The accurate degradation prediction of Lithium-ion batteries is beneficial to the reliability and safety of battery-driven systems. In this paper, a long short-term memory network (LSTM) model is utilized to predict the capacity degradation trend using partial charge and discharge features of Lithium-ion batteries. Firstly, significant features are extracted from the original charge and discharge data. Then the Pearson correlation coefficient is adopted to filter the features with high correlation coefficients. Selected features are subsequently treated as the input of the prediction model. Finally, a LSTM model is developed and associated hyperparameters are established by Adam algorithm. The proposed method is validated by experimental results on the NASA battery dataset.
Common photoelectric pods often have composite photoelectric sensing functions, such as visible light-sensing components, infrared night vision components, distance sensing components, etc. Among them, visible light sensing component is the most basic and widely used image sensing component. With the rapid development of modern optoelectronic technology, the reliability level of a visible light camera has become to be one of the most critical technical requirements in an optoelectronic pod, which greatly determine the success of a mission. For the entire equipment, the optoelectronic pod is like the eye of human beings. Once it fails, it may bring a devastating threat to the safety of the entire equipment and the performance of one task. This paper focuses on the reliability analysis of the visible light camera used in the optoelectronic pod. Through the analysis of its internal structure and performance indicators, the key and weak design links are obtained, which can provide important theoretical support for the reliability design of optoelectronics.
A reliability qualification test (RQT) is used to judge whether a batch of products meets pre-specified reliability requirements. For high reliable systems, traditional RQT plans often require long test time or pose high risks to both producer and consumer. To cope with this problem, this paper proposes a new method to derive RQT plans by making use of subsystem data, when both system and subsystems are following exponential distributions. Compared with conventional RQT plans that construct system test based on which decisions are made, the proposed method enables deriving system test plans with much shorter test time and keeping producer and consumer risks under control at the same time. A case study is presented to prove the validity of the proposed method.
To effectively and quickly solve the robust redundancy allocation problem under the Min-Max regret framework, a sequential two-stage approach is proposed. The proposed metaheuristic approach includes two stages: the first stage is to solve an optimization problem with regard to various scenarios, and the second stage is to search for the optimal solution of decision variables. These two stages are executed sequentially and iteratively until the preset termination condition is satisfied. The listed example demonstrates the performance of the proposed approach.
Under the conditions of systematic warfare, the diversified mission scenarios and the structure of complex equipment have led to problems such as difficulties in clarifying equipment health management objects and in implementing the allocation of support resources. To solve those problems under the influence of different mission profiles, this paper proposes a mission-oriented prioritization method for health management objects of complex equipment. Firstly, using the graph theory, function-dependent network models are constructed according to the composition of equipment system and mission sessions under the mission profile; Secondly, the PageRank algorithm is used to measure the importance of each system; Finally, combined with the characteristics of the mission profile, the priority list is derived by updating the PR values with expert knowledge, by which the key health management objects are determined. The proposed method is verified with a case study on the design of an equipment health management system. The results show that the method can obtain the priority of each sub-system of equipment efficiently, so as to provide appropriate objects for equipment health management and the distribution of support resources.
