奇异谱分析在故障时间序列分析中的应用

王鑫; 吴际; 刘超; 牛文生; 张华; 张奎

doi:10.13700/j.bh.1001-5965.2015.0712

奇异谱分析在故障时间序列分析中的应用

doi: 10.13700/j.bh.1001-5965.2015.0712

王鑫¹,
吴际^1, ,,
刘超¹,
牛文生^1,2,
张华¹,
张奎¹

1.
北京航空航天大学计算机学院, 北京 100083
2. 中航工业西安航空计算技术研究所, 西安 710068

基金项目: 中国民用航空专项研究项目（MJ-S-2013-10）

详细信息

作者简介:
王鑫,男,博士研究生。主要研究方向:数据驱动技术。Tel.:15810128162,E-mail:wangxin_buaa@edu.com.cn;吴际,男,博士,副教授,硕士生导师。主要研究方向:模型驱动、软件可靠性分析。Tel.:010-82317624,E-mail:wuji@buaa.edu.cn;刘超,男,博士,教授,博士生导师。主要研究方向:软件工程、软件测试。Tel.:010-82317641,E-mail:liuchao@buaa.edu.cn

通讯作者:
吴际,Tel.:010-82317624,E-mail:wuji@buaa.edu.cn

中图分类号: O213.2;V37;TB39
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- 被引次数: 0
出版历程
- 收稿日期: 2015-11-02
- 修回日期: 2016-02-29
- 网络出版日期: 2016-11-20

Application of singular spectrum analysis to failure time series analysis

WANG Xin¹,
WU Ji^{1
, ,},
LIU Chao¹,
NIU Wensheng^1,2,
ZHANG Hua¹,
ZHANG Kui¹

1.
School of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
2. Xi'an Aeronautical Computing Technique Research Institute, Aviation Industry Corporation of China, Xi'an 710068, China

摘要

摘要: 由于日益增长的飞行安全和飞机维护质量需求，飞机使用可靠性已经成为一个重要的研究领域。从某航空公司波音737飞机使用过程中现场所记录的18年的故障数据出发，应用奇异谱分析（SSA）方法，对故障时间序列进行了建模和预测，进一步以预测结果的均方根误差（RMSE）最小为优化目标对SSA模型参数进行了优选。在此基础上，提出了一种更为广泛的模型组合方法和实现算法，这种方法采用不同的时间序列模型来构造SSA分解出的趋势、周期和残差等成分。通过与三次指数平滑（Holt-Winters）、自回归移动平均（ARIMA）2种时间序列模型的实验结果对比，SSA及其参数优选和模型组合方法在故障时间序列分析中具有更好的拟合和预测精度。
- 奇异谱分析(SSA) /
- 故障时间序列 /
- 预测 /
- 参数优选 /
- 模型组合
Abstract: Due to significant industrial demands toward flight safety andairplane maintenance quality, improving airplane's reliability in usage stage has become an important activity and the research domain is rapidly evolving. In this paper, eighteen years' field data gathered from the maintenance phase of two Boeing 737 aircrafts are prepared as time-to-failure series. Then singular spectrum analysis (SSA) is usedto cope with this data for modeling and forecasting. Furthermore, a SSA parameter optimization algorithm is proposed by minimizing root mean square error (RMSE) of the prediction results. Based on this,a broader method of model combination is raised by utilizing different time series models to the components obtained from SSA decomposition, which represent trend, period, residuals, etc.The combination model and detailed algorithm are designed. The experimental results are compared with those of cubic exponential smoothing (Holt-Winters) and autoregressive integrated moving average (ARIMA), which verifies that the proposed models and algorithms have better fitting and prediction accuracyin failure time series analysis.
- singular spectrum analysis (SSA) /
- failure time series /
- prediction /
- parameter optimization /
- model combination

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