Stiffness Degradation Modeling for Composite Wind Turbine Blades Based on Full-Scale Fatigue Testing

In order to provide more insights into the damage propagation composite wind turbine blades(blade)under cyclic fatigue loading,a stiffness degradation model for blade is proposed based on the full-scale fatigue testing of a blade.A novel non-linear fatigue damage accumulation model is proposed using the damage assessment theories of composite laminates for the first time.Then,a stiffness degradation model is established based on the correlation of fatigue damage and residual stiffness of the composite laminates.Finally,a stiffness degradation model for the blade is presented based on the full-scale fatigue testing.The scientific rationale of the proposed stiffness model of blade is verified by using full-scale fatigue test data of blade with a total length of 52.5 m.The results indicate that the proposed stiffness degradation model of the blade agrees well with the fatigue testing results of this blade.This work provides a basis for evaluating the fatigue damage and lifetime of blade under cyclic fatigue loading.

Remaining lifetime prediction for nonlinear degradation device with random effect

For the large number of nonlinear degradation devices existing in a project, the existing methods have not systematically studied the effects of random effect on the remaining lifetime(RL),the accuracy and efficiency of the parameters estimation are not high, and the current degradation state of the target device is not accurately estimated. In this paper, a nonlinear Wiener degradation model with random effect is proposed and the corresponding probability density function(PDF) of the first hitting time(FHT)is deduced. A parameter estimation method based on modified expectation maximum(EM) algorithm is proposed to obtain the estimated value of fixed coefficient and the priori value of random coefficient in the model. The posterior value of the random coefficient and the current degradation state of target device are updated synchronously by the state space model(SSM) and the Kalman filter algorithm. The PDF of RL with random effect is deduced. A simulation example is analyzed to verify that the proposed method has the obvious advantage over the existing methods in parameter estimation error and RL prediction accuracy.

Strength Degradation of Wood Members Based on the Correlation of Natural and Accelerated Decay Experiments

An accelerated decay test and a natural decay test were conducted synchronically to explore the strength degradation of decaying wood members under long-term exposure to natural environment.A natural decay test was carried out to measure the bending strength,compressive strength parallel to grain and modulus of elasticity of the wood members,with 6 groups of specimens decayed in natural environment for 3 to 18 months respectively.To compare with corresponding decay test,in which 6 other groups of specimens were measured under accelerated conditions.The experimental data collected were evaluated by Pearson productmoment for the correlation.The results indicate that the mechanical properties of the accelerated decay were highly correlated with those in natural environment,both of which decreased in the same trend.Under the given test conditions,the mean value of the accelerated decay test data were curve-fitted to achieve the time-dependent degradation model of the bending strength,the compressive strength parallel to grain,as well as the modulus of elasticity.Due to the high correlation,the acceleration shift factors(ASF)of the two tests were derived,where the bending strength of 2.934,the compressive strength parallel to grain of 2.519 and the elastic modulus of 2.346 were employed to formulate the strength degradation models in the long-term natural environment.The results verify that the exponential function
σ=σ0e^(-βt)enables to exactly capture the degradation of the mechanical properties of wood members decayed in natural environment.

Validation methodology for distribution-based degradation model

Distribution-based degradation models （or graphical approach in some literature） occur in a wide range of applications. However, few of existing methods have taken the validation of the built model into consideration. A validation methodology for distribution-based models is proposed in this paper. Since the model can be expressed as consisting of assumptions of model structures and embedded model parameters, the proposed methodology carries out the validation from these two aspects. By using appropriate statistical techniques, the rationality of degradation distributions, suitability of fitted models and validity of degradation models are validated respectively. A new statistical technique based on control limits is also proposed, which can be implemented in the validation of degradation models＇ validity. The case study on degradation modeling of an actual accelerometer shows that the proposed methodology is an effective solution to the validation problem of distribution-based de qradation models.

Application of a Novel Method for Machine Performance Degradation Assessment Based on Gaussian Mixture Model and Logistic Regression

The currently prevalent machine performance degradation assessment techniques involve estimating a machine＇s current condition based upon the recognition of indications of failure features,which entail complete data collected in different conditions.However,failure data are always hard to acquire,thus making those techniques hard to be applied.In this paper,a novel method which does not need failure history data is introduced.Wavelet packet decomposition（WPD） is used to extract features from raw signals,principal component analysis（PCA） is utilized to reduce feature dimensions,and Gaussian mixture model（GMM） is then applied to approximate the feature space distributions.Single-channel confidence value（SCV） is calculated by the overlap between GMM of the monitoring condition and that of the normal condition,which can indicate the performance of single-channel.Furthermore,multi-channel confidence value（MCV）,which can be deemed as the overall performance index of multi-channel,is calculated via logistic regression（LR） and that the task of decision-level sensor fusion is also completed.Both SCV and MCV can serve as the basis on which proactive maintenance measures can be taken,thus preventing machine breakdown.The method has been adopted to assess the performance of the turbine of a centrifugal compressor in a factory of Petro-China,and the result shows that it can effectively complete this task.The proposed method has engineering significance for machine performance degradation assessment.

