基于ImOS-ELM的航空发动机传感器故障自适应诊断技术

doi:10.7527/S1000-6893.2013.0222

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基于ImOS-ELM的航空发动机传感器故障自适应诊断技术

李业波¹, 李秋红¹, 王健康², 黄向华¹, 赵永平³

1. 南京航空航天大学能源与动力学院, 江苏南京 210016;
2. 中国空间技术研究院北京控制工程研究所, 北京 100190;
3. 南京理工大学机械工程学院, 江苏南京 210094

收稿日期:2012-11-29 修回日期:2013-04-22 出版日期:2013-10-25 发布日期:2013-04-26
通讯作者: 李秋红,Tel.: 025-84892200-2606 E-mail: lqh203@nuaa.edu.cn E-mail:lqh203@nuaa.edu.cn
作者简介:李业波男, 博士研究生。主要研究方向: 航空发动机建模、控制及故障诊断。 Tel: 025-84892200-2606 E-mail: liyb1985@gmail.com;李秋红女, 博士, 副教授, 硕士生导师。主要研究方向: 航空发动机建模、控制及故障诊断。 Tel: 025-84892200-2606 E-mail: lqh203@nuaa.edu.cn
基金资助:
国家自然科学基金(51006052);航空科学基金(20110652003);中央高校基本科研业务费专项基金(NZ2012104);江苏省2012年度普通高校研究生科研创新计划(CXZZ12_0169)

Sensor Fault Adaptive Diagnosis of Aero-engines Based on ImOS-ELM

LI Yebo¹, LI Qiuhong¹, WANG Jiankang², HUANG Xianghua¹, ZHAO Yongping³

1. College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
2. Beijing Institute of Control Engineering, China Academy of Space Technology, Beijing 100190, China;
3. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Received:2012-11-29 Revised:2013-04-22 Online:2013-10-25 Published:2013-04-26
Supported by:
National Natural Science Foundation of China (51006052);Aeronautical Science Foundation of China (20110652003);Fundamental Research Funds for Central Universities (NZ2012104);Graduate Student Research and Innovation Program of Jiangsu Province (CXZZ12_0169)

摘要/Abstract

摘要：

针对在线贯序极端学习机(OS-ELM)算法矩阵容易陷入奇异和病态、在算法开始阶段不具有预测能力的问题,结合选择策略提出一种改进的OS-ELM (ImOS-ELM)算法。该算法通过引入正则化因子,消除了矩阵奇异和病态的问题,提高了预测精度,并使得算法能够在初始阶段就具有预测能力。同时以泛化能力为判断依据,通过选择策略对输出权值进行选择性地更新,该算法在很大程度上缩短了训练时间。为了验证算法的有效性,用时间序列数据进行了仿真测试验证。最后,将ImOS-ELM算法应用于航空发动机传感器故障的诊断与隔离。仿真结果表明,该算法能够对航空发动机双传感器偏置故障和单传感器漂移故障进行有效地诊断与隔离,并具有较高的预测精度和实时性。

关键词: 极端学习机, 正则化, 选择策略, 航空发动机, 传感器, 故障诊断与隔离

Abstract:

On-line sequential extreme learning machine (OS-ELM) algorithm is likely to fall into matrix singularity and ill-posedness, and it has no predictive ability at the beginning stage of training. Hence, in this paper, an improved scheme with selection strategy, named improved OS-ELM (ImOS-ELM) algorithm, is proposed. This algorithm overcomes matrix singularity and ill-posedness by regularization, which can improve the predicting accuracy and achieve predictive ability from the start stage of training. Meantime, the output layer weight vector is updated selectively based on generalization capability, and it reduces considerably the mean training time of the algorithm. In order to verify the effectiveness of the proposed algorithm, simulation tests are carried out using time series data, which show that the proposed algorithm achieves higher accuracy and faster speed. Finally, ImOS-ELM algorithm is applied to the sensor fault detection and isolation of an aero-engine. The simulation results show that the sensor faults diagnosis method using the proposed algorithm is able to detect and isolate faults of double-sensor failures and single-sensor drift, which also proves the validity and feasibility of the proposed algorithm.

Key words: extreme learning machine, regularization, selection strategy, aero-engine, sensor, fault detection and isolation

中图分类号:

V233.7

李业波, 李秋红, 王健康, 黄向华, 赵永平. 基于ImOS-ELM的航空发动机传感器故障自适应诊断技术[J]. 航空学报, 2013, 34(10): 2316-2324.

LI Yebo, LI Qiuhong, WANG Jiankang, HUANG Xianghua, ZHAO Yongping. Sensor Fault Adaptive Diagnosis of Aero-engines Based on ImOS-ELM[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2013, 34(10): 2316-2324.

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E-mail：hkxb@buaa.edu.cn

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主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于ImOS-ELM的航空发动机传感器故障自适应诊断技术

Sensor Fault Adaptive Diagnosis of Aero-engines Based on ImOS-ELM

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