Impact Force Localization and Reconstruction via ADMM-based Sparse Regularization Method

In practice,simultaneous impact localization and time history reconstruction can hardly be achieved,due to the illposed and under-determined problems induced by the constrained and harsh measuring conditions.Although l_(1) regularization can be used to obtain sparse solutions,it tends to underestimate solution amplitudes as a biased estimator.To address this issue,a novel impact force identification method with l_(p) regularization is proposed in this paper,using the alternating direction method of multipliers(ADMM).By decomposing the complex primal problem into sub-problems solvable in parallel via proximal operators,ADMM can address the challenge effectively.To mitigate the sensitivity to regularization parameters,an adaptive regularization parameter is derived based on the K-sparsity strategy.Then,an ADMM-based sparse regularization method is developed,which is capable of handling l_(p) regularization with arbitrary p values using adaptively-updated parameters.The effectiveness and performance of the proposed method are validated on an aircraft skin-like composite structure.Additionally,an investigation into the optimal p value for achieving high-accuracy solutions via l_(p) regularization is conducted.It turns out that l_(0.6)regularization consistently yields sparser and more accurate solutions for impact force identification compared to the classic l_(1) regularization method.The impact force identification method proposed in this paper can simultaneously reconstruct impact time history with high accuracy and accurately localize the impact using an under-determined sensor configuration.

Uncertainty-Aware Deep Learning: A Promising Tool for Trustworthy Fault Diagnosis

Recently,intelligent fault diagnosis based on deep learning has been extensively investigated,exhibiting state-of-the-art performance.However,the deep learning model is often not truly trusted by users due to the lack of interpretability of“black box”,which limits its deployment in safety-critical applications.A trusted fault diagnosis system requires that the faults can be accurately diagnosed in most cases,and the human in the deci-sion-making loop can be found to deal with the abnormal situa-tion when the models fail.In this paper,we explore a simplified method for quantifying both aleatoric and epistemic uncertainty in deterministic networks,called SAEU.In SAEU,Multivariate Gaussian distribution is employed in the deep architecture to compensate for the shortcomings of complexity and applicability of Bayesian neural networks.Based on the SAEU,we propose a unified uncertainty-aware deep learning framework(UU-DLF)to realize the grand vision of trustworthy fault diagnosis.Moreover,our UU-DLF effectively embodies the idea of“humans in the loop”,which not only allows for manual intervention in abnor-mal situations of diagnostic models,but also makes correspond-ing improvements on existing models based on traceability analy-sis.Finally,two experiments conducted on the gearbox and aero-engine bevel gears are used to demonstrate the effectiveness of UU-DLF and explore the effective reasons behind.

Time-Varying Mesh Stiffness Calculation and Dynamic Modeling of Spiral Bevel Gear with Spalling Defects

Time-varying mesh stiffness(TVMS)is a vital internal excitation source for the spiral bevel gear(SBG)transmission system.Spalling defect often causes decrease in gear mesh stiffness and changes the dynamic characteristics of the gear system,which further increases noise and vibration.This paper aims to calculate the TVMS and establish dynamic model of SBG with spalling defect.In this study,a novel analytical model based on slice method is proposed to calculate the TVMS of SBG considering spalling defect.Subsequently,the influence of spalling defect on the TVMS is studied through a numerical simulation,and the proposed analytical model is verified by a finite element model.Besides,an 8-degrees-of-freedom dynamic model is established for SBG transmission system.Incorporating the spalling defect into TVMS,the dynamic responses of spalled SBG are analyzed.The numerical results indicate that spalling defect would cause periodic impact in time domain.Finally,an experiment is designed to verify the proposed dynamic model.The experimental results show that the spalling defect makes the response characterized by periodic impact with the rotating frequency of spalled pinion.

Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review

Prognostics and Health Management(PHM),including monitoring,diagnosis,prognosis,and health management,occupies an increasingly important position in reducing costly breakdowns and avoiding catastrophic accidents in modern industry.With the development of artificial intelligence(AI),especially deep learning(DL)approaches,the application of AI-enabled methods to monitor,diagnose and predict potential equipment malfunctions has gone through tremendous progress with verified success in both academia and industry.However,there is still a gap to cover monitoring,diagnosis,and prognosis based on AI-enabled methods,simultaneously,and the importance of an open source community,including open source datasets and codes,has not been fully emphasized.To fill this gap,this paper provides a systematic overview of the current development,common technologies,open source datasets,codes,and challenges of AI-enabled PHM methods from three aspects of monitoring,diagnosis,and prognosis.

基于Rockfall数值模拟的危岩体运动规律分析

危岩体发育过程具有渐进性,失稳过程具有突变性和强致灾性,危岩崩塌灾害是山区公路工程建设及运营过程中普遍存在的突发性重大安全隐患,前期预测和分析危岩体落石运动特征可为危岩体治理提供参考。本文以兴义环高某特大桥桥台上方的危岩体为例,通过现场调查研究,分析危岩特征,采用Rockfall数值软件模拟危岩落石运动轨迹、落石终点水平位置,分析落石速度及其能量大小。结果表明:危岩体W5发生崩塌后,有大部分落石到达桥面,且在到达桥面时仍有较大动能和转动速度,会对桥面造成严重撞击和破坏,威胁和影响行车安全。建议对危岩体进行防护或加固,以保障公路建设运营安全。

Replacing fishmeal with soybean meal affects survival, antioxidant capacity, intestinal microbiota, and mRNA expression of TOR and S6K1 in Macrobrachium rosenbergii

Using alternative plant-derived dietary protein to replace fishmeal,combined with practical evaluation indexes,is a recent focus for aquaculture practices.An 8-week feeding experiment with giant freshwater prawn Macrobrachium rosenbergii post-larvae was conducted to determine the eff ects of replacing fi shmeal(FM)with soybean meal in the feed,in terms of growth performance,antioxidant capacity,intestinal microbiota,and mRNA expression of target of rapamycin(TOR)and ribosomal protein S6 kinase B1(S6K1).Four isonitrogenous diets with isocaloric value were prepared to contain 100%,75%,50%,or 25%FM as the protein source(dietary treatments FM100,FM75,FM50,and FM25,respectively).Each diet was fed to post-larval prawns(mean weight 0.045±0.002 g)twice a day in four replicates.No signifi cant diff erence in weight gain was observed among all groups,but the survival rate of prawns fed the FM50 and FM25 diets was signifi cantly lower than that of prawns fed the FM diet.The mRNA expression of both TOR and S6K1 were the lowest in hepatopancreas of prawns fed the FM25 diet.Superoxide dismutase activity of prawns fed the FM25 diet was significantly lower than that of prawns fed FM50.In contrast,the malondialdehyde content was signifi cantly higher in prawns fed FM25 as compared with those fed FM75.The proportion of fishmeal in the diet did not affect the composition of core(phylum-level)intestinal microbiota,but greater fishmeal replacement with soybean meal had a potential risk to increase the relative abundance of opportunistic pathogens in the gut when considered at the genus level.These results suggest that fishmeal replacement with soybean meal should not exceed 50%in a diet for post-larval M.rosenbergii.

Micromechanical Analysis of Interfacial Debonding in Metal Matrix Composites Subjected to off-axis Loading

This study aims to investigate the effects of interfacial debonding and fiber volume fraction on the stressstrain behavior of the fiber reinforced metal matrix composites subjected to off-axis loading.The generalized method of cells(GMC)is used to analyze a representative element whose fiber shape is circular.The constant compliant interface model(CCI)is also adopted to study the response of composites with imperfect interfacial bonding.Results show that for the composites subjected to off-axis loading,the mechanical behaviors are affected appreciably by the interfacial debonding and the fiber volume fraction.

