摘要
针对航空发动机退化导致的缓变故障诊断问题,提出了一种基于相关时间规整算法的诊断模型,该模型通过挖掘发动机退化过程中过渡信息,根据退化数据中发动机状态变化特征来识别故障模式。通过仿真数据和实际案例数据实验证明,在监测数据满足1~2个飞行循环间隔前提下,该模型能够区分发动机正常状态和故障状态,对发动机本体的缓变故障能够定位到部件级,平均G-mean值为0.948 7,拥有较好的鲁棒性和准确度,为民航发动机健康管理提供了一种可行的工程方法。
Aiming at the degradation-caused gradual changing fault diagnosis of aero-engine,this paper proposed a diagnosis model based on the canonical time warping（ CTW） algorithm,which can discriminate the fault pattern based on the transition features from degradation data via mining the transition information of degradation. We conducted the proposed model on both simulated data and real data. The experimental results show that the proposed model can recognize the normal state and fault state and locate the gradual changing fault in component level,whose G-meanvalue is 0. 948 7 in the premise that the flight cycle interval is 1 or 2. The proposed model provides a feasible engineering method for civil aviation aero-engine health management,and has good robustness and high accuracy.
作者
周媛
左洪福
刘鹏鹏
Zhou Yuan Zuo Hongfu Liu Pengpeng(College of Electronic and hfformation Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Naujing 210016, China Systems Engineering Research Institute, China State Shipbuilding Corporation, Beijing 100094, China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2017年第9期1358-1364,共7页
Journal of Electronic Measurement and Instrumentation
基金
江苏高校品牌专业建设工程(1181081501003)
国家自然科学基金(61403198)
江苏省自然科学基金(BK20140827)资助项目
关键词
航空发动机
缓变故障
故障诊断
相关时间规整
并发故障
aero-engine
gradual changing fauh
fault diagnosis
the canonical time warping algorithm
concurrent fault
