3D triple-level cell (TLC) NAND flash based solid state drive (SSD) is gradually becoming the dominant storage media in large-scale storage systems due to high storage density and low cost-per-bit. It ranks one of the top replaced hardware components in systems and their enormous amount also indirectly increases the failure probability, resulting in irreversible data loss disaster and service unavailability. This paper for the first time investigates system-level 3D TLC SSDs to characterize reliability and sub-health status based on field Self-Monitoring, Analysis and Reporting Technology (SMART) data, and predict impending failure proactively. We explore real-world datasets and derive some findings for each selected attribute in predetermined categories, contributing to the following feature selection and enhancing the interpretability of prediction models. Moreover, various machine learning models are trained to predict failures ahead of time, and experimental results show that random forest model can achieve 0.636 f ₁ -score and 0.662 MCC for a 7-day prediction horizon, and 42.5% true positive rate (TPR) with 0.00% false positive rate (FPR). Different time window sizes, training set fractions and ratios of negative to positive are analyzed as well.

For a grasping task planner, the pre-planning of the reachable tip pose in a manipulator’s workspace is important. On this basis, for a seven-degree-of-freedom (7-DOF) redundant manipulator, it is very meaningful to study how to give full play to its redundant characteristics to achieve more dexterous grasping. In this paper, through the improved shape primitive method, the reachability spheres of the 7-DOF manipulator studied were more accurately captured, and a more accurate reachability capability map was generated. Then, based on the idea of redundancy angle discretization, the concept of the obstacle avoidance ability (OAA) index is innovatively proposed to measure the dexterity of the grasping task of the manipulator in a certain tip pose. Based on the OAA index axis, the distribution of the OAA index in each reachability sphere was analyzed. The prediction models of the OAA index of all reachability spheres in the workspace of the manipulator were obtained by the Levenberg–Marquardt algorithm and finally formed a dexterity capability map of the manipulator, which provides a new idea for the pre-planning of the dexterous grasping task of the redundant manipulator. Finally, we give the general frameworks of different grasping pre-planning by combining two kinds of capability maps and verify the effect.

Currently, the passive part of the space station platform’s docking device has been finalized, emphasizing the need for a docking mechanism that enables the on-orbit assembly of large payloads using in-situ resources. This paper presents the design of a compact, high-precision dual-component docking mechanism for large space loads. First, we propose a parametric design for the active and passive sides, accompanied by the constraint equations for the capture mechanism. Next, a progressive positioning method, beginning with coarse correction and followed by fine correction, is proposed. The dual-component calibration and positioning performance are analyzed to initially capture large deviations and achieve high-accuracy docking of electrical and hydraulic connectors subsequently. Static analysis of both single and dual components reveals a positive correlation between load-carrying capacity and diameter. During the prototype testing, the maximum positional deviations were measured 0.12 mm in the X-direction, 0.5 mm in the Y-direction, and 0.07 mm in the Z-direction. The maximum angular deviation was 0.04° when the dual components operated together. Finally, the impact of axial and radial docking conditions in orbit was analyzed to verify that the loading requirements were satisfied. This work offers both theoretical and technical insights for the future development of docking mechanisms for large space loads.

This paper presents an innovative solution that is able to suppress the thrust ripple in a high-power asynchronous linear induction motor (LIM) used in a microgravity experiment facility electromagnetic launch (MEFEL) system. By addressing the crucial need for low levels of thrust ripple in MEFEL applications, we propose a dynamic model-based adaptive controller (MAC) and an enhanced quasi-proportional-resonant (PR) controller. The MAC is designed to compensate for the inherent impedance asymmetry of the linear motor. The PR controller minimizes thrust ripple by eliminating harmonics within the current loop. A comparative analysis indicates that both MAC and PR control are effective in reducing harmonics, suppressing the thrust ripple, and maintaining system stability. Computer simulations show a noteworthy 75% reduction in the thrust ripple and a decrease in the negative current. Partial tests on the MEFEL device validate the practical efficacy of the proposed control methods, emphasizing the method’s ability to enhance the quality of microgravity in real-world scenarios significantly.

To evaluate the lifetime and reliability of long-life, high-reliability products under limited resources, accelerated degradation testing (ADT) technology has been widely applied. Furthermore, the Bayesian evaluation method for ADT can comprehensively utilize historical information and overcome the limitations caused by small sample sizes, garnering significant attention from scholars. However, the traditional ADT Bayesian evaluation method has inherent shortcomings and limitations. Due to the constraints of small samples and an incomplete understanding of degradation mechanisms or accelerated mechanisms, the selected evaluation model may be inaccurate, leading to potentially inaccurate evaluation results. Therefore, describing and quantifying the impact of model uncertainty on evaluation results is a challenging issue that urgently needs resolution in the theoretical research of ADT Bayesian methods. This article addresses the issue of model uncertainty in the ADT Bayesian evaluation process. It analyzes the modeling process of ADT Bayesian and proposes a new model averaging evaluation method for ADT Bayesian based on relative entropy, which, to a certain extent, can resolve the issue of evaluation inaccuracy caused by model selection uncertainty. This study holds certain theoretical and engineering application value for conducting ADT Bayesian evaluation under model uncertainty.

With the rapid development of manned spaceflight engineering, the scientific experimental data in space application system is increasing rapidly. How to efficiently query the specific data in the mass data volume has become a problem. In this paper, a method of retrieving the object data based on the object attribute as the keyword is proposed. The HBase database is used to store the object data and object attributes, and the secondary index is constructed. The research shows that this method is a good way to retrieve specified data based on object attributes.