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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.

The accelerated degradation testing (ADT) Bayesian evaluation method comprehensively utilizes product degradation data under accelerated stress levels collected over a short period of time and multiple sources of prior information, such as historical information, similar product information, simulation information, etc., to conduct life and reliability evaluation. Through the prior distribution, prior information affects the ADT Bayesian evaluation results ultimately. However, different evaluators may obtain different prior distributions based on the same prior information due to varying experiences or rules, which may lead to differences in the ADT Bayesian evaluation results. Therefore, it is necessary to analyze and study the impact of prior distribution uncertainty on the ADT Bayesian evaluation results while also finding criteria to judge the quality of prior distributions. This paper focuses on the ADT Bayesian evaluation method based on the Wiener process and the Arrhenius relation, studying the influence of different prior distributions on the robustness of ADT Bayesian evaluation results. Additionally, based on the deviance information criterion (DIC), a criterion for selecting prior distributions in the ADT Bayesian evaluation method is proposed. Through carrying out uncertainty analysis of prior distribution in the ADT Bayesian evaluation method, a theoretical system and framework for analyzing prior uncertainty in ADT Bayesian evaluation based on DIC are established, providing a better foundation for the practical application of the ADT Bayesian evaluation method in engineering.

Abstract With the benefit of partially or entirely offloading computations to a nearby server, mobile edge computing gives user equipment (UE) more powerful capability to run computationally intensive applications. However, a critical challenge emerged: how to select the optimal set of components to offload considering the UE performance as well as its battery usage constraints. In this paper, we propose a novel energy and performance efficient deep learning based offloading algorithm. The optimal offloading schemes of components based on remaining energy and its performance can be determined by our proposed algorithm. All of these considerations are modeled as a cost function; then, a deep learning network is trained to compute the solution by which the optimal offloading scheme can be determined. Experimental results show that the proposed method is superior to existing methods in terms of energy and performance constraints.

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.

This paper develops a competing risk model to simultaneously analyze censored catastrophic failures and nonlinear degradation data of the NAND-based solid-state drives for space application. Two dominant failure modes are the hard failure of the controller due to single-event latch-up (SEL) and the soft failure of the NAND Flash manifesting as random write current degradation. As hard failure probability increases with radiation intensity and particle number, we establish the inverse power law-Weibull model for SEL cross section to model the accelerated censored data. The hard failure model is presented based on the invariance principle of total environmental particles' energy. On the other hand, soft degradation is described by the nonlinear Wiener-process-based accelerated degradation test model. Specifically, the temporal variability concerning the inherent variability of the degradation process over time and the unit-to-unit variability in degradation rates are both taken into account. Then, we derive the reliability functions and other quantities of interest under normal conditions with the assumption of independence of failure modes. Furthermore, to estimate the unknown parameters in the competing risk model, the transformed extreme value regression analysis other than the least square fitting method is adapted to issue the problem of data uncertainty of hard failures, whereas the maximum likelihood estimation method is developed for soft failures. Finally, a detailed simulation example is given to illustrate the procedure of the proposed reliability model with a sensitivity analysis.

In this paper, an improved algorithm of wavelet packet-chaos model for life prediction of space relays based on volterra series is proposed. In the proposed method, the high and low frequency time sequence components of performance parameters are obtained by employing the improved wavelet packet transform to decompose the performance parameters of the relay into multiple scales. Then the optimization algorithm of parameters in volterra series is improved, and is used to construct a chaotic forecasting model for the high and low frequency time sequence components gained by the wavelet packet transform. At last, the chaotic forecasting results of the high and low frequency components are combined by taking the wavelet packet reconstruction approach, so as to predict the lifetime of the studied space relay. The algorithm can predict the life curve of the relay accurately and reflect the characteristics of the relay performance with sufficient accuracy. The proposed method is validated via a case study of a space relay.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The accelerated degradation testing (ADT) Bayesian evaluation method comprehensively utilizes product degradation data under accelerated stress levels collected over a short period of time and multiple sources of prior information, such as historical information, similar product information, simulation information, etc., to conduct life and reliability evaluation. Through the prior distribution, prior information affects the ADT Bayesian evaluation results ultimately. However, different evaluators may obtain different prior distributions based on the same prior information due to varying experiences or rules, which may lead to differences in the ADT Bayesian evaluation results. Therefore, it is necessary to analyze and study the impact of prior distribution uncertainty on the ADT Bayesian evaluation results while also finding criteria to judge the quality of prior distributions. This paper focuses on the ADT Bayesian evaluation method based on the Wiener process and the Arrhenius relation, studying the influence of different prior distributions on the robustness of ADT Bayesian evaluation results. Additionally, based on the deviance information criterion (DIC), a criterion for selecting prior distributions in the ADT Bayesian evaluation method is proposed. Through carrying out uncertainty analysis of prior distribution in the ADT Bayesian evaluation method, a theoretical system and framework for analyzing prior uncertainty in ADT Bayesian evaluation based on DIC are established, providing a better foundation for the practical application of the ADT Bayesian evaluation method in engineering. [ABSTRACT FROM AUTHOR]

This paper, based on the idea of redundancy angle discretisation, proposes an obstacle avoidance method for the fixed tip pose trajectory of a seven degrees-of-freedom (7-DOF) modular manipulator. First, for the case in which a specific redundancy angle is given, the analytical solutions of the redundant manipulator left 6-DOF subchain are found. Then, through the discretisation of the redundancy angle, the concept of the self-motion space of the tip pose is proposed and is extended to the concept of the self-motion space of the trajectory. Based on this discrete space, a path-planning algorithm is proposed to help select the appropriate redundancy angles to obtain the collision-free solution set of the fixed Cartesian trajectory. However, due to the large fluctuation of the obtained path, a path optimisation method based on the path cost is proposed to smooth the path, and the continuous and collision-free solution set of the manipulator tip's trajectory is obtained. The method proposed in this paper provides a new thought for the problem of collision-free solution set planning for the Cartesian trajectory of a 7-DOF manipulator and it has great application potential in working environments with high accuracy requirements for the trajectory. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the benefit of partially or entirely offloading computations to a nearby server, mobile edge computing gives user equipment (UE) more powerful capability to run computationally intensive applications. However, a critical challenge emerged: how to select the optimal set of components to offload considering the UE performance as well as its battery usage constraints. In this paper, we propose a novel energy and performance efficient deep learning based offloading algorithm. The optimal offloading schemes of components based on remaining energy and its performance can be determined by our proposed algorithm. All of these considerations are modeled as a cost function; then, a deep learning network is trained to compute the solution by which the optimal offloading scheme can be determined. Experimental results show that the proposed method is superior to existing methods in terms of energy and performance constraints. [ABSTRACT FROM AUTHOR]