback in a novel neurorehabilitation brain–machine interface for brain injury
Authors: Flint Robert D , Li Yongcheng , Wang Po T , Vaidya Mukta , Barry Alex , Ghassemi Mohammad , Tomic Goran , Brkic Nenad , Ripley David , Liu Charles , Kamper Derek , Do An H , Slutzky Marc W
Published: May 2022

Objective. Brain injury is the leading cause of long-term disability worldwide, often resulting in impaired hand function. Brain–machine interfaces (BMIs) offer a potential way to improve hand function. BMIs often target replacing lost function, but may also be employed in neurorehabilitation (nrBMI) by facilitating neural plasticity and functional recovery. Here, we report a novel nrBMI capable of acquiring high-γ (70–115 Hz) information through a unique post-traumatic brain injury (TBI) hemicraniectomy window model, and delivering sensory feedback that is synchronized with, and proportional to, intended grasp force. Approach. We developed the nrBMI to use electroencephalogram recorded over a hemicraniectomy (hEEG) in individuals with TBI. The nrBMI empowered users to exert continuous, proportional control of applied force, and provided continuous force feedback. We report the results of an initial testing group of three human participants with TBI, along with a control group of three skull- and motor-intact volunteers. Main results. All participants controlled the nrBMI successfully, with high initial success rates (2 of 6 participants) or performance that improved over time (4 of 6 participants). We observed high-γ modulation with force intent in hEEG but not skull-intact EEG. Most significantly, we found that high-γ control significantly improved the timing synchronization between neural modulation onset and nrBMI output/haptic feedback (compared to low-frequency nrBMI control). Significance. These proof-of-concept results show that high-γ nrBMIs can be used by individuals with impaired ability to control force (without immediately resorting to invasive signals like electrocorticography). Of note, the nrBMI includes a parameter to change the fraction of control shared between decoded intent and volitional force, to adjust for recovery progress. The improved synchrony between neural modulations and force control for high-γ signals is potentially important for maximizing the ability of nrBMIs to induce plasticity in neural circuits. Inducing plasticity is critical to functional recovery after brain injury.
Designing and validating a robust adaptive neuromodulation algorithm for closed-loop control of brain states
Authors: Fang Hao , Yang Yuxiao
Published: May 2022

Objective. Neuromodulation systems that use closed-loop brain stimulation to control brain states can provide new therapies for brain disorders. To date, closed-loop brain stimulation has largely used linear time-invariant controllers. However, nonlinear time-varying brain network dynamics and external disturbances can appear during real-time stimulation, collectively leading to real-time model uncertainty. Real-time model uncertainty can degrade the performance or even cause instability of time-invariant controllers. Three problems need to be resolved to enable accurate and stable control under model uncertainty. First, an adaptive controller is needed to track the model uncertainty. Second, the adaptive controller additionally needs to be robust to noise and disturbances. Third, theoretical analyses of stability and robustness are needed as prerequisites for stable operation of the controller in practical applications. Approach. We develop a robust adaptive neuromodulation algorithm that solves the above three problems. First, we develop a state-space brain network model that explicitly includes nonlinear terms of real-time model uncertainty and design an adaptive controller to track and cancel the model uncertainty. Second, to improve the robustness of the adaptive controller, we design two linear filters to increase steady-state control accuracy and reduce sensitivity to high-frequency noise and disturbances. Third, we conduct theoretical analyses to prove the stability of the neuromodulation algorithm and establish a trade-off between stability and robustness, which we further use to optimize the algorithm design. Finally, we validate the algorithm using comprehensive Monte Carlo simulations that span a broad range of model nonlinearity, uncertainty, and complexity. Main results. The robust adaptive neuromodulation algorithm accurately tracks various types of target brain state trajectories, enables stable and robust control, and significantly outperforms state-of-the-art neuromodulation algorithms. Significance. Our algorithm has implications for future designs of precise, stable, and robust closed-loop brain stimulation systems to treat brain disorders and facilitate brain functions.
Research on online monitoring and fault diagnosis system based on multivariate empirical mode decomposition
Authors: Li Linfeng , Lv Yong , Yuan Rui , Dang Zhang , Wu Lifeng
Published: May 2022