Methods for Reliability Assessment under Irregular Time-Varying-Stress Degradation Testing

Degradation tests are often used to assess the reliability of products with long failure-time or few test units. Much of the previous work on reliability assessment methods has focused on constant-stress degradation test( CSDT) and accelerated degradation test( ADT), mainly under the constant, step or progressive stresses. However,in actual testing environments,some stresses are difficult to control and vary with time irregularly,which are quite different from the three stresses mentioned above. In this paper a new approach was presented for reliability assessment with degradation data under irregular time-varying-stress( ITVS).Firstly,the conventional degradation path modeling method was improved by taking into account the influences of the variable stress on the degradation variable. Then,an example was conducted to show the effectiveness of our improved model.

Prognostics and Remaining Useful Life Prediction of Machinery:Advances,Opportunities,and Challenges

As the fundamental and key technique to ensure the safe and reliable operation of vital systems,prognostics with an emphasis on the remaining useful life(RUL)prediction has attracted great attention in the last decades.In this paper,we briefly discuss the general idea and advances of various prognostics and RUL prediction methods for machinery,mainly including data-driven methods,physics-based methods,hybrid methods,etc.Based on the observations fromthe state of the art,we provide comprehensive discussions on the possible opportunities and challenges of prognostics and RUL prediction of machinery so as to steer the future development.

Multi-objective structural optimization and degradation model of magnesium alloy ureteral stent

Background:Mg alloys have attractive properties,including biocompatibility,biodegradability,and ideal mechanical properties.Moreover,Mg alloys are regarded as one of the promising candidates for manufacturing ureteral stents.This study proposed a multi-objective optimization method based on the Kriging surrogate model,NSGA-III,and finite element analysis to improve the degradation performance of Mg alloy ureteral stents.Methods:The finite element model for the degradation of Mg alloy ureteral stents has been established to compare the degradation performance of the stents under different parameters.Latin hypercube sampling was adopted to generate train sample points in the design space.Meanwhile,the Kriging surrogate model was constructed between strut parameters and stent degradation behavior.The NSGA-III was utilized to determine the optimal solution in the global design space.Results:The optimized stent achieved 5.52degradation uniformity(M),10degradation time(DT),and 4work time(FT).The errors between the Kriging surrogate model and the finite element calculation results were less than 6%.Conclusion:The optimized stent achieved better degradation performance.The degradation behavior of stents was dependent on the design parameters.The multi-objective optimization method based on the Kriging surrogate model and finite element analysis was effective in stent design optimization problems.

Research on the Application of Super Resolution Reconstruction Algorithm for Underwater Image

Underwater imaging is widely used in ocean,river and lake exploration,but it is affected by properties of water and the optics.In order to solve the lower-resolution underwater image formed by the influence of water and light,the image super-resolution reconstruction technique is applied to the underwater image processing.This paper addresses the problem of generating super-resolution underwater images by convolutional neural network framework technology.We research the degradation model of underwater images,and analyze the lower-resolution factors of underwater images in different situations,and compare different traditional super-resolution image reconstruction algorithms.We further show that the algorithm of super-resolution using deep convolution networks(SRCNN)which applied to super-resolution underwater images achieves good results.

深水全电采油树控制系统可靠性评估方法

The subsea all-electric Christmas tree(XT) is a key equipment in subsea production systems.Once it fails,the marine environment will be seriously polluted.Therefore,strict reliability analysis and measures to improve reliability must be performed before a subsea all-electric XT is launched;such measures are crucial to subsea safe production.A fault-tolerant control system was developed in this paper to improve the reliability of XT.A dual-factor degradation model for electrical control system components was proposed to improve the evaluation accuracy,and the reliability of the control system was analyzed based on the Markov model.The influences of the common cause failure and the failure rate in key components on the reliability and availability of the control system were studied.The impacts of mean time to repair and incomplete repair strategy on the availability of the control system were also investigated.Research results show the key factors that affect system reliability,and a specific method to improve the reliability and availability of the control system was given.This reliability analysis method for the control system could be applied to general all-electric subsea control systems to guide their safe production.