Preparation of Electrospun PVDF Nanofiber Composite Filter Medium and Its Application in Air Filtration

Nanofibrous media with both high particle interception efficiency and robust air permeability has broad technological applications in areas including individual protection, industrial security, and environmental governance. However, producing such filtration media has proven to be extremely challenging. Here we reported an approach to preparing and fabricating a polyvinylidene fluoride (PVDF) nanofiber composite filter medium composed of 2D PVDF nanofiber nets and a stable substrate via onestep electrospinning for effective air filtration. PVDF nanofibers are obtained by adjusting the electrospinning process. With the combined properties of ultrasmall diameter, high porosity, and a bonded scaffold, the resulting PVDF nanofiber composite filter medium exhibits a robust high filtration efficiency of 99.901%(equivalent to an F9 rating) for 0.4 mm particles and a long service life (a large dust holding capacity of 36 g/m^2) for ultrafine airborne particles based on the sieving principle and surface filtration behavior. The successful synthesis of PVDF nanofibers medium would not only make it a promising candidate for air filtration, but also provide new insights into the design and development of composite nanofiber structures for various applications.

Preliminary Study on the Reinforcing Effect of Nanocellulose on Supercapacitor Membranes

In this study,six types of nanocellulose,as paper strengthening agents,were investigated for their reinforcing effects on a supercapacitor membrane by adding them to a slurry.The results indicated that adding 3%nanocellulose bacterial cellulose(BC)-B could effectively increase the tensile strength of the supercapacitor membrane by 36.5%without changing the pore structure of the membrane.Scanning electron microscopy images revealed that adding polyacrylamide to the supercapacitor membrane caused serious adhesion between fibers and affected the pore structure of the supercapacitor membrane,whereas adding BC-B did not produce similar effects.

Physics-Informed Deep Neural Network for Bearing Prognosis with Multisensory Signals

Prognosis of bearing is critical to improve the safety,reliability,and availability of machinery systems,which provides the health condition assessment and determines how long the machine would work before failure occurs by predicting the remaining useful life(RUL).In order to overcome the drawback of pure data-driven methods and predict RUL accurately,a novel physics-informed deep neural network,named degradation consistency recurrent neural network,is proposed for RUL prediction by integrating the natural degradation knowledge of mechanical components.The degradation is monotonic over the whole life of bearings,which is characterized by temperature signals.To incorporate the knowledge of monotonic degradation,a positive increment recurrence relationship is introduced to keep the monotonicity.Thus,the proposed model is relatively well understood and capable to keep the learning process consistent with physical degradation.The effectiveness and merit of the RUL prediction using the proposed method are demonstrated through vibration signals collected from a set of run-to-failure tests.

Analytical Modeling and Mechanism Analysis of Time-Varying Excitation for Surface Defects in Rolling Element Bearings

Surface defects,including dents,spalls,and cracks,for rolling element bearings are the most common faults in rotating machinery.The accurate model for the time-varying excitation is the basis for the vibration mechanism analysis and fault feature extraction.However,in conventional investigations,this issue is not well and fully addressed from the perspective of theoretical analysis and physical derivation.In this study,an improved analytical model for time-varying displacement excitations(TVDEs)caused by surface defects is theoretically formulated.First and foremost,the physical mechanism for the effect of defect sizes on the physical process of rolling element-defect interaction is revealed.According to the physical interaction mechanism between the rolling element and different types of defects,the relationship between time-varying displacement pulse and defect sizes is further analytically derived.With the obtained time-varying displacement pulse,the dynamic model for the deep groove bearings considering the internal excitation caused by the surface defect is established.The nonlinear vibration responses and fault features induced by surface defects are analyzed using the proposed TVDE model.The results suggest that the presence of surface defects may result in the occurrence of the dual-impulse phenomenon,which can serve as indexes for surface-defect fault diagnosis.