Mechanical equipment is crucial to industrial production, so the monitoring and fault diagnosis of its vibration signals is important to ensure production safety. And this paper mainly researches on an online monitoring and fault diagnosis system. The system makes use of multiple sensors to collect vibration signals from different positions of machines for multivariate signals so that condition of the machines can be monitored and information of local faults can avoid loss. Besides, this paper applies a method for fault feature extraction of mechanical equipment based on multivariate empirical mode decomposition (MEMD), which can accurately extract frequency of fault features to realize fault diagnosis when the features are weak in the early period. And this paper puts forward a design to the overall framework of the system based on technical requirements of the system, then introduces the multi-sensor mechanical fault diagnosis method based on MEMD. On such basis, this paper employs LabVIEW and Python to develop the upper computer software of the system, and experiments are carried out to testify whether this system is viable or not. The research results show that the system developed by this paper has practical application value in condition monitoring and fault diagnosis of mechanical equipment.
Mass estimation method of loose parts based on function model
Authors: Sheng Lin , Xu Zhongfeng , Tan Yi , Ma Bo
Published: May 2022

The nuclear main pump has a complex structure and contains a large number of components. With the continuous flow of the primary circuit coolant, there is a risk of loosening and falling off of the components in the reactor. The existence of loose parts in the reactor makes the reactor operation a safety hazard. Accurate mass estimation of the loose parts is conductive to quickly judge the degree of the failure of loose parts, provide effective suggestions for the subsequent maintenance of the reactor and ensure the safety of reactor operation. The existing mass estimation methods have problems of large errors and poor consistency. This article proposes a method for estimating the mass of loose parts by establishing a functional relationship model based on signal characteristic value. The characteristic value of the signal is constructed by extracting the Root Mean Square value and the main frequency of the shock signal, and then the functional relationship between the quality of the loose part and the characteristic value of the signal is established, which is used to estimate the mass of the loose part. The experimental results show that the method proposed in this paper has the characteristics of small estimation error and good consistency, which can meet the needs of practical engineering applications.
Feature extraction of scraping noise of the car seat electric track based on sparse representation
Authors: Hou Fatao , Chen Jin , Dong Guangming , Li Yao , Zhang Ke
Published: May 2022

With the development of the science and technology, the demanding of convenience and comfort when driving a car are getting higher and higher. As an important part that has a great influence on the comfort of the car driving, the car seat is also developing towards the electric direction. The electric seat is becoming the choice of more and more manufacture of automobile. However, the product quality inspection of the electric track of the car seat is still in a relatively preliminary stage. At present, the detection of the unqualified track product is mainly performed by workers listening in an anechoic room. This will lead to the high cost of construction of the anechoic chamber, low quality inspection efficiency, and instability by different quality inspection personnel. In this paper, we focus on the scraping noise detection of the electric track product, as scraping noise is the main cause of the product disqualification. Since scraping noise often features a higher energy in the high frequency band, we adopt the Short Time Fourier Transform (STFT) to acquire the total energy at different time duration. Then we model the acquired time series as summation of one sparse signal, one low-passing signal, and one noise signal. Based on the property of the three parts, the objective function is established and solved with Majorization-Minimization (MM) approach. The effectiveness of the proposed scraping detection method is verified with the real track vibration signal, and the superiority of the proposed method is verified via the comparison with other impulse detection method.
Refined time-shift multiscale diversity entropy: a novel feature extraction algorithm for fault diagnosis of planetary gearbox
Authors: Wang Shun , Li Yongbo
Published: May 2022

Planetary gearboxes play a critical role in aerospace and heavy industry fields, such as wind turbines, heavy vehicles and construction machines. Intelligent fault diagnosis is significant for safe operation and fault prevention of planetary gearboxes. Recently, multiscale diversity entropy and related entropy methods are proposed to extract features of time series and applied for the fault diagnosis. However, there are still some limitations in fault feature representation and stability for multiscale diversity entropy. To solve the problem, in this paper, a novel planetary gearboxes fault diagnosis method via refined time-shift multiscale diversity entropy (RTSMDE) is proposed. First, a novel entropy algorithm called RTSMDE is proposed to measure the complexity of time series and extract fault features of the vibration signals, which is robust and efficient in performance. Then, the obtained features are utilized to fulfil automatically the fault pattern identifications using support vector machine. To confirm the superiority of the RTSMDE-based fault diagnosis method, simulated signals and experimental studies are constructed and three used widely methods are employed to present a comprehensive comparison. The results indicate that RTSMDE performs best and obtains the highest accuracy.
Dynamic modelling of two-stage gear systems with localized wear defects
Authors: Huangfu Y F , Yu X L , Dong X J , Long X H , Peng Z K
Published: May 2022

