Data Mining-Based Maintenance Management Framework of Multi-component System

Complex repairable system is composed of thousands of components.Some maintenance management and decision problems in maintenance management and decision need to classify a set of components into several classes based on data mining.Furthermore,with the complexity of industrial equipment increasing,the managers should pay more attention to the key components and carry out the lean management is very important.Therefore,the idea"customer segmentation"of"precise marketing"can be used in the maintenance management of the multi-component system.Following the idea of segmentation,the components of multicomponent systems should be subdivied into groups based on specific attributes relevant to maintenance,such as maintenance cost,mean time between failures,and failure frequency.For the target specific groups of parts,the optimal maintenance policy,health assessment and maintenance scheduling can be determined.The proposed analysis framework will be given out.In order to illustrate the effectiveness of this method,a numerical example is given out.

Research on shell-side heat and mass transfer with multi-component in LNG spiral-wound heat exchanger under sloshing conditions

The spiral-wound heat exchanger(SWHE) is the primary low-temperature heat exchanger for large-scale LNG plants due to its high-pressure resistance, compact structure, and high heat exchange efficiency. This paper studied the shell-side heat and mass transfer characteristics of vapor-liquid two-phase mixed refrigerants in an SWHE by combining a multi-component model in FLUENT software with a customized multicomponent mass transfer model. Besides, the mathematical model under the sloshing condition was obtained through mathematical derivation, and the corresponding UDF code was loaded into FLUENT as the momentum source term. The results under the sloshing conditions were compared with the relevant parameters under the steady-state condition. The shell-side heat and mass transfer characteristics of the SWHE were investigated by adjusting the component ratio and other working conditions. It was found that the sloshing conditions enhance the heat transfer performance and sometimes have insignificant effects. The sloshing condition is beneficial to reduce the flow resistance. The comprehensive performance of multi-component refrigerants has been improved and the improvement is more significant under sloshing conditions, considering both the heat transfer and pressure drop.These results will provide theoretical support for the research and design of multi-component heat and mass transfer enhancement of LNG SWHE under ocean sloshing conditions.

Predicting gas-bearing distribution using DNN based on multi-component seismic data: Quality evaluation using structural and fracture factors

The tight-fractured gas reservoir of the Upper Triassic Xujiahe Formation in the Western Sichuan Depression has low porosity and permeability. This study presents a DNN-based method for identifying gas-bearing strata in tight sandstone. First, multi-component composite seismic attributes are obtained.The strong nonlinear relationships between multi-component composite attributes and gas-bearing reservoirs can be constrained through a DNN. Therefore, we identify and predict the gas-bearing strata using a DNN. Then, sample data are fed into the DNN for training and testing. After optimized network parameters are determined by the performance curves and empirical formulas, the best deep learning gas-bearing prediction model is determined. The composite seismic attributes can then be fed into the model to extrapolate the hydrocarbon-bearing characteristics from known drilling areas to the entire region for predicting the gas reservoir distribution. Finally, we assess the proposed method in terms of the structure and fracture characteristics and predict favorable exploration areas for identifying gas reservoirs.

A novel method for clustering cellular data to improve classification

Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.

A New Encryption Mechanism Supporting the Update of Encrypted Data for Secure and Efficient Collaboration in the Cloud Environment

With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud servers vulnerable due to insufficient encryption.This paper introduces a novel mechanism that encrypts data in‘bundle’units,designed to meet the dual requirements of efficiency and security for frequently updated collaborative data.Each bundle includes updated information,allowing only the updated portions to be reencrypted when changes occur.The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes,such as Cipher Block Chaining(CBC)and Counter(CTR),which require decrypting and re-encrypting the entire dataset whenever updates occur.The proposed method leverages update-specific information embedded within data bundles and metadata that maps the relationship between these bundles and the plaintext data.By utilizing this information,the method accurately identifies the modified portions and applies algorithms to selectively re-encrypt only those sections.This approach significantly enhances the efficiency of data updates while maintaining high performance,particularly in large-scale data environments.To validate this approach,we conducted experiments measuring execution time as both the size of the modified data and the total dataset size varied.Results show that the proposed method significantly outperforms CBC and CTR modes in execution speed,with greater performance gains as data size increases.Additionally,our security evaluation confirms that this method provides robust protection against both passive and active attacks.

