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Cloud Masking is one of the most essential products for satellite remote sensing and downstream applications. This study develops machine learning-based (ML-based) cloud detection algorithms using spectral observations for the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite. Collocated active observations from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used to provide reference labels for model development and validation. We introduce both daytime and nighttime algorithms that differ according to whether solar band observations are included, and the artificial neural network (ANN) and random forest (RF) techniques are adopted for comparison. To eliminate the influences of surface conditions on cloud detection, we introduce three models with different treatments of the surface. Instead of developing independent ML-based algorithms, we add surface variables in a binary way that enhances the ML-based algorithm accuracy by ~5%. Validated against CALIOP observations, we find that our daytime RF-based algorithm outperforms the AHI operational algorithm by improving the accuracy of cloudy pixel detection by ~5%, while at the same time, reducing misjudgment by ~3%. The nighttime model with only infrared observations is also slightly better than the AHI operational product but may tend to overestimate cloudy pixels. Overall, our ML-based algorithms can serve as a reliable method to provide cloud mask results for both daytime and nighttime AHI observations. We furthermore suggest treating the surface with a set of independent variables for future ML-based algorithm development.

PurposeGas pipelines are facing serious risk because of the factors such as long service life, complex working condition and most importantly, corrosion. As one of the main failure reasons of gas pipeline, corrosion poses a great threat to its stable operation. Therefore, it is necessary to analyze the reliability of gas pipelines with corrosion defect. This paper uses the corresponding methods to predict the residual strength and residual life of pipelines.Design/methodology/approachIn this paper, ASME-B31G revised criteria and finite element numerical analysis software are used to analyze the reliability of a special dangerous section of a gas gathering pipeline, and the failure pressure and stress concentration of the pipeline under three failure criteria are obtained. Furthermore, combined with the predicted corrosion rate of the pipeline, the residual service life of the pipeline is calculated.FindingsThis paper verifies the feasibility of ASME-B31G revised criteria and finite element numerical analysis methods for reliability analysis of gas pipelines with corrosion defect. According to the calculation results, the maximum safe internal pressure of the pipeline is 9.53 Mpa, and the residual life of the pipeline under the current operating pressure is 38.41 years, meeting the requirements of safe and reliable operation.Originality/valueThe analysis methods and analysis results provide reference basis for the reliability analysis of corroded pipelines, which is of great practical engineering value for the safe and stable operation of natural gas pipelines.

A complex and varied terrain has a great impact on the distribution of wind energy resources, resulting in uncertainty in accurately assessing wind energy resources. In this study, three wind speed distributions of kernel, Weibull, and Rayleigh type for estimating average wind power density were first compared by using meteorological tower data from 2018 to 2020 under varied desert steppe terrain contexts in northern China. Then three key parameters of scale factor (c) and shape factor (k) from the Weibull model and surface roughness (z0) were investigated for estimating wind energy resource. The results show that the Weibull distribution is the most suitable wind speed distribution over that terrain. The scale factor (c) in the Weibull distribution model increases with an increase in height, exhibiting an obvious form of power function. While there were two different forms for the relationship between the shape factor (k) and height: i.e., the reciprocal of the quadratic function and the logarithmic function, respectively. The estimated roughness length (z0) varied with the withering period, the growing period, and the lush period, which can be represented by the estimated median value in each period. The maximum and minimum values of surface roughness length over the whole period are 0.15 m and 0.12 m, respectively. The power-law model and the logarithmic model are used to estimate the average power density values at six specific heights, which show greater differences in autumn and winter, and smaller differences in spring and summer. The gradient of the increase in average power density values with height is largest in autumn and winter, and smallest in spring and summer. Our findings suggest that dynamic changes in three key parameters (c, k, and z0) should be accurately considered for estimating wind energy resources under varied desert steppe terrain contexts.

