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Highly transparent Er:Y2O3 ceramics (1–9 at.% Er) were fabricated by hot pressing sintering with ZrO2 as the sintering additive. The microstructures, transmittance, luminescent properties, thermal conductivity, and mechanical properties of the Er:Y2O3 ceramic samples were investigated in detail. The samples all exhibited dense and fine grain microstructures; the average grain sizes were about 0.8 μm. The transmittance levels of the samples with various Er concentrations (2 mm thick) at the wavelengths of 600 and 2700 nm were ~74 and ~83%, respectively. As the Er doping concentration increased from 1 to 9 at.%, the up-conversion luminescence of the samples gradually changed from green to red, with the intensity ratio of red/green light increasing from 0.28 to 2.01. Meanwhile, the down-conversion luminescence properties of the specimens were also studied. When the samples were under 980 nm excitation, the emission bands were detected at 1552, 1573, 1639, and 1661 nm. The thermal conductivity of the samples was found to decrease from 8.72 to 5.81 W/(m·K) with an increase of the Er concentration from 1 to 9 at.%. Moreover, the microhardness and fracture toughness of the samples with 1 at.% Er concentration were ~8.51 GPa and ~1.03 MPa·m1/2, respectively.

Degradation reliability analysis incorporating two-phase or even multi-phase features has become a research focus in recent years. Motivated by the fact that a practical multi-phase degradation procedure may probably involve both linear phases and nonlinear phases, a multi-phase degradation model is constructed that can consider the phase-varying nonlinear property. The unit-to-unit variability is incorporated. Meanwhile, the reliability function based on the first passage time (FPT) conception is derived, where the solution for the two-phase situation is first obtained, and the one for the multi-phase circumstance is constructed via a reclusive scheme. To verify the reasonability of the proposed reliability function, a simulation study has been complemented. Then two real applications are given to demonstrate the validity and the effectiveness of the proposed method.

To achieve the safe, reliable autonomous operation of spacecraft, research on the fault diagnosis of control systems has attracted the attention of engineers and academicians throughout the aerospace field. Diagnosability can characterize the fault diagnosis capability of control systems. Connecting diagnosability analysis to the design of a spacecraft control system’s structure and diagnosis method can fundamentally improve the system’s fault diagnosis capability, improving the safety and reliability of autonomous spacecraft operation. In this paper, the diagnosability of spacecraft control systems is systematically studied from five perspectives: necessity, the current research status, the connotation, a novel index system and current development trends of diagnosability. First, the current status of spacecraft reliability analysis and reliability-based design is briefly reviewed, and existing problems are described, highlighting the advantages and importance of diagnosability research. Furthermore, the basic concepts of diagnosability are briefly introduced. By analyzing the current status of existing research on the diagnosability of both general and spacecraft control systems, the application scope of the diagnosability of spacecraft control systems is summarized. Moreover, the definition and influencing factors of the diagnosability of spacecraft control systems are presented to refine existing concepts, and a universal evaluation index system is proposed for the diagnosability of spacecraft control systems to further enhance the applicability of diagnosability evaluation and diagnosability-based design to spacecraft. Finally, the existing shortcomings and future development trends of diagnosability research for spacecraft control systems are discussed.

Working on product lifetime data is of significant importance for evaluating safety and reliability, predicting remaining useful life and formulating maintenance strategy or replacement policy. In practical applications, observed datasets often consist of failure data and randomly censored data, which are referred as general censored data. Meanwhile, inverse Gaussian (IG) distribution has been widely adopted to depict lifetime data because it not only can possess flexible expression formats but also can explain the mechanism of first hitting time from a soft failure viewpoint. Motivated by these two regards, this paper develops a novel method on best linear unbiased estimations (BLUEs) for general censored data. A three-parameter IG distribution type is adopted. A novel method is established to optimize the skewness parameter. Then, BLUEs of mean and standard deviation can be obtained. The proposed method can construct closed-form parameter estimations in linear functions of order statistics. The computation process has been further simplified for flexible applications. The frequently utilized maximum likelihood estimation method is also introduced as a reference for a better understanding. Comparative results of both comprehensive simulation study and empirical application illustrate that the proposed method can significantly enhance the estimation accuracy and keep a stable performance, because more life information can be extracted and adopted from the censored datasets.