Prognostics of radiation power degradation lifetime for ultraviolet light-emitting diodes using stochastic data-driven models

With their advantages of high efficiency,long lifetime,compact size and being free of mercury,ultraviolet light-emitting diodes(UV LEDs)are widely applied in disinfection and purification,photolithography,curing and biomedical devices.However,it is challenging to assess the reliability of UV LEDs based on the traditional life test or even the accelerated life test.In this paper,radiation power degradation modeling is proposed to estimate the lifetime of UV LEDs under both constant stress and step stress degradation tests.Stochastic data-driven predic-tions with both Gamma process and Wiener process methods are implemented,and the degradation mechanisms occurring under different aging conditions are also analyzed.The results show that,compared to least squares regression in the IESNA TM-21 industry standard recommended by the Illuminating Engineering Society of North America(IESNA),the proposed stochastic data-driven methods can predict the lifetime with high accuracy and narrow confidence intervals,which confirms that they provide more reliable information than the IESNA TM-21 standard with greater robustness.

Research of Step-down Stress Accelerated Degradation Data Assessment Method of a Certain Type of Missile Tank

The performance degradation rates of the missile tank are generally time-varying functions uneasily evaluated by general classical evaluation methods. This paper develops a segmented nonlinear accelerated degradation model （SNADM） based on the equivalent method of accumulative damage theory, which tackles the problem that product life is difficult to be determined with degradation rate being a function of the variable of time. A segmented expression of the function of population accumulative degradation is derived. And combined with nonlinear function, an accelerated degradation function, i.e., SNADM is obtained. The parameters of the SNADM are identified by numerical iteration, and the statistical function of degradation track is extrapolated. The reliability function is determined through the type of random process of the degradation distribution. Then an evaluation of product storage life is undertaken by combining the statistical function of degradation track, reliability function and threshold. An example of a missile tank undergoes a step-down stress accelerated degradation test （SDSADT）, in which the results with the SNADM and the classical method are evaluated and compared. The technology introduced is validated with the resultant coincidence of both evaluated and field storage lives.

Kinetics of nitrobenzene degradation coupled to indigenous microorganism dissimilatory iron reduction stimulated by emulsified vegetable oil

Widespread contamination by nitrobenzene（NB） in sediments and groundwater requires better understanding of the biogeochemical removal process of the pollutant. NB degradation, coupled with dissimilatory iron reduction, is one of the most efficient pollutant removal methods. However, research on NB degradation coupled to indigenous microorganism dissimilatory iron reduction stimulated by electron donors is still experimental. A model for remediation in an actual polluted site does not currently exist.Therefore, in this study, the dynamics was derived from the Michaelis–Menten model（when the mass ratio of emulsified vegetable oil and NB reached the critical value 91：1）. The effect of SO4^（2-）, NO3^-, Ca^（2＋）/Mg^（2＋）, and the grain size of aquifer media on the dynamics were studied, and the NB degradation dynamic model was then modified based on the most significant factors. Utilizing the model, the remediation time could be calculated in a contaminated site.

Objective Bayesian analysis for the accelerated degradation model usingWiener process with measurement errors

The Wiener process as a degradation model plays an important role in the degradation analysis.In this paper, we propose an objective Bayesian analysis for an acceleration degradation Wienermodel which is subjected to measurement errors. The Jeffreys prior and reference priors underdifferent group orderings are first derived, the propriety of the posteriors is then validated. It isshown that two of the reference priors can yield proper posteriors while the others cannot. A simulation study is carried out to investigate the frequentist performance of the approach comparedto the maximum likelihood method. Finally, the approach is applied to analyse a real data.

Effect of particle degradation on electrostatic sensor measurements and flow characteristics in dilute pneumatic conveying

Vigorous particle collisions and mechanical processes occurring during high-velocity pneumatic con- veying often lead to particle degradation. The resulting particle size reduction and particle number increase will impact on the flow characteristics, and subsequently affect the electrostatic type of flow measurements. This study investigates this phenomenon using both experimental and numerical meth- ods. Particle degradation was induced experimentally by recursively conveying the fillite material within a pneumatic pipeline. The associated particle size reduction was monitored. Three electrostatic sensors were embedded along the pipeline to monitor the flow. The results indicated a decreasing trend in the electrostatic sensor outputs with decreasing particle size, which suggested the attenuation of the flow velocity fluctuation. This trend was more apparent at higher conveying velocities, which suggested that more severe particle degradation occurred under these conditions. Coupled computational fluid dynamics and discrete element methods （CFD-DEM） analysis was used to qualitatively validate these experimental results. The numerical results suggested that smaller particles exhibited lower flow velocity fluctua- tions, which was consistent with the observed experimental results. These findings provide important information for the accurate aoolication of electrostatic measurement devices in oneumatic conveyors.