Accurate estimation of the spatiotemporal variations of solar radiation is crucial for assessing and utilizing solar energy, one of the fastest‐growing and most important clean and renewable resources. Based on observations from 2,379 meteorological stations along with scare solar radiation observations, the random forest (RF) model is employed to construct a high‐density network of daily global solar radiation (DGSR) and its spatiotemporal variations in China. The RF‐estimated DGSR is in good agreement with site observations across China, with an overall correlation coefficient (R) of 0.95, root‐mean‐square error of 2.34 MJ/m2, and mean bias of −0.04 MJ/m2. The geographical distributions of R values, root‐mean‐square error, and mean bias values indicate that the RF model has high predictive performance in estimating DGSR under different climatic and geographic conditions across China. The RF model further reveals that daily sunshine duration, daily maximum land surface temperature, and day of year play dominant roles in determining DGSR across China. In addition, compared with other models, the RF model exhibits a more accurate estimation performance for DGSR. Using the RF model framework at the national scale allows the establishment of a high‐resolution DGSR network, which can not only be used to effectively evaluate the long‐term change in solar radiation but also serve as a potential resource to rationally and continually utilize solar energy.

Evaluation of the cloud seeding effect is a challenge due to lack of directly physical observational evidence. In this study, an approach for directly observing the cloud seeding effect is proposed using a 1548 nm coherent Doppler wind lidar (CDWL). Normalized skewness was employed to identify the components of the reflectivity spectrum. The spectrum detection capability of a CDWL was verified by a 24.23-GHz Micro Rain Radar (MRR) in Hefei, China (117°15′ E, 31°50′ N), and different types of lidar spectra were detected and separated, including aerosol, turbulence, cloud droplet, and precipitation. Spectrum analysis was applied as a field experiment performed in Inner Mongolia, China (112°39′ E, 42°21′ N ) to support the cloud seeding operation for the 70th anniversary of China’s national day. The CDWL can monitor the cloud motion and provide windshear and turbulence information ensuring operation safety. The cloud-precipitation process is detected by the CDWL, microwave radiometer (MWR) and Advanced Geosynchronous Radiation Imager (AGRI) in FY4A satellites. In particular, the spectrum width and skewness of seeded cloud show a two-layer structure, which reflects cloud component changes, and it is possibly related to cloud seeding effects. Multi-component spectra are separated into four clusters, which are well distinguished by spectrum width and vertical velocity. In general, our findings provide new evidence that the reflectivity spectrum of CDWL has potential for assessing cloud seeding effects.

Degradation observations of modern engineering systems, such as manufacturing systems, turbine engines, and high-speed trains, often demonstrate various patterns of time-varying degradation rates. General degradation process models are mainly introduced for constant degradation rates, which cannot be used for time-varying situations. Moreover, the issue of sparse degradation observations and the problem of evolving degradation observations both are practical challenges for the degradation analysis of modern engineering systems. In this paper, parametric inverse Gaussian process models are proposed to model degradation processes with constant, monotonic, and S-shaped degradation rates, where physical meaning of model parameters for time-varying degradation rates is highlighted. Random effects are incorporated into the degradation process models to model the unit-to-unit variability within product population. A general Bayesian framework is extended to deal with the degradation analysis of sparse degradation observations and evolving observations. An illustrative example derived from the reliability analysis of a heavy-duty machine tool's spindle system is presented, which is characterized as the degradation analysis of sparse degradation observations and evolving observations under time-varying degradation rates.

A unified ductility criterion for fatigue–creep life prediction is presented based on the static fracture toughness exhaustion and dissipated cyclic strain energy density of high temperature components. It provides a general failure criterion for both low and high cycle fatigue regimes. The effects of mean stress, creep and loading waveform on fatigue life are incorporated into this criterion. Applicability and prediction accuracy of the newly proposed criterion was validated through comparing model predictions to experimental results taken from the literature. The results show that the proposed criterion is robust for different loading conditions and more accurate than other existing strain energy/ductility-based methods.

The lathes are basic machine tools for manufacturing cylindrical parts. In recent years, the DLseries computer numerical control (CNC) heavy-duty horizontal lathes (HDHLs) have been widely used in the transportation, energy and aviation industries. High availability of the CNC heavy-duty lathes is demanded to guarantee the efficiency and benefit of these manufacturing industries. As one of the key subsystems of the HDHLs, the feeding control system is studied in this paper on reliability modeling and reliability analysis. The fault tree analysis (FTA) method is used for reliability modelling of the feeding control system. Considering the multiple common cause failure groups (CCFGs) existing in the system, a modified beta factor parametric model is introduced to model the common cause failure (CCF) in system. The reliability of feeding control system is then obtained and the effect of CCF on the reliability of the whole system is studied as well.