Considering the profile deviation induced by localized wear defects, a dynamic model of two-stage gear systems is established. In the proposed dynamic model, the time-varying mesh stiffness of worn gears is calculated by the loaded tooth contact analysis method. The mesh stiffness and the non-loaded transmission error are regarded as the time-varying stiffness excitation and the displacement excitation, respectively. Based on the proposed dynamic model, the dynamic responses of the two-stage gear system with localized wear defects are acquired. The simulated vibration responses are compared to the measured results to test the effectiveness of the proposed dynamic model. It is found that the proposed model can yield similar fault feature with the experiment, which shows the validity of the proposed dynamic model. This paper can afford theoretical basis for the fault mechanism and dynamic characterization of localized wear defects.
An abnormal vibration diagnosis and control method for internal combustion power plant
Authors: Xu Fanghui , Dong Dawei , Yan Bing , Song Shizhe , Zhang Rui , Huang Yan
Published: May 2022

Focusing on the difficulty in vibration control of internal combustion power plant because of its diverse substructures and complex excitation, an abnormal vibration diagnosis and attenuation method based on the system’s whole-body vibration signals is proposed. The characteristics of the exciting forces, such as toppling torque, inertia force (or moment) and coupling centrifugal force, are analyzed. The resonance frequency distribution of the power unit is obtained by modal analysis. Then an expression that could reveal the relationship between the whole-body vibration intensity and the exciting force amplitude is established, and conclusion is drawn that different excitations would drive the system to vibrate at different frequencies. Based on this basic relation, a fault diagnosis method relying on comprehensive analysis of vibration signals in both time and frequency domain is proposed. Vibration attenuation methods targeted at specific faults are also put forward. Finally, the validity of the proposed method is verified by applying it to a 12V280 diesel generator set.
Construction and application of knowledge graph for fault diagnosis of turbine generator set based on ontology
Authors: Wang J , Yan C F , Zhang Y M , Li Y J , Wang H B
Published: May 2022

Aiming at the problems of complex structure and multi-source heterogeneity, imperfect knowledge representation, single knowledge extraction method, and difficulty of sharing and reuse in the knowledge field of turbine generator set fault diagnosis. The construction of knowledge graph is studied from multiple dimensions such as experts, fault characteristics, diagnosis techniques, research results, and solutions in the field for fault diagnosis knowledge of turbine generator set. And an ontology model of fault diagnosis knowledge for the turbine generator set is constructed. The entities, attributes, and relationships of the fault diagnosis knowledge graph for the turbine generator set are represented based on the model. The knowledge graph data are stored by the Neo4j graph database. The problems caused by multi-source and heterogeneous knowledge, fuzzy knowledge, and difficulty sharing, was solved in this field. The knowledge search system and automated quiz system based on knowledge graph are developed using the B/S framework. Many functions are realized by the knowledge graph, such as knowledge correlation, intelligent retrieval, visual display, and automated quiz, which improves the service and sharing ability of fault diagnosis knowledge for turbine generator set. Finally, the effectiveness and superiority of the system are verified by an example of a turbine generator set.
A new strategy to eliminate interference of varying operating conditions during multivariate signal processing-based fault diagnosis approach
Authors: Yuan Rui , Lv Yong , Yang Di , Lu Zhiwen
Published: May 2022

Multivariate signal acquisition and processing of faulty mechanical components extract more fault characteristic information, contributing to comprehensive and accurate fault diagnosis. Multivariate empirical mode decomposition (MEMD) is an effective approach in the field of multivariate signal processing. While nuisance attribute projection (NAP) can be used to filter out the components of nuisance attributes caused by varying operating condition. This paper proposes a new strategy for eliminating interference during multivariate signal processing- based fault diagnosis approach. MEMD is used to decompose multivariate signal of faulty rolling bearing into multivariate IMFs depicting dynamical properties, with extracting quantitative features of multivariate IMFs. Then, NAP is used to eliminate nuisance attributes of varying operating conditions in the constructed feature matrices. To illustrate the effectiveness and superiority of the proposed new strategy, the extracted features are presented by t-distribution stochastic neighbour embedding (t-SNE). The dimensionalities of feature vectors are reduced to 3 on account of the visualization simplicity. The comparison analysis during application researches can validate the superiority of the proposed novel strategy.
Multi-fault Classification of Train Wheelset System
Authors: Xiong Libo , Lv Lumei , Jiang Yuhan , Hua Chunrong , Dong Dawei
Published: May 2022