AI-Enabled Monitoring, Diagnosis & Prognosis

The emerging and development of Artificial Intelligence(AI),especially deep learning,has stimulated its application in various engineering domains.Monitoring,diagnosis and prognosis,as the key elements of intelligence maintenance of manufacturing systems in the era of Industry 4.0,has also benefited from the advancement of AI technology.The main objective of this special issue aims at bringing scholars to show their research findings in the field of monitoring,diagnosis and prognosis driven by AI,and promote its application in intelligent maintenance of manufacturing system in China.Ten papers have been selected in this special issue after rigorous review and they represent the latest research outcomes in this active area.

Design and Experiment of 1LFQ-325 Pneumatic Reversible Plough

A pneumatic reversible plough is developed, which complements to the tractor of 25.7-36.8 kW.The plough adopts the cylinder as reversing mechanism between the right and left plough bodies, and the cylinder can substitute the mechanical reversing mechanism. The pneumatic turnover allows the plough to be operated easily and turned over flexibly. Field experiment results show that indicators of plough performance meet the requirements of the relevant national standards.

Hybrid Intelligent Damage Identification of Composite Plate Based on Ensemble Empirical Mode Decomposition and Support Vector Machine

In this paper,a novel method based on strain distribution is presented to determine the presence of damage and its location in composite plate.By building a damage monitoring experimental platform with Fiber Bragg Gratings(FBGs)sensors,impact experiments are made respectively to gain the strain distribution both in heath and damage state.EEMD is used to process the data and IMFs energy feature is evaluated.Then,support vector machine is applied to identify the damage and the testing classification accuracy reaches 92.86%.Finally,by using the influence of the damage position and the propagation path on energy,the damage location is predicted.The experimental results indicate that the proposed method can accurately identify the presence and position of damage.The effectiveness and reliability of the proposed method is verified.

Semi-synthetic chondroitin sulfate CS-semi5 upregulates miR-122-5p,conferring a therapeutic effect on osteoarthritis via the p38/MMP13 pathway

Osteoarthritis(OA)is an aging-associated disease characterized by joint stiffness pain and destroyed articular cartilage.Traditional treatments for OA are limited to alleviating various OA symptoms.There is a lack of drugs available in clinical practice that can truly repair cartilage damage.Here,we developed the chondroitin sulfate analog CS-semi5,semi-synthesized from chondroitin sulfate A.In vivo,CS-semi5 alleviated inflammation,provided analgesic effects,and protected cartilage in the modified Hulth OA rat model and papain-induced OA rat model.A bioinformatics analysis was performed on samples from OA patients and an exosome analysis on papain-induced OA rats,revealing miR-122-5p as the key regulator associated with CS-semi5 in OA treatment.Binding prediction revealed that miR-122-5p acted on the 30-untranslated region of p38 mitogen-activated protein kinase,which was related to MMP13 regulation.Subsequent in vitro experiments revealed that CS-semi5 effectively reduced cartilage degeneration and maintained matrix homeostasis by inhibiting matrix breakdown through the miR-122-5p/p38/MMP13 axis,which was further validated in the articular cartilage of OA rats.This is the first study to investigate the semi-synthesized chondroitin sulfate CS-semi5,revealing its cartilageprotecting,anti-inflammatory,and analgesic properties that show promising therapeutic effects in OA via the miR-122-5p/p38/MMP13 pathway.

Di-and tri-methylation of histone H3K36 play distinct roles in DNA double-strand break repair

Histone H3 Lys36(H3K36)methylation and its associated modifiers are crucial for DNA double-strand break(DSB)repair,but the mechanism governing whether and how different H3K36 methylation forms impact repair pathways is unclear.Here,we unveil the distinct roles of H3K36 dimethylation(H3K36me2)and H3K36 trimethylation(H3K36me3)in DSB repair via non-homologous end joining(NHEJ)or homologous recombination(HR).Yeast cells lacking H3K36me2 or H3K36me3 exhibit reduced NHEJ or HR efficiency.y Ku70 and Rfa1 bind H3K36me2-or H3K36me3-modified peptides and chromatin,respectively.Disrupting these interactions impairs y Ku70 and Rfa1 recruitment to damaged H3K36me2-or H3K36me3-rich loci,increasing DNA damage sensitivity and decreasing repair efficiency.Conversely,H3K36me2-enriched intergenic regions and H3K36me3-enriched gene bodies independently recruit y Ku70 or Rfa1 under DSB stress.Importantly,human KU70 and RPA1,the homologs of y Ku70 and Rfa1,exclusively associate with H3K36me2 and H3K36me3 in a conserved manner.These findings provide valuable insights into how H3K36me2 and H3K36me3 regulate distinct DSB repair pathways,highlighting H3K36 methylation as a critical element in the choice of DSB repair pathway.