In accelerated degradation tests, products are usually exposed to several environmental variables or operating conditions. This motivates the need for developing an accelerated degradation model involving multiple accelerating variables. Among the current literature, the conventional accelerated degradation models involving multiple accelerating variables have not considered the measurement error, which inevitably exists in practical degradation datasets. Therefore, a Wiener process-based accelerated degradation model that simultaneously considering multiple accelerating variables and measurement error is proposed. Then approximate closed-form expressions for the failure time distribution (FTD) and its percentiles are derived. The expectation maximization (EM) algorithm is adopted to estimate unknown parameters. Moreover, a multivariate normality testing method is developed to test the fitting goodness of the degradation model. Finally, a comprehensive simulation study and a real application are given to validate the proposed method. The result shows that the proposed model can provide precise estimates even for small sample size of approximately five, and the estimated mean square errors (MSEs) of the mean time to failure (MTTF) and the FTD percentile of the proposed model can be improved by at least 70 % compared with those of the reference methods when the sample size is same.

To properly depict the heteroscedasticity when analyzing strain-based fatigue reliability, this paper proposes a novel P-ε-N model from both viewpoints of geometric properties and statistical characteristics. The constructed approach deals with the elastic strain and plastic strain separately, where the logarithmic fatigue life is considered as normally distributed with linear mean and standard deviation (std) functions of logarithmic elastic strain or plastic strain, respectively. P-ε-N curve with any percentile can be derived to facilitate the reliability analysis. An improved parameter estimation method based on maximum likelihood estimation (MLE) via genetic algorithm (GA) is further established. The presented method is illustrated and verified via a comprehensive simulation study and two real applications by comparing with the conventional homoscedasticity model. Corresponding results indicate that our proposed method can enhance the modeling accuracy and provide an extensive adaptation.

Fault diagnosis of satellite attitude control system is an important task to ensure the safe and reliable operation of on-orbit satellites. At present, most fault diagnosis methods are to diagnose independent identically distributed(i.i.d) task objects. However, even if the same device works under different working conditions, the distribution domain of the collected data almost always changes. At the same time, the training of fault diagnosis model under full working conditions can increase the model complexity and training time, and there may unknown working conditions. In view of the above situation, this paper proposed a domain adaptive adversarial deep neural network based fault diagnosis method. By combining the feature extractor, label classifier and domain classifier with the convolutional neural network and gradient inversion layer (GRL), the effective label classification can be achieved while the resolution of different domains can be reduced. We achieved feature extraction of the classification learning task in the source domain and transfer of the classification task between the two domains. The effectiveness of the diagnosis model is verified in the ground simulation data of a certain satellite under different conditions.

A fault diagnosis method of control moment gyroscope (CMG) based on K-means algorithm is proposed to improve the fault diagnosis accuracy by considering the hidden correlation between multidimensional telemetry data. Principal component analysis (PCA) and t-distribution random neighborhood embedding (t-SNE) are used to extract features of CMG digital and physical data. On this basis, the fault diagnosis process of CMG based on K-means algorithm is supplied. Finally, the effectiveness of the proposed method is verified by using the actual in-orbit CMG data.

Highly transparent and submicrometer-grained Yb:Y2O3 ceramics were obtained by hot-pressing method with ZrO2 sintering additive. Hot-pressing method provides extra driving force to promote the densification of Yb:Y2O3 transparent ceramics, resulting in high transmittance achieved. The transmittances of all samples were ranging from 80.5-81.8% at 1100 nm and 72.7-77.7% at 600 nm. The ceramic samples in this work were sintered at a low temperature of 1600 ℃ for 3 h, providing dense microstructures with average grain size less than 1μm, which are smaller than those of Yb:Y2O3 ceramics prepared by other traditional sintering techniques. The microhardness and fracture toughness were ranging from 8.16 to 7.99 GPa, and from 0.94 to 0.88 MPa·m1/2, respectively, which are higher than the comparison samples fabricated by pressureless sintering. The absorption cross-sections of the 1 at.% Yb-doped sample at 950 and 976 nm were 0.93 × 10-20 and 1.22 × 10-20 cm2, respectively. The fluorescence decay lifetime at 1032 nm was in the range of 0.79-1.36 ms. According to the available literature, this is the first report about Yb:Y2O3 transparent ceramics fabricated by hot-pressing sintering without any combined sintering process. The ceramics obtained may have potential use for solid-state lasers partly replacing single crystals.