The condition monitoring and faults diagnosis of wheelset are of great significance for the safe and stable operation of trains. Aiming at the axle crack, wheel flat, wheel out-of-roundness and their coupling faults in wheelset system, this paper proposes a method to diagnose these multiple faults simultaneously. Firstly, the dynamic model of a wheelset system with flat and out-of-roundness is established in SIMPACK software to analyze the fault characteristics of these two typical wheel damages. And the wheel flat and out-of-roundness are equivalent to displacement excitation and then loaded into the finite element (FE) model of the cracked wheelset. The vibration responses of the cracked wheelset with flat and out-of-roundness are solved in ABAQUS software. Then, the simulated multi-fault vibration data of wheelset are input into a Light-GBM model to realize the faults classification of axle crack, wheel flat, wheel out-of-roundness and their coupling faults. Finally, the wheelset vibration signals under the conditions of health, axle crack, wheel flat, and crack-flat coupling fault are collected from an experimental bench to verify the proposed method. This work can provide a basis for multi-fault classification and on-line health monitoring of trains.
Digital Twin-Driven Crack Monitoring for Rotating Blade: An L1 regularization Method
Authors: Pang Ding , Yang Laihao , Yan Ruqiang , Chen Xuefeng , Yang Zhibo , Sun Yu
Published: May 2022

Crack-related damage of rotating blade is one of the most common failures of aeroengines. However, conventional blade crack monitoring methods are challenging to obtain the exact location and severity of the crack. In this study, a digital twin-driven blade crack monitoring method using the L1 regularization and sensitivity-based model updating is proposed to quantify the location and severity of crack damage for rotating blade. First and foremost, a digital twin model framework for rotating blade is established, including physical entity, digital entity, and the connection between them. Then a digital entity based on finite element model and an L1 regularized objective function based on the residual of natural frequencies are constructed, where the damage parameter of crack could be obtained by using the sensitivity-based model updating technology, hereby the location and severity of crack could be determined. Finally, numerical simulations are carried out, which verify that the proposed method is capable of accurately identifying the location and severity of crack, and could provide a reference for further research in this field.
On-board vibration-based remaining useful life estimation of hollow worn railway vehicle wheels
Authors: Iliopoulos Ilias A. , Sakellariou John S. , Fassois Spilios D. , Chronopoulos Elias , Deloukas Alexandros , Giannakis Christos , Kapiris Dimosthenis , Kritikakos Kiriakos , Leoutsakos George , Tountas Elias , Vlachospyros Georgios
Published: May 2022

The feasibility of on-board remaining useful life (RUL) estimation of hollow worn railway vehicle wheels under varying travelling speed using vibration signals from the vehicle’s bogie, is for the first time explored. A variant of the Multiple Model Power Spectral Density (MM-PSD) method is employed, treating RUL estimation as a multi-class classification problem, where each class corresponds to a specific condition of hollow worn wheels associated with an empirical remaining mileage. The method classifies the current, unknown, condition of hollow worn wheels to one of the available by examining the vehicular dynamics similarity among them through the amplitude of the sharp periodic valleys (antiresonances) induced due to the Wheelbase Filtering (WF) effect. The vibration signals are obtained from Monte Carlo experiments based on the high-fidelity SIMPACK software, while the remarkable performance of the method is demonstrated via thousands of inspection cases.
Feature Extraction from Charging Profiles for State of Health Estimation of Lithium-ion Battery
Authors: Wang Zuolu , Feng Guojin , Sun Xiuquan , Zhen Dong , Gu Fengshou , Ball Andrew D.
Published: May 2022