The appearance of macro-engineering and mega-project have led to the increasing complexity of modern electromechanical systems (EMSs). The complexity of the system structure and failure mechanism makes it more difficult for reliability assessment of these systems. Uncertainty, dynamic and nonlinearity characteristics always exist in engineering systems due to the complexity introduced by the changing environments, lack of data and random interference. This paper presents a comprehensive study on the reliability assessment of complex systems. In view of the dynamic characteristics within the system, it makes use of the advantages of the dynamic fault tree (DFT) for characterizing system behaviors. The lifetime of system units can be expressed as bounded closed intervals by incorporating field failures, test data and design expertize. Then the coefficient of variation (COV) method is employed to estimate the parameters of life distributions. An extended probability-box (P-Box) is proposed to convey the present of epistemic uncertainty induced by the incomplete information about the data. By mapping the DFT into an equivalent Bayesian network (BN), relevant reliability parameters and indexes have been calculated. Furthermore, the Monte Carlo (MC) simulation method is utilized to compute the DFT model with consideration of system replacement policy. The results show that this integrated approach is more flexible and effective for assessing the reliability of complex dynamic systems.

In order to arrange maintenance resources according to system condition, the lead time needs to be considered within the context of condition-based maintenance (CBM). Therefore, a scheduling threshold is introduced to replace the time to schedule, which is used as a decision variable in combination with a maintenance threshold and a failure threshold. The long-run expected cost rate for maintenance considers the maintenance cost, the cost of the waiting time of suppliers and customers. In this way, suppliers can schedule maintenance services in advance when the system condition reaches the scheduling threshold, and perform maintenance when the system condition exceeds the maintenance threshold. Furthermore, the optimal maintenance plan is updated dynamically in the framework of Prognostics and Health Management (PHM). Finally, a numerical example is provided to demonstrate the effectiveness and the dynamic nature of the proposed method.

Modern engineering systems are generally composed of multicomponents and are characterized as multifunctional. Condition monitoring and health management of these systems often confronts the difficulty of degradation analysis with multiple performance characteristics. Degradation observations generally exhibit an $s$ -dependent nature and sometimes experience incomplete measurements. These issues necessitate investigating multiple $s$ -dependent degradations analysis with incomplete observations. In this paper, a new type of bivariate degradation model based on inverse Gaussian processes and copulas is proposed. A two-stage Bayesian method is introduced to implement parameter estimation for the bivariate degradation model by treating the degradation processes and copula function separately. Degradation inferences for missing observation points, and for future observation points are investigated. A simulation study is presented to study the effectiveness of the dependence modeling and degradation inference of the proposed method. For demonstration, a bivariate degradation analysis of positioning accuracy and output power of heavy machine tools subject to incomplete measurements is provided.

A new mean stress corrected strain energy model is proposed for fatigue life prediction of metals. Specifically, a mean stress sensitivity parameter is incorporated into modify the dissipated strain energy by introducing two mean stress correction factors. The prediction accuracy of the proposed model is compared with those of Walker, Smith–Watson–Topper, Morrow, and generalized damage parameter models by using 13 experimental data sets. All data points for each material are, respectively, fitted into a single mean stress corrected strain energy-life curve. More accurate predictions are achieved by the proposed model for all data sets with lower model prediction errors than others.

Traditionally, degradation testing and condition monitoring are used separately to investigate field reliability. Barriers are naturally formed between these two types of methods due to condition-discrepancies between lab testing and field monitoring, as well as time-varying missions among product population groups. In this paper, a joint framework for field reliability analysis is presented by integrating degradation testing data as well as mission operating information with condition monitoring observations. A coherent modeling strategy is introduced for the information integration by gradually adopting random effects, dynamic covariates, and marker processes into a baseline stochastic degradation model. In detail, random effects are introduced to cope with the inherent unit-to-unit variation. Dynamic covariates are adopted to deal with the external condition heterogeneity. Marker processes are used to account for the time-varying missions. To facilitate information integration and reliability analysis, the Bayesian method is used to implement parameter estimation and degradation analysis. The reliability assessment of products' populations, degradation prediction, and residual life prediction of individual products are investigated. Finally, an illustrative example for field degradation analysis of oil debris in a lubrication system of a machine tool's spindle system is presented. The effectiveness of information integration and the capability of degradation inference are demonstrated through this example.

