The brushless DC motor experiences operating safety problems due to the deterioration of its components following long-term operations, which are easily overlooked. To resolve these problems, failure mode, effects, and criticality analysis is utilized to characterize potential hazards in the motors. Hilbert-Huang transform is then employed to obtain the frequency-domain energy values of the vibration signals, which is defined as characteristic values that represent the performance degradation state. Second, gray model is selected to analyze the frequency-domain energy values and establish differential equations to predict the future vibration status, thereby achieving the vibration-based fault prediction. Furthermore, a gray safety assessment model is proposed to implement the safety assessment for the motor. The fault prediction and gray safety assessment are carried out based on historical data obtained from the brushless DC motor vibration experiment. The accuracy level of the gray model predictions is classified as Wonderful, thereby demonstrating the efficiency of gray model for the fault prediction. In addition, as low as reasonably practicable law is chosen to classify risk levels and formulate safety strategies in accordance with the results of safety assessment. Finally, the proposed safety effects of the methods and strategies are evaluated for microscopic and macroscopic levels.
Unmanned aerial vehicle (UAV) swarms is an emerging technology that will significantly expand the application areas and open up new possibilities for UAVs, while also presenting new requirements for the robustness and reliability of the UAV swarming system. However, its complex and dynamic characteristics make it extremely challenging and uncertain to model such a system. In this study, to reach a full understanding of the swarming system, a modeling framework based on complex network theory is presented. First, the scope of work is identified from the point of view of control algorithms considering the dynamics and research novelty of the development of UAV swarming control strategy and three control structures consisting of three interdependent network layers are proposed. Second, three algorithms that systematically build the modeling framework considering all characteristics of the system are also developed. Finally, some network measurements are introduced by adjusting the fundamental ones into the UAV swarming system. The proposed framework is applied to a case study to illustrate the visualization models and estimate the statistical characteristics of the proposed networks with static and dynamic topology analysis. Furthermore, a simple demonstration of the robustness evaluation of the network is also presented. The networks obtained from this framework can be used to further analyze the robustness or reliability of a UAV swarming system in a high-confrontation battlefield environment the effect of cascading failure in ad-hoc network on system.
Product lifetime prediction is challenging when the product is subject to a time-varying operational environment. Most of the existing studies use some functions to explicitly specify the relationship between degradation parameters and environmental conditions so as to reveal how the degradation process evolves over time. However, in many applications, the assumptions needed for establishing these functions cannot be validated in engineering practice or they cannot accurately model the entire underlying degradation mechanism. In contrast to previous work, the focus of our study is placed on product degradation prognosis by implementing an ensemble learning method. This method combines the stochastic process modeling approach and the machine learning approach, taking advantage of these approaches to gain a more accurate and stable degradation prediction. The proposed method is demonstrated by some simulation examples and by a case study of lithium-ion battery accelerated degradation test. Both the simulation study and the real case verify the superiority of the proposed method. The case study indicates that the ensemble learning method can further help to effectively manage the energy storage and energy distribution of battery packs.
K E Y W O R D Sdegradation process, ensemble model, Li-ion battery, machine learning, stochastic process
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.