基于故障可诊断性量化评价的传感器优化配置方法研究

蒋栋年; 李炜; 王君; 孙晓静

doi:10.16383/j.aas.2017.c160776

基于故障可诊断性量化评价的传感器优化配置方法研究

doi: 10.16383/j.aas.2017.c160776

蒋栋年^1,2,3,,
李炜^1,2,3, ,,
王君^1,2,3,,
孙晓静^4,

1.
兰州理工大学电气工程与信息工程学院兰州 730050
2.
甘肃省工业过程先进控制重点实验室兰州 730050
3.
兰州理工大学国家级电气与控制工程实验教学中心兰州 730050
4.
兰州电源车辆研究所有限公司兰州 730050

基金项目:

甘肃省自然科学基金 1506RJYA108

国家自然科学基金 61370037

国家自然科学基金 61763027

国家自然科学基金 61463030

详细信息

作者简介:
蒋栋年  兰州理工大学讲师, 博士研究生.主要研究方向为故障诊断与容错控制, 控制系统故障可诊断性评价与设计.E-mail:dreamjdn@126.com

王君  兰州理工大学教授.2013年获得兰州理工大学博士学位.主要研究方向为故障诊断与容错控制.E-mail:wangj31901@163.com

孙晓静   兰州电源车辆研究所有限公司教授级高级工程师.2013年获得兰州理工大学博士学位.主要研究方向为移动电站控制系统的研究和设计.E-mail:sxj12382@163.com

通讯作者:
李炜兰州理工大学教授.主要研究方向为故障诊断与容错控制, 预测维护.本文通信作者.E-mail:liwei@lut.cn

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出版历程
- 收稿日期: 2016-11-17
- 录用日期: 2017-05-06
- 刊出日期: 2018-06-20

Research on Sensor Optimal Placement Method Using Quantitative Evaluation of Fault Diagnosability

JIANG Dong-Nian^{1,2,3
,},
LI Wei^{1,2,3
, ,},
WANG Jun^{1,2,3
,},
SUN Xiao-Jing^4
,

1.
College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050
2.
Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou 730050
3.
National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou 730050
4.
Lanzhou Power Supply Vehicle Research Institute Co., LTD, Lanzhou 730050

Funds:

Gansu Provincial Natural Science Foundation 1506RJYA108

National Natural Science Foundation of China 61370037

National Natural Science Foundation of China 61763027

National Natural Science Foundation of China 61463030

More Information

Author Bio:
Lecturer and Ph. D. candidate at Lanzhou University of Technology. His research interest covers fault diagnosis and tolerant control, fault diagnosability evaluation and design for control systems

Professor at Lanzhou University of Technology. She received her Ph. D. degree from Lanzhou University of Technology in 2013. Her research interest covers fault diagnosis and tolerant control

Professorate senior engineer at Lanzhou Power Supply Vehicle Research Institute Co., LTD. She received her Ph. D. degree from Lanzhou University of Technology in 2013. Her research interest covers mobile power station control system research and design

Corresponding author: LI Wei Professor at Lanzhou University of Technology. Her research interest covers fault diagnosis and tolerant control, and predictive maintenance. Corresponding author of this paper

摘要

摘要: 提出了一种基于故障可诊断性量化评价的传感器优化配置方法.针对可能发生故障的非线性系统，首先，基于K-L散度思想，通过计算故障情形下残差概率密度函数的差异度，得到了系统不同故障下故障可检测性和可分离性的量化指标，由于稀疏内核密度估计和蒙特卡洛算法的引入，克服了K-L散度计算中残差概率密度函数难以估计和非线性结构的K-L散度计算复杂度高的困难；其次，以故障可诊断性的定量评价为基础，借助于动态规划方法给出了系统满足期望故障可诊断性的传感器最优集合；最后，通过数值仿真和实体实验仿真验证了文中方法在故障诊断系统传感器优化配置中的有效性.
- 故障可诊断性 /
- 量化评价 /
- K-L散度 /
- 传感器配置 /
- 动态规划
Abstract: A method of sensor optimal placement using quantitative evaluation of fault diagnosability is proposed. With the idea of K-L divergence, using quantificational indices of fault detectability and isolability under different fault conditions are obtained by calculating the difference degree of residual probability density function. Sparse kernel density estimation and Monte Carlo algorithm are introduced to overcome the difficulty of estimating the residual probability density function of K-L divergence calculation and high complexity of K-L divergence computation of nonlinear structure. Secondly, with the quantitative evaluation of fault diagnosability, the optimal set of sensors satisfying the expected fault diagnosability is given by means of dynamic programming method. Finally, the validity of the proposed method is validated through numerical simulation and physical experiment simulation in a sensor optimal configuration of fault diagnosis system.
- Fault diagnosability /
- quantitative evaluation /
- Kullback-Leibler divergence /
- sensor placement /
- dynamic programming
注释:

1) 本文责任编委吴立刚

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本文责任编委吴立刚

图 1 不同分布集合中概率密度函数的距离示意图

Fig. 1 The distance of probability density function in different distribution sets

