航空发动机燃油控制系统故障诊断技术研究进展与挑战
Research Status and Challenges on Fault Diagnosis Methodology for Fuel Control System of Aero-engine
严如强 1许文纲 1王志颖 1朱启翔 1周峥 1赵志斌 1孙闯 1王诗彬 1陈雪峰 1张军辉 2徐兵2
作者信息
- 1. 西安交通大学机械工程学院 西安 710049
- 2. 浙江大学流体动力与机电系统国家重点实验室 杭州 310027
- 折叠
摘要
随着发动机性能要求的不断提升,燃油控制系统服役的工况变得越来越恶劣、边界条件越来越复杂.燃油泵固有压力脉动与管路、活门的流固耦合振动,密封圈腐蚀或老化导致的泄漏,油液污染或润滑油失效而产生的磨损加剧等均会造成燃油控制系统的致命故障.同时,燃油控制系统具有少测点、变工况、强干扰及强非线性等特点,导致该领域对故障诊断技术存在迫切需求,同时也面临巨大挑战.为推动故障诊断技术在燃油控制系统领域的发展,总结燃油控制系统的特点与常见故障,并在此基础上介绍故障诊断技术的主要方法与分类.进一步从液压元件互换性角度,概述基于物理模型、信号处理和人工智能诊断方法在燃油控制系统关键部件中的研究现状.最后指出燃油控制系统故障诊断技术存在的挑战与机遇.
Abstract
As engine performance requirements continue to improve,the operating conditions of the fuel control system have become harsher and the boundary conditions have become more complex.There are various causes of fatal failures in fuel control systems,including inherent pressure pulsation of the fuel pump and fluid-solid coupling vibration of pipelines and valves,leakage caused by corrosion or aging of sealing rings,increased wear due to oil contamination or lubricating oil failure,etc.At the same time,the fuel control system has the characteristics of few measuring points,variable operating conditions,strong interference,and strong nonlinearity.Therefore,there is an urgent need for fault diagnosis technology in this field while facing huge challenges.In order to promote the development of fault diagnosis technology in the field of fuel control systems,this study reviews the main methods and classifications of fault diagnosis technology after summarizing the characteristics and common faults of the fuel control system.Furthermore,from the perspective of hydraulic component interchangeability,the current research status of key components of fuel control systems is summarized based on physical models,signal processing and artificial intelligence diagnostic methods.Finally,the challenges and opportunities existing in fuel control system fault diagnosis technology are pointed out.
关键词
燃油控制系统/故障诊断/物理模型/信号处理/人工智能Key words
fuel control system/fault diagnosis/physical model/signal processing/artificial intelligence引用本文复制引用
基金项目
国家自然科学基金重点项目(51835009)
出版年
2024
NETL
NSTL
万方数据