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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.

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.

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.

Space electronic products usually have to meet the requirements of high reliability and long lifetime, and it is hard to obtain sufficient reliability data in the general accelerated testing. Highly accelerated life testing (HALT) and highly accelerated stress screening (HASS) can be used to improve the reliability of long lifetime products; however, they are mainly applied in qualitative method. This paper integrated degradation analysis as a quantitative assessment method after HALT and HASS. A method based on the integration of HALT, HASS and accelerated degradation testing (ADT) (HHA) is proposed. Firstly, the basic framework of HHA is developed based on the three accelerated testing methods. Secondly, the HHA model and degradation data model are researched. Lastly, a case study was performed on solid state drives (SSDs). Research shows that the HHA method is effective and could be applied in the space electronic products.

As commercial DC/DC converters are widely used in space application, their failures can cause the electronic system to work abnormally and lead to severe safety accidents. To predetermine time to failure and generate substantial safety, it is necessary to conduct an accelerated life test and assess reliability in advance. This paper has applied high-temperature accelerated tests to predict DC/DC converters reliability and lifetime under practical temperature conditions considering field stress variation. First, periodic performance parameters measurements, such as output voltage and efficiency over high temperature tests are performed at 100, 120 and 140oC. Additionally, the Arrhenius-Weibull model is developed and the Weibull regression analysis method for time-censored failure data is proposed. The estimation results are compared with maximum likelihood estimation (MLE) and failure-censored approximation method, demonstrating the effectiveness of the regression analysis method. Finally, reliability analysis is established considering variations in field temperature conditions. The whole procedure is illustrated by the time-censored testing results of the ISL8225M series DC/DC converter.

The traditional accelerated degradation testing (ADT) technology usually evaluates reliability and life of products through analyzing degradation mechanism and processing degradation data with probabilistic method. However, different failure thresholds will result in different evaluation results. In traditional evaluation method of ADT, the failure threshold of product is influenced by the environmental factors and the cognitive level of experimenter, which makes the failure threshold ambiguous and affects the accuracy of evaluation. Ignoring the uncertainty of failure threshold may lead to the fact that results of ADT statistical analysis can be hardly used as the basis for life or reliability evaluation of product. Thus, it is very necessary to analyze the uncertainty of failure threshold during ADT evaluation, which can help deciders make a more credible evaluation of products. This article introduces the traditional evaluation method of ADT and analyzes the importance of the failure threshold to the evaluation result. Then, fuzzy model and membership function of the failure threshold are modeled and analyzed. After that, the reliability function considering the uncertainty of failure threshold is obtained. At last, based on evaluation method considering uncertainty of failure threshold, a case study of lithium-ion battery (Li-ion) is presented and its effectiveness is verified.

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.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.

Space electronic products usually should meet the mission requirements of long life and low cost. Because of insufficient failure data, it is hard to assess the reliability precisely by using traditional reliability testing and statistical method. In this paper, a method of reliability assessment based on integration of highly accelerated life testing (HALT) and accelerated degradation testing (ADT) is proposed. Firstly, based on performance analysis of electronic products, the degradation of performance is modeled. Moreover, the data of drift performance is extracted, fitted and fused into the observation data in ADT. Lastly, the reliability assessment for electronic products is studied based on degradation data. This research provides an effective way for accelerated reliability testing of space electronic products, and it has been applied into practice.