Accurate state of health (SOH) estimation of lithium-ion batteries is of great importance to ensure the reliability and safety of battery management systems (BMS). The difficulty of modelling the complex degradation mechanism has made the data-driven methods gain much attention in battery SOH prediction. To improve the estimation accuracy of battery SOH, extracting the suitable health indicators is still a challenging work. In this work, the health indication features are attracted from the charging voltage profile based on the experimental data measured under constant current charging mode. Subsequently, the Pearson correlation coefficient is used to evaluate the relationships between the extracted health features and battery capacity, thus selecting the most effective health features for establishing the prediction models. Finally, the battery SOH is estimated using a Gaussian process regression (GPR) method. The estimation results with R
2 of 1 and lower mean absolute error (MAE) and maximum error (MAX) provide higher accuracy based on the extracted health feature.
Directional wavelet modal curvature method for damage detection in plates
Authors: Yang Gang , Yang Zhi-bo , Zhu Ming-feng , Tian Shao-hua , Chen Xue-feng
Published: May 2022

Vibration-based damage detection using the 2D modal curvature has become a research focus. However, the traditional methods based on modal curvature lack the sensitivity to direction of the damage. To address this problem, a directional 2D Morlet wavelet modal curvature is proposed in this paper. The directional wavelet modal curvatures show the following merits: robust to noise interference and sensitive to direction of damage. By the numerical simulation, the efficiency of the proposed method is demonstrated in the plate-like structure.
Toward on-orbit structural health monitoring of inflatablestructures using electromechanical impedance method
Authors: Li Pengfei , Yan Jinwei , Cao Shancheng , Xu Chao
Published: May 2022

Inflatable structures are promising candidates for a wide range of aerospace vehicles due to their volume/weight savings, high storage rate, low cost, and easy deployment. However, space survivability is a major concern for such structures. Therefore, structural health monitoring (SHM) of inflatable structures has been increasingly studied. Electromechanical impedance (EMI) method is one of the most-popular SHM methods, which can promptly and accurately identify early structural damage. Nevertheless, the necessary of using bulky, expensive and high-power commercial impedance analyzer hampers electromechanical impedance method from being used in-situ and online. In recent years, several miniature impedance measurement devices have been developed for practical applications. However, the aerospace application of such devices is still absence. In this paper, a miniature integrated impedance measurement printed circuit board (PCB) prototype toward on-orbit SHM of inflatable structures is developed. The proposed PCB prototype consists of impedance measurement module, in which the AD5933 chip and the ADG708 chip were adopted to achieve impedance signals. In this case, the PCB prototype can effectively measure the electrical impedance of up to six channels of the piezoelectric transducers at 0.2 ∼ 2V excitation voltage in the frequency range of 10 ∼ 100kHz. Moreover, an inflatable boom was taken as the inspected structure, and a PZT-5H was used as the piezoelectric transducer for experimental validation. The results demonstrate that the proposed PCB prototype works functionally and can accurately quantify the simulated damage on the inflatable boom. In addition, it is found that the miniature integrated impedance measurement PCB prototype developed in this paper has the advantages of low bulk and less weight, which can be further enhanced to satisfy the demands of on-orbit SHM of inflatable structures.
A circumferential SH wave piezoelectric transducer array for damage detection in large-diameter pipes
Authors: Du Yuehao , Kan Qianhua , Kang Guozheng , Miao Hongchen
Published: May 2022

Torsional or longitudinal wave-based inspection is an effective method for rapid defect screening in pipes, but it has difficulty to locate the circumferential position of a defect due to the axially symmetric properties of torsional and longitudinal modes. In order to overcome this problem, several flexural-wave-based focusing methods have been proposed while multiple mode propagation associated with dispersion has to be taken into account in them, which increases the complexity of identifying defects in practical applications. In this work, based on our recently developed bidirectional SH wave piezoelectric transducer (BSH-PT), a fundamental circumferential shear horizontal (CSH0) wave linear phased array was proposed for in-situ defect localization in large-diameter pipes. Firstly, the working principle of the BSH-PT array was presented and its performance on defects detection was assessed by simulations using the total focusing method (TFM). Then, experiments were conducted to examine the array’s performance. Results showed that the proposed BSH-PT array can detect multiple defects simultaneously at varied frequencies. Finally, a method for eliminating the mirrored artifact was proposed and then validated by both simulations and experiments on pipes. Considering the quasi-nondispersive character of CSH0 wave and the desirable performance of the BSH-PT on exciting and receiving pure CSH0 wave, the proposed CSH0 wave phased array may be useful in structural health monitoring (SHM) of large-diameter pipes such as pressure vessels and oil tanks.
Nonlinear assessment of “breathing” fatigue crack using steady-state vibration responses
Authors: Lu Q , Cao M , Xu W
Published: May 2022