Impact of ocean data assimilation on the seasonal forecast of the 2014/15 marine heatwave in the Northeast Pacific Ocean

A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study employed two assimilation schemes based on the global Climate Forecast System of Nanjing University of Information Science(NUIST-CFS 1.0)to investigate the impact of ocean data assimilation on the seasonal prediction of this extreme marine heatwave.The sea surface temperature(SST)nudging scheme assimilates SST only,while the deterministic ensemble Kalman filter(EnKF)scheme assimilates observations from the surface to the deep ocean.The latter notably improves the forecasting skill for subsurface temperature anomalies,especially at the depth of 100-300 m(the lower layer),outperforming the SST nudging scheme.It excels in predicting both horizontal and vertical heat transport in the lower layer,contributing to improved forecasts of the lower-layer warming during the Blob.These improvements stem from the assimilation of subsurface observational data,which are important in predicting the upper-ocean conditions.The results suggest that assimilating ocean data with the EnKF scheme significantly enhances the accuracy in predicting subsurface temperature anomalies during the Blob and offers better understanding of its underlying mechanisms.

Synthetic data as an investigative tool in hypertension and renal diseases research

There is a growing body of clinical research on the utility of synthetic data derivatives,an emerging research tool in medicine.In nephrology,clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient privacy.This is especially important given the epidemiology of chronic kidney disease,renal oncology,and hypertension worldwide.However,there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.

Evaluation of ERA5 reanalysis temperature data over the Qilian Mountains of China

Air temperature is an important indicator to analyze climate change in mountainous areas.ERA5 reanalysis air temperature data are important products that were widely used to analyze temperature change in mountainous areas.However,the reliability of ERA5 reanalysis air temperature over the Qilian Mountains(QLM)is unclear.In this study,we evaluated the reliability of ERA5 monthly averaged reanalysis 2 m air temperature data using the observations at 17 meteorological stations in the QLM from 1979 to 2017.The results showed that:ERA5 reanalysis monthly averaged air temperature data have a good applicability in the QLM in general(R2=0.99).ERA5 reanalysis temperature data overestimated the observed temperature in the QLM in general.Root mean square error(RMSE)increases with the increasing of elevation range,showing that the reliability of ERA5 reanalysis temperature data is worse in higher elevation than that in lower altitude.ERA5 reanalysis temperature can capture observational warming rates well.All the smallest warming rates of observational temperature and ERA5 reanalysis temperature are found in winter,with the warming rates of 0.393°C/10a and 0.360°C/10a,respectively.This study will provide a reference for the application of ERA5 reanalysis monthly averaged air temperature data at different elevation ranges in the Qilian Mountains.

Topology Data Analysis-Based Error Detection for Semantic Image Transmission with Incremental Knowledge-Based HARQ

Semantic communication(SemCom)aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy.Nevertheless,semantic communication still suffers from unexpected channel volatility and thus developing a re-transmission mechanism(e.g.,hybrid automatic repeat request[HARQ])becomes indispensable.In that regard,instead of discarding previously transmitted information,the incremental knowledge-based HARQ(IK-HARQ)is deemed as a more effective mechanism that could sufficiently utilize the information semantics.However,considering the possible existence of semantic ambiguity in image transmission,a simple bit-level cyclic redundancy check(CRC)might compromise the performance of IK-HARQ.Therefore,there emerges a strong incentive to revolutionize the CRC mechanism,thus more effectively reaping the benefits of both SemCom and HARQ.In this paper,built on top of swin transformer-based joint source-channel coding(JSCC)and IK-HARQ,we propose a semantic image transmission framework SC-TDA-HARQ.In particular,different from the conventional CRC,we introduce a topological data analysis(TDA)-based error detection method,which capably digs out the inner topological and geometric information of images,to capture semantic information and determine the necessity for re-transmission.Extensive numerical results validate the effectiveness and efficiency of the proposed SC-TDA-HARQ framework,especially under the limited bandwidth condition,and manifest the superiority of TDA-based error detection method in image transmission.

Data driven prediction of fragment velocity distribution under explosive loading conditions

This study presents a machine learning-based method for predicting fragment velocity distribution in warhead fragmentation under explosive loading condition.The fragment resultant velocities are correlated with key design parameters including casing dimensions and detonation positions.The paper details the finite element analysis for fragmentation,the characterizations of the dynamic hardening and fracture models,the generation of comprehensive datasets,and the training of the ANN model.The results show the influence of casing dimensions on fragment velocity distributions,with the tendencies indicating increased resultant velocity with reduced thickness,increased length and diameter.The model's predictive capability is demonstrated through the accurate predictions for both training and testing datasets,showing its potential for the real-time prediction of fragmentation performance.

AVAILABILITY SIMULATION OF MULTI-COMPONENT SYSTEM BASED ON OPPORTUNISTIC MAINTENANCE POLICY

A multi-component system has the long fixed maintenance time, so the opportunistic maintenance policy is adopted to put preventive replacement and corrective replacement together, so that the long fixed maintenance time can be shared by more than one component, and the system availability can be improved. Then, the generation characteristics of the random failure time are researched based on the replacement maintenance and the minima[ maintenance. Furthermore, by choosing the opportunistic replacement ages of each component as opti- mized variables, a simulation algorithm based on an opportunistic maintenance policy is designed to maximize the total availability. Finally, the simulation result shows the validity of the algorithm by an example.

