CN105389427A - 基于自适应粒子滤波的航空发动机气路部件故障检测方法 - Google Patents
基于自适应粒子滤波的航空发动机气路部件故障检测方法 Download PDFInfo
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- CN105389427A CN105389427A CN201510712090.1A CN201510712090A CN105389427A CN 105389427 A CN105389427 A CN 105389427A CN 201510712090 A CN201510712090 A CN 201510712090A CN 105389427 A CN105389427 A CN 105389427A
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- G—PHYSICS
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- G06F30/367—Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M15/00—Testing of engines
- G01M15/04—Testing internal-combustion engines
- G01M15/05—Testing internal-combustion engines by combined monitoring of two or more different engine parameters
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Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105911975A (zh) * | 2016-04-14 | 2016-08-31 | 南京航空航天大学 | 一种航空发动机气路故障融合诊断方法 |
CN106503746A (zh) * | 2016-11-03 | 2017-03-15 | 哈尔滨工业大学 | 一种基于性能偏移量的航空发动机故障诊断方法 |
CN108062428A (zh) * | 2017-10-30 | 2018-05-22 | 南京航空航天大学 | 一种涡扇发动机在线部件故障诊断方法及*** |
CN108829928A (zh) * | 2018-05-08 | 2018-11-16 | 南京航空航天大学 | 一种涡轴发动机自适应部件级仿真模型构建方法 |
CN109657329A (zh) * | 2018-12-14 | 2019-04-19 | 重庆大学 | 基于改进自适应粒子滤波的传感器故障和结构损伤识别方法 |
CN110286607A (zh) * | 2019-07-22 | 2019-09-27 | 中国人民解放军军事科学院国防科技创新研究院 | 一种航天器姿控喷管故障数据生成***及方法 |
CN110991295A (zh) * | 2019-11-26 | 2020-04-10 | 电子科技大学 | 一种基于一维卷积神经网络的自适应故障诊断方法 |
CN113917434A (zh) * | 2021-10-08 | 2022-01-11 | 陕西科技大学 | 一种基于代价参考粒子滤波器组的检测前跟踪方法 |
Citations (1)
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CN104200062A (zh) * | 2014-08-04 | 2014-12-10 | 南京航空航天大学 | 一种航空发动机气路故障的融合诊断方法 |
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2015
- 2015-10-28 CN CN201510712090.1A patent/CN105389427B/zh active Active
Patent Citations (1)
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CN104200062A (zh) * | 2014-08-04 | 2014-12-10 | 南京航空航天大学 | 一种航空发动机气路故障的融合诊断方法 |
Non-Patent Citations (5)
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FENG LU 等: "Gas Path Health Monitoring for a Turbofan Engine Based on a Nonlinear Filtering Approach", 《ENERGIES》 * |
冯敏: "涡扇发动机气路健康参数滤波估计方法研究", 《中国优秀硕士学位论文全文数据库工程科技II辑》 * |
刘志仓: "基于粒子滤波的非线性***故障诊断与预测方法研究", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
鲁峰 等: "航空发动机部件性能故障融合诊断方法研究", 《航空学报》 * |
黄金泉 等: "航空发动机部件性能故障融合诊断方法研究", 《航空动力学报》 * |
Cited By (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105911975B (zh) * | 2016-04-14 | 2018-08-03 | 南京航空航天大学 | 一种航空发动机气路故障融合诊断方法 |
CN105911975A (zh) * | 2016-04-14 | 2016-08-31 | 南京航空航天大学 | 一种航空发动机气路故障融合诊断方法 |
CN106503746A (zh) * | 2016-11-03 | 2017-03-15 | 哈尔滨工业大学 | 一种基于性能偏移量的航空发动机故障诊断方法 |
CN108062428A (zh) * | 2017-10-30 | 2018-05-22 | 南京航空航天大学 | 一种涡扇发动机在线部件故障诊断方法及*** |
CN108829928A (zh) * | 2018-05-08 | 2018-11-16 | 南京航空航天大学 | 一种涡轴发动机自适应部件级仿真模型构建方法 |
CN109657329B (zh) * | 2018-12-14 | 2022-02-22 | 重庆大学 | 基于改进自适应粒子滤波的传感器故障和结构损伤识别方法 |
CN109657329A (zh) * | 2018-12-14 | 2019-04-19 | 重庆大学 | 基于改进自适应粒子滤波的传感器故障和结构损伤识别方法 |
CN110286607A (zh) * | 2019-07-22 | 2019-09-27 | 中国人民解放军军事科学院国防科技创新研究院 | 一种航天器姿控喷管故障数据生成***及方法 |
CN110286607B (zh) * | 2019-07-22 | 2020-04-03 | 中国人民解放军军事科学院国防科技创新研究院 | 一种航天器姿控喷管故障数据生成***及方法 |
CN110991295A (zh) * | 2019-11-26 | 2020-04-10 | 电子科技大学 | 一种基于一维卷积神经网络的自适应故障诊断方法 |
CN110991295B (zh) * | 2019-11-26 | 2022-05-06 | 电子科技大学 | 一种基于一维卷积神经网络的自适应故障诊断方法 |
CN113917434A (zh) * | 2021-10-08 | 2022-01-11 | 陕西科技大学 | 一种基于代价参考粒子滤波器组的检测前跟踪方法 |
CN113917434B (zh) * | 2021-10-08 | 2024-05-28 | 陕西科技大学 | 一种基于代价参考粒子滤波器组的检测前跟踪方法 |
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Effective date of registration: 20210517 Address after: 518000 509, Dongfang Science and technology building, 16 Keyuan Road, science and Technology Park community, Yuehai street, Nanshan District, Shenzhen City, Guangdong Province Patentee after: Shenzhen Gao Hang Intellectual Property Operation Co.,Ltd. Address before: No. 29, Qinhuai District, Qinhuai District, Nanjing, Jiangsu Patentee before: Nanjing University of Aeronautics and Astronautics Effective date of registration: 20210517 Address after: 201100 room 1202, South Building, building 1, 951 Jianchuan Road, Minhang District, Shanghai Patentee after: Shanghai hangshu Intelligent Technology Co.,Ltd. Address before: 518000 509, Dongfang Science and technology building, 16 Keyuan Road, science and Technology Park community, Yuehai street, Nanshan District, Shenzhen City, Guangdong Province Patentee before: Shenzhen Gao Hang Intellectual Property Operation Co.,Ltd. |
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