Acute lung injury (ALI) is a type of severe pulmonary inflammatory disease with high rates of morbidity and mortality. Now, an increasing number of studies suggest that lncRNAs may act as key regulators of the inflammatory response and play a crucial role in the pathogenesis of many inflammatory diseases. Our study firstly explored the function and underlying mechanism of lncRNA metastasis-associated lung adenocarcinoma transcription 1 (MALAT1) in regulating the inflammatory response of lipopolysaccharide (LPS)-induced ALI in rats.The ALI rats were constructed by intratracheal instillation with LPS. Hematoxylin and eosin (HE) for histological examination were performed to detect histopathological changes in the lung tissues. Enzyme-linked immunosorbent assay (ELISA) was used to determine the concentrations of cytokines TNF-α, IL-6, and IL-1β in the supernatants of the bronchoalveolar lavage fluid (BALF). Quantitative real-time PCR (qRT-PCR) analysis was employed to assess the expression of MALAT1, miR-146a, TNF-α, IL-6, and IL-1β in lung tissues. Luciferase reporter assay and RNA immunoprecipitation (RIP) assay were used to detect the relationship between MALAT1 and miR-146a.The results revealed that MALAT1 knockdown played a protective role in the LPS-induced ALI rat model. In addition, knockdown of MALAT1 in vitro inhibited LPS-induced inflammatory response in murine alveolar macrophages cell line MH-S and murine alveolar epithelial cell line MLE-12. This study found that MALAT1 acts as a molecular sponge for miR-146a and MALAT1 negatively regulated miR-146a expression. Mechanistically, MALAT1 overexpression alleviated the inhibitory effect of miR-146a on LPS-induced inflammatory response in MH-S.Together, our study provided the first evidence that MALAT1 knockdown could suppress inflammatory response by up-regulating miR-146a in LPS-induced ALI, which provided a potential therapeutic target for the treatment of ALI.

Objective: To investigate the correlation between plasma drug concentration of aripiprazole and its clinical efficacy in the treatment of children and adolescent patients with schizophrenia. Methods: 27 cases of children and adolescent patients with schizophrenia received oral non-fixed dosage of aripiprazole for 8 weeks. The plasma concentration of aripiprazole was determined with High Performance Liquid Chromatography (HPLC). The psychotic symptoms of patients were evaluated with Positive and Negative Syndrome Scale (PANSS) and the adverse reactions were assessed with Treatment Emergent Symptom Scale (TESS). Results: There was no correlation between the plasma concentration of aripiprazole and the reduction rate of PANSS scores at the end of first week (r = 0.201, p > 0.05). A positive correlation between the plasma concentration of aripiprazole and the reduction rate of PANSS scores was found at the end of second week (r = 0.352, p = 0.055), and significant correlations were observed at the end of both fourth and eighth weeks (r > 0.503, p < 0.05). No relationship was found between the oral dosage of aripiprazole and the clinical efficacy. The mean concentration of aripiprazole in effective group was higher than that of ineffective group. The lowest effective concentration of aripiprazole was 285.2 ng/ml and the concentration threshold occurring side effects was 440 ng/ml. Conclusion: The plasma concentration of aripiprazole is correlated with its clinical efficacy in the treatment of children and adolescent patients with schizophrenia symptoms, and the suitable concentration range is 285.2~440 ng/ml.

This article presents a prognostic approach to estimate remaining useful life for systems subjected to dependent competing failure processes. In the literature, shock damage is the damage to a soft failure process caused by a shock process. However, how the degradation process causes damage to a hard failure process has not been well studied. In this article, the degradation damage is modeled as the damage to a hard failure process from a degradation process. Degradation and shock processes, as “elemental processes,” result in failures via either a soft failure or a hard failure process, namely, “compound processes.” Instead of leading to a direct failure, elemental processes construct compound processes: the soft failure process consists of a degradation process and shock damage, and the hard failure process consists of a shock process and degradation damage. In this way, the damage in this article especially represents the effect of an elemental process on other compound processes. Furthermore, a particle filter is applied based on the established model for system statement estimation and on-line prediction of remaining useful life distribution with and without measurement noise in prognostics. Finally, a numerical example is presented with sensitivity analysis.