Separation of P. and SV-wavefields from multi-componen seismic data

In multi-component seismic exploration, the horizontal and vertical components both contain P- and SV-waves. The P- and SV-wavefields in a seismic record can be separated by their horizontal and vertical displacements when upgoing P- and SV-waves arrive at the sea floor. If the sea floor P wave velocity, S wave velocity, and density are known, the separation can be achieved in ther-p domain. The separated wavefields are then transformed to the time domain. A method of separating P- and SV-wavefields is presented in this paper and used to effectively separate P- and SV-wavefields in synthetic and real data. The application to real data shows that this method is feasible and effective. It also can be used for free surface data.

Multi-component Dirac equation hierarchy and its multi-component integrable couplings system

A general scheme for generating a multi-component integrable equation hierarchy is proposed. A simple 3M- dimensional loop algebra ~X is produced. By taking advantage of ~X a new isospectral problem is established and then by making use of the Tu scheme the multi-component Dirac equation hierarchy is obtained. Finally, an expanding loop algebra ~FM of the loop algebra ~X is presented. Based on the ~FM, the multi-component integrable coupling system of the multi-component Dirac equation hierarchy is investigated. The method in this paper can be applied to other nonlinear evolution equation hierarchies.

Multi-component AVO response of thin beds based on reflectance spectrum theory

Seismic AVO analysis now is one of the major criteria for recognizing potential hydrocarbon reservoirs. Volume scattering information that carries information of stratigraphic structure, lithology, and pore fluid is more useful for seismic exploration. However, traditional AVO analysis is based on the Zoeppritz equation, which only contains single-interface information. Quantitative interpretation of the thin bed thickness is essential to thin bed structure interpretation, reservoir description, and lateral reservoir prediction. The reflectance spectrum equation based on the elastic wave propagation matrix in the frequency domain derived in this paper shows that both interfaces and intervals have an effect on amplitude. The equation includes information about both single interfaces and volume scattering. Since the reflectance spectrum equation is a continuous function of thin bed thickness and frequency, it is convenient to analyze the effects of a single frequency and bed thickness on the reflectance spectrum. Bed thickness is analyzable until the bed thickness is vanishingly small. These characteristics can＇t be achieved by Fourier transform. The propagation of seismic waves is complex and various wave modes exist simultaneously. The reflectance spectrum includes various propagating wave modes and multiples and is better for simulating multi-component thin bed AVO responses than the ray tracing method.

Layout problem of multi-component systems arising for improving maintainability

To improve the mainlainability design efficiency and quality, a layout optimization method for maintainability of multi-component systems was proposed. The impact of the component layout design on system maintainability was analyzed, and the layout problem for maintainability was presented. It was formulated as an optimization problem, where maintainability, layout space and distance requirement were formulated as objective functions. A multi-objective particle swarm optimization algorithm, in which the constrained-domination relationship and the update strategy of the global best were simply modified, was then used to obtain Pareto optimal solutions for the maintainability layout design problem. Finally, application in oxygen generation system of a spacecraft was studied in detail to illustrate the effectiveness and usefulness of the proposed method. The results show that the concurrent maintainability design can be carried out during the layout design process by solving the layout optimization problem for maintainability.

Multi-scale and multi-component digital core construction and elastic property simulation

The conventional digital core models are usually small in size and have difficulty in representing the complex structures of heterogeneous rocks;Therefore,the parameters of simulated rock physics are difficult to be referenced.In this study,we propose a feasible simulation method for obtaining multi-scale and multi-component digital cores based on three types of sandstone samples.In the proposed method,the plug and subplug samples are scanned via micro-computed tomography at different resolutions.Furthermore,the images are precisely registered using the proposed hybrid image registration method.In case of high-resolution images,the traditional segmentation method is used to segment the cores into pores and minerals.Subsequently,we established the relations between the gray values and the porosity/mineral content in case of the low-resolution images based on the registered domains and the relation curves were applied to the segmentation of the low-resolution images.The core images constitute the multi-scale and multi-component digital core models after segmentation.Further,the elastic properties of the three samples were simulated at both fine and coarse scales based on the multi-scale and multi-component digital core models,and four component models were considered.The results show that the multi-scale and multi-component digital core models can overcome the representative limits of the conventional digital core models and accurately characterize pores and minerals at different scales.The numerical results of the elastic modulus are more representative at large scales,and considerably reliable results can be obtained by appropriately considering the minerals.