Non-contact air-coupled transducers Lamb wave signal multipath effect and blind separation for different modes using PZT as receiver

Due to the complexity and faintness of the detection wave patterns obtained by aircoupled transducers,if it is possible to effectively separate the various modes and obtain nondispersive signals for more accurate detection and positioning,it will help to improve the accuracy and reliability of air-coupled ultrasonic Lamb wave detection,providing better technical support for the application and development of related fields.Because of the increased complexity of aircoupled signals,there is no definite theoretical formula to describe the mode changes of aircoupled signals,so the method based on blind separation has unique value.To address these challenges,the paper proposes a single-channel blind source separation(SCBSS)method.The effectiveness of this method is evaluated through simulations and experiments,demonstrating favorable separation results and efficient computational speed.This work first conducts an in-depth analysis of the signal characteristics of air-coupled ultrasonic non-destructive testing,and simulates the ultrasonic excitation conditions of air-coupled sensors through finite element software.The study of modal changes and multipath effects caused by the variation of the incidence angle of the ACT signal is carried out,and the situation of the Lamb wave signal excited by ACT at the receiving end is analyzed.By combining ACT with PZT signals,the ultrasonic signals of air-coupled Lamb waves are compared and studied,and their modal purification is carried out.

Surrounding rock classification from onsite images with deep transfer learning based on EfficientNet

This paper proposes an accurate,efficient and explainable method for the classification of the surrounding rock based on a convolutional neural network(CNN).The state-of-the-art robust CNN model(EfficientNet)is applied to tunnel wall image recognition.Gaussian filtering,data augmentation and other data pre-processing techniques are used to improve the data quality and quantity.Combined with transfer learning,the generality,accuracy and efficiency of the deep learning(DL)model are further improved,and finally we achieve 89.96%accuracy.Compared with other state-of-the-art CNN architectures,such as ResNet and Inception-ResNet-V2(IRV2),the presented deep transfer learning model is more stable,accurate and efficient.To reveal the rock classification mechanism of the proposed model,Gradient-weight Class Activation Map(Grad-CAM)visualizations are integrated into the model to enable its explainability and accountability.The developed deep transfer learning model has been applied to support the tunneling of the Xingyi City Bypass in the high mountain area of Guizhou,China,with great results.

Effect of long-acting polyethylene glycol recombinant human growth hormone dosages in pediatric practice:A pooled analysis of two phase IV randomized controlled trials

To the Editor:Growth hormone deficiency(GHD)impairs growth and development,affecting approximately 1 in 4000 children worldwide.[1]Recombinant human growth hormone(rhGH)has been standard practice since 1985.[2]However,compliance has been challenged by the need for frequent injections.Polyethylene glycol rhGH(PEG-rhGH)was developed,improving protein stability and extending half-life.Some studies suggested that a weekly dosage of 0.20 mg/kg of PEG-rhGH offers improved growth rates and increased insulin-like growth factor-1 standard deviation score(IGF-1 SDS)without novel toxicities.[3]Here,we present a pooled analysis of two phase IV trials evaluating the efficacy and safety of various PEG-rhGH dosages in GHD.

Basic research on machinery fault diagnostics： Past, present, and future trends

Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R＆D trends in the basic research field of machinery fault diagnosis in terms of four main aspects： Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.