非线性输出频域响应函数的自适应辨识算法及应用

韩海涛 1; 2; 曹建福 1; 马红光 2; 张家良 1

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非线性输出频域响应函数的自适应辨识算法及应用

更新时间：2025-07-09

- 非线性输出频域响应函数的自适应辨识算法及应用
- An Adaptive Identification Algorithm of Nonlinear Output Frequency Response Functions and Its Application
- 西安交通大学学报 2011年45卷第10期页码：77-81+87
- 作者机构：
  
  1. 西安交通大学机械制造系统工程国家重点实验室,西安,710049
  2. 第二炮兵工程学院101教研室,西安,710025
- 作者简介：
- 基金信息：
  
  : 2011-01-04. 作者简介: 韩海涛(1983-),男,博士生;曹建福(联系人),男,教授,博士生导师. 基金项目: 国家“863计划”资助项目(2008AA01Z126)
- DOI：
  中图分类号： TN47
- 网络首发：2011-10-10，
  
  纸质出版：2011
- 稿件说明：
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韩海涛 1, 2, 曹建福 1, 等. 非线性输出频域响应函数的自适应辨识算法及应用[J]. 西安交通大学学报, 2011,45(10):77-81+87.

An Adaptive Identification Algorithm of Nonlinear Output Frequency Response Functions and Its Application[J]. 2011, 45(10): 77-81+87.
韩海涛 1, 2, 曹建福 1, 等. 非线性输出频域响应函数的自适应辨识算法及应用[J]. 西安交通大学学报, 2011,45(10):77-81+87. DOI：

An Adaptive Identification Algorithm of Nonlinear Output Frequency Response Functions and Its Application[J]. 2011, 45(10): 77-81+87. DOI：

摘要

为解决非线性输出频域响应函数(NOFRF)模型用于模拟电路系统故障诊断时

传统辨识算法需多次激励计算过程耗时长的问题

提出了NOFRF的频域自适应辨识算法(NOFRF-BLMS).该算法构造了NOFRF的输入观测向量与核向量

从而可将NOFRF表示成一个伪线性结构.根据块最小均方(BLMS)原理及约束优化理论

推导出满足最小均方误差指标的NOFRF自适应辨识迭代计算公式

采用输入功率普迭代估算学习因子

由输出误差构造残差向量.NOFRF-BLMS通过在线学习方式

只需一次激励即可辨识出NOFRF

使辨识过程大幅度简化

缩短了辨识时间

具有更强的噪声抑制能力.实验结果表明

NOFRF-BLMS在相同的辨识精度下

耗时仅为传统算法的3%

且故障判断准确.

Abstract

A nonlinear output frequency response functions(NOFRF)frequency adaptive identification algorithm(NOFRF-BLMS)is proposed to deal with the problems that the conventional identification method of NOFRF needs multiple stimulus and costs long time when the model of NOFRF is applied to fault diagnosis of analog circuit system. Input observation vectors and kernel vectors are constructed by means of NOFRF-BLMS

which makes the model of NOFRF become a pseudo-linear combination structure. Then

NOFRF adaptive identification recursive computational formula

which satisfies the norm of least mean square error

is deduced based on block least mean square(BLMS)and constraint optimization theory. Input power is used to estimate recursive learning factors

and output error is used to construct residual error vectors. NOFRF is identified via online learning and only one stimulus is needed in NOFRF-BLMS which simplifies the procedure of identifying dramatically and shortens the time of identifying. NOFRF-BLMS is robust to noise. Experimental results indicate that NOFRF-BLMS costs only 3% of the identifying time of the conventional method

and the faults are correctly identified.

关键词

Keywords

references

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SUN Yongkui, CHEN Guangju, HUI Li. Analog circuits fault diagnosis using support vector machine [C]∥Proceedings of International Conference on Communication, Circuits and Systems. Piscataway, NJ, USA: IEEE, 2007:1003-1006.

YANG Chenglin, TIAN Shulin, LONG Bing, et al. Methods of handling the tolerance and test-point selection problem for analog-circuit fault diagnosis[J]. IEEE Transactions on Instrumentation and Measurement, 2011,60(1):1-10.

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PENG Zekai, LANG Zhiqiang, WOLTTERS C, et al. Feature extraction for damage detection in structures based on nonlinearity analysis [J]. Key Engineering Materials, 2009, 413(8):627-634.

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OGUNFUNMI A O, PETERSON A M. On the implementation of the frequency-domain LMS adaptive filter [J]. IEEE Transactions on Circuits and Systems: Ⅱ Analog and Digital Signal Processing, 1992, 39(5):318-322.

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曹建福,韩崇昭,方洋旺.非线性系统理论及应用[M].西安:西安交通大学出版社,2006:128-140.

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