Fatigue cracks can occur in structural components under long-term cyclic loads. Such fatigue cracks can open and close like "breathing" under harmonic excitations. Fatigue cracks can develop and jeopardize safety of structures. Therefore, there is a great need to assess fatigue cracks especially in their early stages. Higher harmonics have been widely used as crack indicators, however, slight opening-closing motion can lead to weak higher harmonics with much less amplitudes compared to the first harmonics. In these situations, higher harmonics can be barely identified for crack detection. Addressing this problem, this study develops the damage assessment approach based on singular spectrum analysis to distinguish crack-induced nonlinearities in steady-state vibration responses, whereby nonlinear assessment of the crack can be achieved. The capability of the approach is experimentally validated on a steel beam with a “breathing” fatigue crack, steady-state velocity responses of which are acquired through non- contact laser measurement using a Doppler laser vibrometer.
A two-step identification method for crack parameter identification of train axles
Authors: Zhang Wei , Dong Dawei , Song Shizhe , Xiong libo
Published: May 2022

In this paper, a crack parameter identification method based on the coordinate of 1x intersections and Kriging surrogate model with enhanced samples is proposed to solve the crack parameter identification problem of train axles in start-stop condition. The location of the crack is identified in the first step by the x-coordinate of the linear intersection of 1x frequency components. Then, the intersecting coordinates and rigid constraint points were used to solve response curves of equal crack depth, and numerous enhanced samples of same depth at different positions were obtained to establish a Kriging model. Finally, the crack depth was directly obtained by the Kriging model. In this paper, response curve s of equal crack depth is proposed according to the symmetry of the train structure, which greatly reduces the number of samples needed to construct the Kriging model. Numerical simulation and experimental results demonstrate the effectiveness and robustness of the proposed method.
Structural Health Monitoring of Large Structures via mmWave Sensing
Authors: Li Songxu , Xiong Yuyong , Ren Zesheng , Peng Zhike
Published: May 2022

Vibration and deformation measurement are of great significance for structural health monitoring (SHM). While the existing accelerometer, laser and vision-based approaches have limitations in multi-point vibration measurement, efficiency and environmental adaptability, especially for large structures. In this paper, we present a method for structural health monitoring with multi-point vibration and deformation measurement via millimetre- wave sensing. First, we describe the mathematical model and the principle of multi-target synchronous vibration and deformation monitoring. Second, with a built prototype of mmWave sensing system, we evaluate our method with both laboratory and field tests. The experimental results of different amplitudes, frequencies and velocities show that our approach can accurately monitor the vibration and deformation of multi-point with μm-level accuracy. The field test of Shanghai Songpu Bridge is provided to further validate the effectiveness of the method, offering a novel approach for contactless vibration and deformation monitoring of large structures in SHM.
Output-only modal analysis of the Humen Bridge from video measurement
Authors: Lin Guanfu , Wang Li , Lu Zhong-Rong
Published: May 2022

Video-based vibration testing is a promising non-contact method for structural health monitoring. This article develops an innovative methodology for estimating the structural modal parameters from videos without recovering the responses of structures. Firstly, an image processing (i.e., Gaussian smoothing) are adopted to smooth frames and alleviate the noises. Secondly, several regions of interest (ROIs) contained the projection area of structure are selected, and edge detection is performed to that to reduce the size of vision data. Then under some assumptions, the reduced vision data can be subjected to the frequency domain decomposition (FDD) similar to the structural modal analysis to identify the modal parameters of structure. Moreover, the structural mode shapes are recovering from such decomposition. The feasibility and accuracy of the proposed method were validated by a field experiment on the Humen Bridge in Guangzhou, China.
A new explanation of Rolling Contact Fatigue in bearing steels based on multiscale models
Authors: Sun Yachao , Cao Hongrui
Published: May 2022