In this paper, by using multi-temporal and high resolution Landsat data and geographic information system techniques, the land use/land cover (LULC) in the 2-km buffer zone of 52 meteorological stations in the Anhui province of China is retrieved and categorized into three types: vegetation (including farmland, forest and grass land), water (including lakes, rivers and pools), and construction (including buildings and roads). Besides, the land surface temperature (LST) in the buffer zone of these stations is also obtained from thermal infrared data. The normalized LST index (NLI) and the heat effect contribution index (HECI) of different LULC types are calculated. Via case studies and statistical analysis, the LULC and thermal environment’s temporal-spatial variance in the 2-km buffer zone of these stations are surveyed, and their impacts on the observational environment are investigated. The study shows that the observational environments of the meteorological stations in Anhui province have been greatly influenced by rapid urbanization. The study proposes two new methods to classify the stations’ observational environment into three types (urban, sub-urban, and rural). One uses the NLI and the other uses the HECI. The NLI method needs only LST information. The HECI method combines both LULC and LST information and, hence, is considered more reliable. The evaluation methods and criteria can be used conveniently, effectively, and quantitatively, and are especially useful when analyzing observational data from meteorological stations in weather and climate research and when choosing a location for a new meteorological station.

This paper conducts a Bayesian analysis of inverse Gaussian process models for degradation modeling and inference. Novel features of the Bayesian analysis are the natural manners for incorporating subjective information, pooling of random effects information among product population, and a straightforward way of coping with evolving data sets for on-line prediction. A general Bayesian framework is proposed for degradation analysis with inverse Gaussian process models. A simple inverse Gaussian process model and three inverse Gaussian process models with random effects are investigated using Bayesian method. In addition, a comprehensive sensitivity analysis of prior distributions and sample sizes is carried out through simulation. Finally, a classic example is presented to demonstrate the applicability of the Bayesian method for degradation analysis with the inverse Gaussian process models.

The aim of this study was to assess the incidence of pulmonary thromboembolism (PTE) in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD),and to evaluate the efficacy and safety of low-dose urokinase (UK) thrombolysis therapy when treating hemodynamically stable AECOPD patients with acute PTE (AECOPD-PTE).A total of 419 AECOPD patients, including 96 AECOPD-PTE, were enrolled. A total of 30 AECOPD-PTE patients were collected retrospectively, and 66 AECOPD-PTE patients were prospectively divided into anticoagulation-only, low-dose UK and standard-dose UK groups. Follow-up 1 year, we evaluated the efficacy and safety of low-dose UK therapy for hemodynamically stable AECOPD-PTE patients.The incidence of PTE in AECOPD patients was 22.9% (96/419), which increased with COPD severity degree ranging from 3.5% (2/57) in mild, 13.6% (19/140) in moderate and 33.8% (75/222) in severe subgroups (P .05). In the prospective study, the total effective rate of low-dose UK group 97.2% (35/36) was higher than that in anticoagulation 75.0% (12/16) and standard-dose UK group 78.6% (11/14) respectively (P .05). In the follow-up, the adverse events rate in low-dose UK group 8.3% (3/36) was significantly lower than that in anticoagulation group 25.0% (4/16) and standard-dose UK group 71.4% (10/14) respectively (P .05). In addition, the mean PTE recurrence time of low-dose UK group (9.0 ± 0) months was longer than anticoagulation group (2.0 ± 1.41) months (P .05). AECOPD relapse time in anticoagulation, low-dose UK and standard-dose UK groups corresponding to (8.5 ± 2.12), (9.0 ± 0) and (8.8 ± 3.40) months were compared with no significant difference (P .05).The incidence of PTE in AECOPD patients was 22.9%, especially with higher occurrence rate in severe COPD. Compared with anticoagulation-only therapy, low-dose UK treatment (500 000 IU/day for 5-7 days) could obtain a better efficacy and safety in hemodynamically stable AECOPD patients with acute PTE, corresponding with a higher effective rate (97.2%) and lower adverse events rate (8.3%) respectively.