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图 2 基于动态规划法的传感器配置优化过程

Fig. 2 Sensor optimal placement based on dynamic programming

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图 3 车辆电源系统结构图

Fig. 3 System structure diagram of vehicle power supply

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图 4 车辆电源模块关系图

Fig. 4 Module diagram of vehicle power supply

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图 5 基于动态规划法的传感器配置优化过程

Fig. 5 Sensor optimal placement based on dynamic programming

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表 1 故障可诊断性分析

Table 1 Fault diagnosability analysis

	FD	$f_{1}$	$f_{2}$	$f_{3}$	$f_{4}$
$f_{1}$	$\times$	0	0	$\times$	$\times$
$f_{2}$	$\times$	0	0	$\times$	$\times$
$f_{3}$	$\times$	$\times$	$\times$	0	$\times$
$f_{4}$	$\times$	$\times$	$\times$	$\times$	0

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表 2 故障可诊断性量化评价

Table 2 Quantitative evaluation of fault diagnosability

$\{x_1, x_3\}$	FD	$f_{1}$	$f_{2}$	$f_{3}$	$f_{4}$
$f_{1}$	0.036	0	0	0.058	0.061
$f_{2}$	0.054	0	0	0.099	0.054
$f_{3}$	0.008	0.051	0.074	0	0.116
$f_{4}$	0.093	0.059	0.053	0.129	0

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表 3 故障可诊断性量化评价

Table 3 Quantitative evaluation of fault diagnosability

$\{x_{2}, x_{4} \}$	FD	$f_{1}$	$f_{2}$	$f_{3}$	$f_{4}$
$f_{1}$	0.055	0	0	0.089	0.083
$f_{2}$	0.086	0	0	0.098	0.063
$f_{3}$	0.122	0.133	0.126	0	0.285
$f_{4}$	0.232	0.080	0.059	0.180	0

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表 4 非线性系统故障可诊断性量化评价

Table 4 Quantitative evaluation of fault diagnosability for nonlinear systems

	FD	$f_{1}$	$f_{2}$	$f_{3}$	$f_{4}$
$f_{1}$	0.660	0	0	1.412	1.564
$f_{2}$	0.671	0	0	1.273	2.229
$f_{3}$	1.532	1.518	1.936	0	0.330
$f_{4}$	1.510	1.617	2.520	0.202	0

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表 5 非线性系统故障可诊断性量化评价

Table 5 Quantitative evaluation of fault diagnosability for nonlinear systems

{$x_{2}$, $x_{3}$, $x_{4}$}	FD	$f_{1}$	$f_{2}$	$f_{3}$	$f_{4}$
$f_{1}$	0.460	0	0	1.142	0.465
$f_{2}$	0.423	0	0	1.103	1.110
$f_{3}$	1.067	1.174	1.248	0	0.143
$f_{4}$	1.028	0.379	1.039	0.156	0

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表 6 车辆电源常见故障描述

Table 6 Common fault description of vehicle power supply

故障	故障描述
$f_{1}$	发电机失磁
$f_{2}$	柴油滤清器堵塞
$f_{3}$	调速器调节失灵
$f_{4}$	发动机高温
$f_{5}$	系统超载
$f_{6}$	励磁模块故障
$f_{7}$	喷油嘴故障

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表 7 故障可诊断性定性评价

Table 7 Qualitative evaluation of fault diagnosability

	$r_{1}$	$r_{2}$	$r_{3}$	$r_{4}$	$r_{5}$	$r_{6}$	$r_{7 }$
$f_{1}$	0	0	0	1	0	1	0
$f_{2}$	0	0	0	0	0	1	0
$f_{3}$	1	0	0	0	1	0	1
$f_{4}$	0	0	1	0	0	1	0
$f_{5}$	0	1	1	0	0	0	0
$f_{6}$	1	1	0	0	0	1	0
$f_{7}$	0	0	0	0	0	1	0

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表 8 故障可诊断性量化评价

Table 8 Quantitative evaluation of fault diagnosability

	FD	$f_{1}$	$f_{2}$	$f_{3}$	$f_{4}$	$f_{5}$	$f_{6}$	$f_{7 }$
$f_{1}$	0.3462	0	0.1298	0.8978	0.1290	0.1432	0.2765	0.0988
$f_{2}$	0.4387	0.1304	0	0.8070	0.9908	0.1435	0.2787	0
$f_{3}$	0.2122	0.9029	0.7865	0	0.7434	0.4634	0.4172	0.8432
$f_{4}$	0.3456	0.1300	0.8990	0.7321	0	0.6432	0.7432	0.8432
$f_{5}$	0.6783	0.1765	0.1543	0.4764	0.7325	0	0.3434	0.1088
$f_{6}$	0.5435	0.2910	0.2898	0.4278	0.7000	0.3299	0	0.5898
$f_{7}$	0.7646	0.0910	0	0.1022	0.7853	0.0987	0.5786	0

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