Degradation modeling of degradable copolymers for biomimetic scaffolds

Biomimetic scaffolds provide a suitable growth environment for tissue engineering and demonstrate good potential for application in biomedical fields.Different-sized copolymerized biomimetic scaffolds degrade differently,and the degradation rate is affected by the copolymerization ratio.The study of the degradation property is the foundational research necessary for realizing individualized biomimetic scaffold design.The degradation performance of polyesters with different copolymerization ratios has been widely reported;however,the modeling of this performance has been rarely reported.In this research,the degradation of copolymers was studied with multi-scale modeling,in which the copolymers were dispersed in a cellular manner,the chain break time was simulated,and the chain selection was based on the Monte Carlo(MC)algorithm.The probability model of the copolymer's chain break position was established as a//roulette,/model,whose probability values were estimated by the calculation of the potential energy difference at different chain break positions by molecular dynamics that determined the position of chain shear,thereby fully realizing the simulation of the chain micro-break process.The diffusion of the oligomers was then calculated using the macro diffusion equation,and the degradation process of the copolymer was simulated by three-scale coupling calculations.The calculation results were in good agreement with the experimental data,demonstrating the effectiveness of the proposed method.

An improved HCI degradation model for a VLSI MOSFET

An improved hot carrier injection （HCI） degradation model was proposed based on interface trap generation and oxide charge injection theory. It was evident that the degradation behavior of electric parameters such as Idlin, Idsat, Gm and Vt fitted well with this model. Devices were prepared with 0.35μm technology and different LDD processes. Idlin and Idsat after HCI stress were analyzed with the improved model. The effects of interface trap generation and oxide charge injection on device degradation were extracted, and the charge injection site could be obtained by this method. The work provides important information to device designers and process engineers.

Validation Metric of Degradation Model with Dynamic Performance

With more and more attention on degradation process, we need the degradation model to be accurate all over the time rather than only at some specific moments. However, the traditional validation metric only estimates difference of static features. A validation method proposed in this paper uses hypothesis testing to identify whether the distributions of experimental measurements and simulation results are consistent. Then, based on the deviation between sample means, a global validation metric which reflects the difference of degradation process between computational model and physical system all over the service time is derived from the statistics of deviation between sample means. Furthermore, curve fit method for discrete experimental measurements is introduced. The case of electro-hydraulic servo valve is studied, and the results show that the proposed validation metric is appropriate for the validation of degradation model with dynamic performance output.

Predicting localised corrosion and mechanical performance of a PEO surface modified rare earth magnesium alloy for implant use through in-silico modelling

In this study,the influence of a plasma electrolytic oxidation(PEO)surface treatment on a medical-grade WE43-based magnesium alloy is examined through an experimental and computational framework that considers the effects of localised corrosion features and mechanical properties throughout the corrosion process.First,a comprehensive in-vitro immersion study was performed on WE43-based tensile specimens with and without PEO surface modification,which included fully automated spatial reconstruction of the phenomenological features of corrosion through micro-CT scanning,followed by uniaxial tensile testing.Then the experimental data of both unmodified and PEO-modified groups were used to calibrate parameters of a finite element-based surface corrosion model.In-vitro,it was found that the WE43-PEO modified group had a significantly lower corrosion rate and maintained significantly higher mechanical properties than the unmodified.While corrosion rates were~50%lower in the WE43-PEO modified specimens,the local geometric features of corroding surfaces remained similar to the unmodified WE43 group,however evolving after almost the double amount of time.We were also able to quantitatively demonstrate that the PEO surface treatment on magnesium continued to protect samples from corrosion throughout the entire period tested,and not just in the early stages of corrosion.Using the results from the testing framework,the model parameters of the surface-based corrosion model were identified for both groups.This enabled,for the first time,in-silico prediction of the physical features of corrosion and the mechanical performance of both unmodified and PEO modified magnesium specimens.This simulation framework can enable future in-silico design and optimisation of bioabsorbable magnesium devices for load-bearing medical applications.