基于多组件系统的固定翼无人机维护政策设计及其案例研究

IF 6.7 1区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Guangshuai Liu , Xurui Li , Si Sun , Xing Zhao , Bailin Li
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引用次数: 0

摘要

目前,飞行器结构维护正经历着模式转变,从依赖日历时间和飞行周期的预防性维护转变为使用结构状态监测数据的基于状态的维护(CBM)。随着无人飞行器(UAV)系统越来越多地执行空中任务,这种转变显得尤为重要。然而,与针对单个组件的 CBM 不同,大多数无人机系统由多个相互关联的组件组成。从系统层面优化飞行前维护并确保检查的可追溯性仍然具有挑战性。本研究为无人机可靠性和稳定性多组件评估引入了一种多层决策策略,以加强服务状态监测和维护评估。该系统采用知识驱动方法,为多组件无人机开发了一个全面的维护框架。该系统可实现详细的损坏评估、有效管理、预测能力和优化策略。通过对知识模型、几何模型和决策模型进行集成和推理,该系统支持动态维护和持续迭代改进。为进一步降低系统复杂性,本文创建了一个风险光栅评估系统。在单个组件的框架内,建立了决策规则,以便在风险评估决定激活时,以最佳方式确定需要预防性维护的组件。此外,还定义了一个新的软故障阈值模型,以确定最佳维护决策变量。该模型结合了系统和子系统的一般知识,进一步优化了预测可靠性和经济依赖性。最后,通过固定翼无人机维护案例研究验证了该政策的有效性和可行性。结果表明,所提出的框架在飞机系统维护管理方面大有可为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design of a multi-component system-based fixed-wing unmanned aerial vehicle maintenance policy and its case study
At present, flight vehicle structure maintenance is undergoing a paradigm shift from preventive maintenance depending on calendar time and flight cycle to condition-based maintenance (CBM) using structural condition monitoring data. This shift is particularly relevant as unmanned aerial vehicle (UAV) systems increasingly undertake aerial missions. However, unlike CBM for single components, most UAV systems consist of multiple interconnected components. Optimizing pre-flight maintenance and ensuring traceability of inspections from a system-level perspective remains challenging. This study introduces a multi-layered decision-making policy for UAV reliability and stability multi-component evaluation, enhancing service condition monitoring and maintenance evaluation. The system adopts a knowledge-driven approach to develop a comprehensive maintenance framework for multi-component UAVs. It enables detailed damage assessment, effective management, predictive capabilities, and optimization strategies. By integrating and reasoning across knowledge-based, geometric, and decision-making models, the system supports dynamic maintenance and continuous iterative enhancements. To further reduce system complexity, this paper created a risk grating evaluation system. Within the framework of individual components, decision-making rules are established to optimally determine the components needing preventive maintenance when activated by decisions from risk assessments. Additionally, a new soft failure threshold model to determine the optimal maintenance decision variables is defined. This model incorporates general knowledge of the system and subsystems, further optimizing predictive reliability and economic dependency. Finally, the effectiveness and feasibility of this policy are validated through a fixed-wing UAV maintenance case study. The results demonstrate that the proposed framework holds significant promise for maintenance management in aircraft systems.
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来源期刊
Computers & Industrial Engineering
Computers & Industrial Engineering 工程技术-工程:工业
CiteScore
12.70
自引率
12.70%
发文量
794
审稿时长
10.6 months
期刊介绍: Computers & Industrial Engineering (CAIE) is dedicated to researchers, educators, and practitioners in industrial engineering and related fields. Pioneering the integration of computers in research, education, and practice, industrial engineering has evolved to make computers and electronic communication integral to its domain. CAIE publishes original contributions focusing on the development of novel computerized methodologies to address industrial engineering problems. It also highlights the applications of these methodologies to issues within the broader industrial engineering and associated communities. The journal actively encourages submissions that push the boundaries of fundamental theories and concepts in industrial engineering techniques.
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