Electrohydraulic servo valves play critical roles in modern servo control systems, which require high reliability and high safety. The reliability analysis of a direct drive electrohydraulic servo valve is conducted in this article. First, the failure mechanism of the direct drive electrohydraulic servo valve is investigated by analyzing the structure and the working principle of the direct drive electrohydraulic servo valve. It shows that clamping stagnation, internal leakages and spring fatigue are the main failure modes of direct drive electrohydraulic servo valve. The structure degradation caused by wear enlarges the clearance and results in the increase in null leakages. Then, a gamma process is adopted to describe the internal structure degradation based on the failure mechanism analysis. Heterogeneity among different samples of direct drive electrohydraulic servo valves is studied and handled by introducing unit-specific random effects into the gamma process degradation model. Additionally, in this article, a Bayesian method is used to facilitate the degradation analysis and reliability estimation. The reliability models of sealing, springs and spool valves are presented. Finally, a brief introduction of the experiment of the direct drive electrohydraulic servo valves and an illustrative example of reliability analysis are presented to demonstrate the introduced failure mechanism analysis and the proposed reliability analysis method for direct drive electrohydraulic servo valves.

Fault tree analysis is a powerful and computationally efficient technique for safety analysis and reliability prediction. It decomposes an undesired failure into multiple possible root causes by constructing a sub-event tree and spreading it into basic events. Classical reliability theory using probability theory to quantify the uncertainties of basic events encounters many challenges when failure data are limited. In this case, uncertainty quantification should be carried out based on subjective information, such as experts’ assessment or engineers’ experience. As a generalization of probability theory, imprecise probability theory can quantify subjective information as the upper and lower expectations or previsions. In this article, a fault tree analysis algorithm incorporating subjective information into imprecise reliability models of basic events is proposed to calculate the failure interval of lubricating oil warning system.

Conducting a real aircraft evacuation trial is oftentimes unaffordable as it is extremely expensive and may cause severe injury to participants. Simulation models as an alternative have been used to overcome the aforementioned issues in recent years. This paper proposes a new simulation model for emergency evacuation of civil aircraft. Its unique features and advantages over the existing models are twofold: (1) passengers' critical physical characteristics, e.g. waist size, gender, age, and disabilities, which impact the movement and egress time of individual evacuee from a statistical viewpoint, are taken into account in the new model. (2) Improvements are made to enhance the accuracy of the simulation model from three aspects. First, the staggered mesh discretization method together with the agent-based approach is utilized to simulate movements of individual passengers in an emergency evacuation process. Second, each node discretized to represent cabin space in the new model can contain more than one passenger if they are moving in the same direction. Finally, each individual passenger is able to change his/her evacuation route in a real-time manner based upon the distance from the current position to the target exit and the queue length. The effectiveness of the proposed simulation model is demonstrated on Boeing 767-300 aircraft.

Reliability analysis of complex systems is a critical issue in reliability engineering. Motivated by practical needs, this paper investigates a Bayesian approach for system reliability assessment and prediction with multilevel heterogeneous data sets. Two major imperatives have been handled in the proposed approach, which provides a comprehensive Bayesian framework for the integration of multilevel heterogeneous data sets. In particular, the pass-fail data, lifetime data, and degradation data at different system levels are combined coherently for system reliability analysis. This approach goes beyond the alternatives that deal with solely multilevel pass-fail or lifetime data, and presents a more practical tool for real engineering applications. In addition, the indices for reliability assessment and prediction are constructed coherently within the proposed Bayesian framework. It gives rise to a natural manner of incorporating this approach into a decision-making procedure for system operation and management. The effectiveness of the proposed approach is illustrated with reliability analysis of a navigation satellite.

From the viewpoint of engineering applications, the prediction of the failure of railway axles plays an important role in preventing the occurrence of fatigue fractures. Combining a nonlinear damage accumulation model, a probabilistic S-N curve, and a one-to-one probability density functions transformation technique, a general probabilistic methodology for modeling damage accumulation is developed to analyze the time-dependent fatigue reliability of railway axle steels. The damage accumulation is characterized as a distribution in a general degradation path, which captures a nonlinear damage accumulation phenomenon under variable-amplitude loading conditions; its mean and variability change with time. Moreover, a framework for fatigue reliability assessments and service life prediction is presented based on the estimation of the evolution and probabilistic distribution of fatigue damage over time. The proposed methodology is then validated by experimental data obtained for a railway axle (45 steel and LZ50 steel). The time-dependent reliability is analyzed and demonstrated through probabilistic modeling of cumulative fatigue damage, and good agreement between the predicted results and the experimental measurements under different variable amplitude loadings is obtained.