Reconfigurability refers to the ability of the system to overcome all faults or restore some of its performance by changing the structure or control algorithm under the condition of resource constraints and operating conditions within a certain period of time to ensure the security when the control system fails. The establishment of reconfigurability evaluation and design theoretical system is of great significance for improving the operational reliability and service life of the whole spacecraft. Research projects are being conducted worldwide regarding reconfiguration control technology. We summarize the performance factors that affect system reconfigurability based on several typical reconfiguration methods by analyzing the constraints that the system can satisfy through fault-tolerant approaches. Since the reconfigurability evaluation index reveals the limitations and potentials of reconfigurable ability of the system, we refine the quantitative reconfigurability evaluation method based on various influencing factors. We anticipate that this work will play a guiding role in the reconfiguration strategy and design of spacecraft in-orbit to achieve the fault forward.

The Wiener process model has been widely applied in degradation analysis for long-life and highly reliable products. However, the biggest challenge lies in how to properly describe the time-varying degradation mean and variance and the unit-specific property. Meanwhile, the current Wiener process degradation models cannot properly describe the degradation processes with increasing deterioration path and decreasing dispersity which do exist in real applications. Motivated by this practical problem and based on our previous study, an improved Wiener process degradation model is proposed. The degradation model can be widely adopted to depict practical degradation procedures illustrating a linear degradation mean and a quadratic variance. A random drift coefficient is further incorporated to properly consider the heterogeneity among items. The improvements can further expand the applicable scope and improve the statistical inference accuracy. Maximum likelihood estimations of unknown parameters are derived. The mean time to failure and the percentile of the failure time distribution are also constructed. Comparative results with reference methods from comprehensive simulation studies and practical applications both illustrate that the proposed method is more reasonable and can provide superior evaluation accuracy, even in limited sample size circumstances.

Degradation information reflecting the product or system health state plays an important role in assessing reliability and making maintenance schedule. Since degradation inspections are usually compounded and contaminated by measurement errors in real applications, the conventional Wiener process with identically distributed independent Gaussian error is usually adopted. However, in many situations, autocorrelation may probably exist among the measurement errors at sequential test points because of cyclic changes or modeling errors, especially when the time intervals are relatively short. Motivated by this practical issue, a Wiener process degradation model with one-order autoregressive (AR(1)) measurement errors is proposed for degradation analysis. Explicit forms of the probability distribution function (PDF), the cumulative distribution function (CDF) and the corresponding mean time to failure (MTTF) are derived based on the concept of first hitting time (FHT). Furthermore, maximum likelihood estimations (MLE) of unknown parameters are derived. The effects of model mis-specification regarding the estimation of MTTF are also discussed. Finally, a comprehensive simulation study and two practical applications are given to demonstrate the necessity and efficiency of the proposed model.

In recent years, there has been considerable interest on the part of scientist and engineers in effectively designing accelerated degradation test, which is very useful to assess product's life information with long-life and high-reliability properties. An effective optimal ADT design method can provide a reasonable arrangement of accelerated stress level setting, sample allocation and test time. However, existing researches can hardly optimize all the multiple decision variables simultaneously in an efficient way. In the current study, an effective stochastic process based constant stress-accelerated degradation test (CSADT) model is constructed, and then an optimization design method is proposed based on the CSADT model, where the multiple decision variables involving stress level, sample allocation and test time arrangement for each stress level can be simultaneously optimized based on genetic algorithm (GA) and Monte Carlo simulation. Meanwhile, a comprehensive sensitivity analysis method incorporating orthogonal experimental design, normalization and range analysis is constructed to illustrate the robustness of the optimal design procedure. Finally, an optimal CSADT design for self-regulating heating cables is presented, and comparative results demonstrate the rationality and effectiveness of the proposed method.