Rolling Contact Fatigue (RCF) is harmful and inevitable to bearings and usually results in the initiation of subsurface damage. The root cause for this damage is the cumulative plastic deformation accentuated by carbides. This paper gives a new explanation of RCF based on multiscale models. The distribution and change law of subsurface shear stress in bearing steels was previously investigated by a finite element model. A two-phase atomic model of bcc-Fe and cementite was built. Ten alternating shear load cycles were designed to explore the mechanisms of the cyclic plastic accumulation when the atomic model was initially in the elastic, elastic- plastic and plastic stages, respectively. The results show that cyclic softening diversely occurs in all three types of stress responses. Severe cyclic shear deformation eventually leads to earlier cyclic softening and stress yield, which might be the micromechanism of plastic accumulation and RCF in bearing steels.
The effect of strain rate on the mechanical property of PDMS and PDMS/graphene: A molecular dynamics study
Authors: Han Tianrui , Sun Yu , Zhao Zhou , Chen Xuefeng
Published: May 2022

Molecular dynamics simulation was used to study the mechanical behavior of polydimethylsiloxane (PDMS) and PDMS/graphene at different strain rates. Firstly, the mechanical behavior of PDMS under uniaxial tension at different strain rates (109 /s-1010 /s) was studied. Secondly, graphene with a mass ratio of 3.62 was added to PDMS to study the mechanical behavior of the mixed system under uniaxial tension at different strain rates (108/s - 1010 /s). The results showed that the Young’s modulus and tensile strength of PDMS were greatly affected by strain rate. After adding graphene into PDMS, the mechanical properties of the blends at different strain rates were almost the same. This work may provide a deeper understanding on mechanical property of PDMS composites.
Microstructure of laser cladding nickel-based superalloy: characterization, finite element modeling, and fatigue analysis
Authors: Zhang Miao , Liu Shujie
Published: May 2022

After laser cladding, the mechanical response of nickel-based single crystal superalloy is more complex when the loading direction is perpendicular to the columnar grain growth direction. In order to understand the deformation at grain level, a representative volume element (RVE) model based on the randomness of columnar grain texture was established for DD6 single crystal alloy, and the finite element model simulation of crystal plasticity (CPFEM) was performed. The results show that the stress and strain responses of the cladding microstructure are related to the grain distribution. The orientation difference between columnar grains in the epitaxial growth microstructure leads to non-uniform deformation and local stress concentration. The greater the crystal orientation difference is, the more obvious the stress concentration phenomenon is. On this basis, the statistical standard deviation of strain component is chosen to represent the fatigue damage of material, and the relationship between the statistical standard deviation of strain component and the external load strain is studied, which has certain reference value for the fatigue damage research of micro-cladding.
Quantitative study of shaking based on non-destructive testing of steel wire ropes
Authors: Guo Zichang , Luo Han , Zhuo Jianguo , Zhang Chengzhuo , Li Xinyue
Published: May 2022

Due to the superiority of wire rope in strength, cost, ductility and other characteristics, wire rope has become an irreplaceable material in metallurgy, mining, oil and gas drilling, machinery, chemical industry, aerospace and other fields. Therefore, the wire rope flaw detection is very important work, and in the wire rope detection, jitter is very serious interference. In this article, we firstly discuss the quantitative relationship between displacement and direction of shaking and leakage magnetic field, and this study can also be used as a essential basis for subsequent quantitative studies of local flaws to remove the effect of shaking on leakage magnetic signal.
A temperature field reconstruction method for debonding defect identification in laminated structures
Authors: Wu T.T. , Zhang C. , Ji H.L. , Tao C.C. , Qiu J.H.
Published: May 2022

The surface temperature is an important parameter capable of detecting and quantifying defects in laminated structures if subjected to a thermal load. In this paper, a temperature field reconstruction method is developed to detect the size of debonding defect in laminated structures. A Tikhonov regularization based conjugate gradient method (CGM) is applied to reconstruct the temperature field of a laminated structure for determination of the size of a debonding defect. Tikhonov regularization deals with the severe ill-posedness and the CGM is used for solving the minimization problem. Some simulation examples are carried out to validate the effectiveness and stability of the inverse analysis. Results show that for small defects, the defect detection results based on temperature field reconstruction have higher accuracy than that detected directly by available surface temperature measurement.