The redundancy allocation problem has been extensively studied with the aim of determining optimal redundancy levels of components at various stages to achieve the required system reliability or availability. In most existing studies, failed elements are assumed to be as good as new after repair, from a failure perspective. Due to deterioration, the repaired element cannot always be restored to a virtually new condition unless replaced with a new element. In this paper, we present an approach of joint redundancy and imperfect maintenance strategy optimization for multi-state systems. Along with determining the optimal redundancy levels, the element replacement strategy under imperfect repair is also optimized simultaneously, so as to reach the desired availability with minimal average expenditure. A generalized imperfect repair model is proposed to characterize the stochastic behavior of multi-state elements (MSEs) after repair, and a replacement policy under which a MSE is replaced once it reaches the pre-determined number of failures is introduced. The cost-repair efficiency relation, which regards the imperfect repair efficiency as a function of assigned repair cost, is put forth to provide a flexibility of assigning repair efforts strategically among MSEs. The benefits of the proposed method compared to the existing ones are demonstrated and verified via an illustrative case study of a three-stage coal transportation system.

Turbine blades are one of the key components in a typical turbofan engine, which plays an important role in flight safety. In this paper, we establish a establishes a three-dimensional finite element model of the turbine blades, then analyses the strength of the blade in complicated conditions under the joint function of temperature load, centrifugal load, and aerodynamic load. Furthermore, contact analysis of blade tenon and dovetail slot is also carried out to study the stress based on the contact elements. Finally, the Von Mises stress-strain distributions are obtained to acquire the several dangerous points and maximum Von Mises stress, which provide the basis for life prediction of turbine blade.

Service-life is a widely used reliability index in reliability engineering. For a complex dynamic system for which whole system tests are limited, and there is insufficient information to determine the distribution function of reliability models. Fortunately, the boundaries of lifetime variable can be obtained, which can be incorporated through the theory of interval uncertainty. In this study, a service-life assessment method for complex dynamic systems under interval uncertainty is introduced based on Bayesian networks (BN). Firstly, a dynamic fault tree (DFT) model is built for a system. Based on the comprehensive integration of test data, field data, design data and engineering experience, the lifetime of system units are expressed as interval numbers. Then, a coefficient of variation (COV) method is employed to determine the parameters of life distributions. Finally, the BN method is used to estimate the mean life of the example system, and the service-life of this system is assessed as well. The presented method can be easily used in engineering practice for service-life evaluation of complex dynamic systems under interval uncertainty, where lifetime data is limited.Copyright © 2015 by ASME

Because solder joint interconnections are the weaknesses of microelectronic packaging, their reliability has great influence on the reliability of the entire packaging structure. Based on an accelerated life test the reliability assessment and life prediction of lead-free solder joints using Weibull distribution are investigated. The type-I interval censored lifetime data were collected from a thermal cycling test, which was implemented on microelectronic packaging with lead-free ball grid array (BGA) and fine-pitch ball grid array (FBGA) interconnection structures. The number of cycles to failure of lead-free solder joints is predicted by using a modified Engelmaier fatigue life model and a type-I censored data processing method. Then, the Pan model is employed to calculate the acceleration factor of this test. A comparison of life predictions between the proposed method and the ones calculated directly by Matlab and Minitab is conducted to demonstrate the practicability and effectiveness of the proposed method. At last, failure analysis and microstructure evolution of lead-free solders are carried out to provide useful guidance for the regular maintenance, replacement of substructure, and subsequent processing of electronic products.

The inverse Gaussian (IG) process is recently proposed as a flexible family of models for degradation modeling. This paper investigates Bayesian analysis of IG process model with random effects for degradation modeling. Novel features of Bayesian analysis are the natural manners for incorporating subjective information and pooling of random effects information between test specimens. An IG process model with random effects named as the random drift IG process model is investigated using the Bayesian method. In addition, a Bayesian χ2 goodness-of-fit test is developed for this Bayesian analysis. The applicability of the Bayesian method for degradation analysis with this IG process model is demonstrated with a classic example.Copyright © 2013 by ASME

It is crucial to evaluate reliability of the electrohydraulic servo valve (EHSV) which has been widely used in many fields. This paper firstly presents the physical structure and functional principle of EHSV. The Failure Mode, Effects and Criticality Analysis (FMECA) is conducted afterwards. Lastly, a method that can quantitatively analyze reliability and failure rate of EHSV suffering common cause failures is proposed.

