Guangshuai Liu , Xurui Li , Si Sun , Xing Zhao , Bailin Li
{"title":"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","authors":"Guangshuai Liu , Xurui Li , Si Sun , Xing Zhao , Bailin Li","doi":"10.1016/j.cie.2024.110701","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55220,"journal":{"name":"Computers & Industrial Engineering","volume":"198 ","pages":"Article 110701"},"PeriodicalIF":6.7000,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Design of a multi-component system-based fixed-wing unmanned aerial vehicle maintenance policy and its case study\",\"authors\":\"Guangshuai Liu , Xurui Li , Si Sun , Xing Zhao , Bailin Li\",\"doi\":\"10.1016/j.cie.2024.110701\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":55220,\"journal\":{\"name\":\"Computers & Industrial Engineering\",\"volume\":\"198 \",\"pages\":\"Article 110701\"},\"PeriodicalIF\":6.7000,\"publicationDate\":\"2024-11-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers & Industrial Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0360835224008234\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Industrial Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0360835224008234","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
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.
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.