Segregation modes,characteristics,and mechanisms of multi-component lignite in a vibrated gas-fluidized bed

The segregation modes and characteristics of 1-6 mm multi-component lignite were studied in a microporous, vibrated, gas-fluidized bed of Φ110 mm ×400 mm. The effects of particle density and size, vibration frequency and amplitude, and gas velocity on these characteristics were considered. The average size, average density, size deviation coefficient, and density deviation coefficient were used to identify lignite size and density. The separation efficiency was adopted to evaluate the segregation performance,and the segregation mechanisms were explored. The results show that ε(size,max) of heterogeneous multisize-component lignite with K_(size) = 65% reaches 80% at f= 20 Hz, A = 5 mm, and N =(1,3). ε_(density,max) Of heterogeneous multi-density-component lignite with K_(density)= 25% reaches 50% at f = 15 Hz, A = 5 mm,and N =(1,1.5). The density segregations of 1-3 and 3-6 mm multi-component mixtures are remarkable,ε_(density,max)= 42% and 31% at f= 14 and 16 Hz, and A = 3 and 5 mm, respectively. The size segregation of 1-6 mm multi-component mixture is prominent and ε_(size,max)= 55% at f= 15 Hz, A = 5 mm. The mediumsized mixture with a narrow size distribution at low frequency is favorable for density segregation,and a mixture with a wider size distribution at high frequency is most favorable for size segregation.Precise control of gas flow and vibration as well as optimal design of the fluidized bed can improve the performance of segregation in the vibrated gas-fluidized bed.

Opportunistic maintenance for multi-component systems considering structural dependence and economic dependence

Although opportunistic maintenance strategies are widely used for multi-component systems, all opportunistic mainte- nance strategies only consider economic dependence and do not take structural dependence into account. An opportunistic main- tenance strategy is presented for a multi-component system that considers both structural dependence and economic dependence. The cost relation and time relation among components based on structural dependence are developed. The maintenance strategy for each component of a multi-component system involves one of five maintenance actions, namely, no-maintenance, a minimal maintenance action, an imperfect maintenance action, a perfect maintenance action, and a replacement action. The maintenance action is determined by the virtual age of the component, the life expectancy of the component, and the age threshold values. Monte Carlo simulation is designed to obtain the optimal oppor- tunistic maintenance strategy of the system over its lifetime. The simulation result reveals that the minimum maintenance cost with a strategy that considers structural dependence is less than that with a strategy that does not consider structural dependence. The availability with a strategy that considers structural dependence is greater than that with a strategy that does not consider structural dependence under the same conditions.

Dynamic probabilistic design technique for multi-component system with multi-failure modes

For unacceptable computational efficiency and accuracy on the probabilistic analysis of multi-component system with multi-failure modes,this paper proposed multi-extremum response surface method(MERSM).MERSM model was established based on quadratic polynomial function by taking extremum response surface model as the sub-model of multi-response surface method.The dynamic probabilistic analysis of an aeroengine turbine blisk with two components,and their reliability of deformation and stress failures was obtained,based on thermal-structural coupling technique,by considering the nonlinearity of material parameters and the transients of gas flow,gas temperature and rotational speed.The results show that the comprehensive reliability of structure is 0.9904 when the allowable deformations and stresses of blade and disk are 4.78×10–3 m and 1.41×109 Pa,and 1.64×10–3 m and 1.04×109 Pa,respectively.Besides,gas temperature and rotating speed severely influence the comprehensive reliability of system.Through the comparison of methods,it is shown that the MERSM holds higher computational precision and speed in the probabilistic analysis of turbine blisk,and MERSM computational precision satisfies the requirement of engineering design.The efforts of this study address the difficulties on transients and multiple models coupling for the dynamic probabilistic analysis of multi-component system with multi-failure modes.

Thermo-Calc based multi-component micro-segregation model and solidification paths calculations

On the basis of a multi-length scale modeling, a mixture-averaged multi-component/multiphase micro- segregation model was proposed without pre-set function for the micro-scale solute profile. The model explains the effect of morphologies of solidifying phases and solid back diffusion （SBD） on segregation, and covers the two limiting solidification cases of Scheil and Lever-rule models. A commercial Thermo-Calc software package/database was linked to the algorithms via its TQ6-interface for instantaneous determination of the related thermodynamic data of the multi-component alloys. The influences of cooling rate and other parameters on the solidification path and micro-segregation behavior were numerically investigated by sample calculation of the ternary AI-Cu-Mg alloys. A parallel experimental investigation on AI-Cu-Si alloys solidified under different cooling conditions was conducted to validate the theoretical model. Reasonable agreements were gained between the predicted solidification paths and the measured results.