According to the aero-engines' flight hours and the speed cycle number, we can obtain the relationship between the load spectrum and the life of its main components; According to the analysis of actual measured load spectrum and the fatigue damage model, this paper adopts the S-N curve and Miner's rule to estimate the fatigue life of engine's first stage turbine disk.

When damage tolerance based design and analysis of crack growth process are carried out based on the experimental data, there exist great uncertainties for model parameters, if these parameters were considered to determined value, it will cause a large predicted error. Moreover, if the correlation between these parameters are ignored or not be considered, the scatter of parameters cannot be captured accurately. In order to improve the prediction ability of fatigue life, Copula theory is introduced into fatigue fracture analysis and the measurement of parameter correlation associated with copula functions. Furthermore, the method to choose optimal Copula are introduced, a more appropriate t-Copula-based function is demonstrated to construct the joint probability density function model of log-normal fatigue crack parameters considering the uncertainty of the parameters. As a general approach, the proposed Copula method can not only characterize the correlation between crack growth parameters, but also, in the lack of datasets, model Gaussian-copula to obtain the bivariate joint probability distribution, which expands the application of Copula theory and the parameter uncertainty measures.

By collecting the data from thermal cycling test of an lead-free BGA interconnection structure, using type-I interval censored data processing method and modified Emgelmaier fatigue life model, the cycles to failure of lead-free solder joint are predicted. The comparison between the results obtained by proposed methods and the life calculated by Matlab verifies the practicability and effectiveness of the two methods.

In this paper, a new fatigue reliability analysis method based on saddlepoint approximation (SPA) was proposed for calculating the probability of failure of turbine disk alloy in a low cycle fatigue (LCF) regime. Firstly, two LCF life prediction models based on total strain energy density and Support Vector Regression (SVR) meta- model are presented for turbine disk alloy GH4133 under different loading conditions at 250 °C. Compared with the SWT model, modified Walker model and Response Surface (RS) model, the predicted lives by the proposed models are within a factor of ±2 and a factor of ±1.1 respectively. Sec- ondly, based on the fatigue design criteria, the probabili- ties of failure are calculated using SPA for the explicit and implicit performance functions using two proposed LCF models and viscosity-based model. These three models have provided the reliability design rules for GH4133. Finally, the failure probabilities curves between SPA and the designed fatigue lives are achieved. The reliability analysis results were found to be in good agreement with the calculated results of test data. These results show that SPA is very apt for the fatigue reliability analysis of turbine disk under different loading conditions using only a small number of samples without any distribution assumptions for random variables. Moreover, it can be used to estimate the system's probability of failure with a large number of random variables or high nonlinearity of performance functions. The effectiveness and accuracy of the combina- tion of the fatigue models and SPA for fatigue reliability analysis are verified using three examples.

Improved fatigue life prediction methods will assist in reducing life cycle costs and increasing the availability of mechanical components. These methods include Stromeyer function, Basquin law, Manson-Coffin law, frequency modified damage function (FMDF) model, strain range partition (SRP) and so on. However, different models are applied to assess the life under high cycle fatigue (HCF) and low cycle fatigue (LCF). This is a domain of concern in the development of fatigue life prediction models which contain combinations of HCF and LCF. In this paper, based on the Manson-Coffin law and an energy-based damage parameter, a general energy-based model is presented to predict fatigue life under both HCF and LCF conditions. By correlated with the energy-based damage parameter to the test data of superalloys GH901 and K403 in existing literatures, the lives predicted by the proposed model are compared with experimental results resulting in a good agreement.

According to the necessity of reliability analysis for satellite system subjected to common-cause failures, the unreliability expression under a variety of common-cause condition is developed to analyze the unreliability of satellite system in this paper. Based on static fault tree analysis of satellite system, the binary decision diagram is used to evaluate the reliability considering common-cause failures. The importance of prevention of common-cause failures in large structured redundancy system is verified by using practical calculation example.