CN116221903A - 一种在线主动诊断故障的智能健康管理方法 - Google Patents

一种在线主动诊断故障的智能健康管理方法 Download PDF

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CN116221903A
CN116221903A CN202211200486.4A CN202211200486A CN116221903A CN 116221903 A CN116221903 A CN 116221903A CN 202211200486 A CN202211200486 A CN 202211200486A CN 116221903 A CN116221903 A CN 116221903A
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陈斌
李雪
陈蕾
瞿遂春
张小倩
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    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24HEATING; RANGES; VENTILATING
    • F24FAIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24HEATING; RANGES; VENTILATING
    • F24FAIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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    • F24FAIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24HEATING; RANGES; VENTILATING
    • F24FAIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00Control or safety arrangements
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    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24HEATING; RANGES; VENTILATING
    • F24FAIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00Control or safety arrangements
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    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24HEATING; RANGES; VENTILATING
    • F24FAIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00Control inputs relating to air properties
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Abstract

本发明提供了一种在线主动诊断故障的智能健康管理方法,属于故障诊断技术领域。解决了微小故障因幅值较小不易被及时诊断的问题。其技术方案为:包括以下步骤:a.基于多性能指标权衡优化思想设计故障灵敏性观测器组,使得每个故障灵敏性观测器的输出残差信号对某一特定故障灵敏,同时对其他故障和干扰鲁棒;b.设计智能切换管理机制驱动观测器组主动有序切换;c.在观测器切换过程中,通过阈值法设计逻辑决策主动诊断微小故障。本发明的有益效果为:本发明不仅可以保证无故障情况下系统的良好性能,而且还可以及时检测出系统中的微小故障,并实现系统残差信号只对某一特定故障灵敏而对剩余其它故障均鲁棒的目的。

Description

一种在线主动诊断故障的智能健康管理方法
技术领域
本发明涉及故障诊断技术领域,尤其涉及一种在线主动诊断故障的智能健康管理方法。
背景技术
随着现代工程系统的规模逐渐扩大、功能日益增强,系统中发生的故障,尤其是隐蔽性较强的微小故障,如果不能被及时的检测和有效的处理,不仅会造成环境破坏和经济损失,甚至还可能导致人员伤亡。而被动式故障诊断通过使用系统的输入、输出信号对故障进行获取、分析和处理,但微小故障因其幅值较小,由微小故障引起的系统变化和其它干扰、噪声或模型不确定性带来的系统变化往往不易区分,同时系统中反馈控制器的存在还会掩盖因故障引起的系统异常,进而导致系统输入、输出信号中包含的故障信息不充分,最终使得被动式故障诊断方法对微小故障的诊断效果不佳。
如何解决上述技术问题为本发明面临的课题。
发明内容
本发明的目的在于提供一种在线主动诊断故障的智能健康管理方法,该方法被提出用于解决微小故障因幅值较小不易被及时诊断的问题,其属于主动式故障诊断,它可以通过主动增强特定故障在系统输入输出信号中的表现来使故障特征更明显,进而使微小故障更易被诊断,该方法不仅可以保证无故障情况下系统的良好性能,而且还可以及时检测出系统中的微小故障,并实现系统残差信号只对某一特定故障灵敏而对剩余其它故障均鲁棒的目的。
为了实现上述发明目的,本发明采用技术方案具体为:一种在线主动诊断故障的智能健康管理方法,包括如下步骤:
a.基于多性能指标权衡优化思想设计故障灵敏性观测器组,使得每个故障灵敏性观测器的输出残差信号对某一特定故障灵敏,同时对其他故障和干扰鲁棒;
b.设计智能切换管理机制驱动观测器组主动有序切换,使得观测器组生成的状态估计值可用于反馈控制来保证整体系统稳定;
c.在观测器切换过程中,通过阈值法设计逻辑决策主动诊断微小故障。
所述步骤a中设计的故障灵敏性观测器如下:
Figure BDA0003872262310000011
其中,
Figure BDA0003872262310000012
yi(t)∈Rs、/>
Figure BDA0003872262310000013
分别是系统的状态估计、输出信号和输出估计,ui(t)∈Rm是系统的控制输入信号,ri(t)∈Rs是系统的残差信号,A、B、C是适维的系统矩阵,L0为极点配置法获取的标称观测器增益矩阵,而Li∈Rn×s,i=1,2,…N为待设计的故障灵敏性观测器增益矩阵,并满足/>
Figure BDA0003872262310000021
其中,:,表示取/>
Figure BDA0003872262310000022
的所有行,1:s表示取/>
Figure BDA0003872262310000023
的第1列至第s列,即Li为/>
Figure BDA0003872262310000024
的前s列;/>
Figure BDA0003872262310000025
的设计需要满足残差信号与特定故障fi的灵敏性指标||ri(t)||2>βi||fi(t)||2以及残差信号与扰动d(t)、剩余其他故障fj的鲁棒性指标
Figure BDA0003872262310000026
即/>
Figure BDA0003872262310000027
通过如下矩阵不等式获得:
Figure BDA0003872262310000028
Figure BDA0003872262310000029
Figure BDA00038722623100000210
其中,符号“*”表示矩阵对称部分,
Figure BDA00038722623100000211
Figure BDA00038722623100000212
Ed、Fd、/>
Figure BDA00038722623100000213
分别是系统状态干扰矩阵、输出干扰矩阵和故障矩阵,I为适维的单位阵,正定对称矩阵Pi=Pi T>0,α1、α2、λi、γi、βi均为任意的正标量,且βi 2=α2i
所述步骤b中可以使观测器主动有序切换的智能切换管理机制如下:
Lgh=Lk+σ(t)[I-σ(t)(I-H)]-1H(Lk+1-Lk),g,h∈{0,1,…,N}
其中,
Figure BDA00038722623100000214
且Hk T=Hk>0、Hk+1 T=Hk+1>0,I为适维的单位阵,0≤σ(t)≤1为切换信号,具体描述为:
Figure BDA0003872262310000031
上述主动有序切换的观测器生成的状态估计值
Figure BDA0003872262310000032
可以用于组成状态反馈控制器/>
Figure BDA0003872262310000033
其中,F∈Rm×n是由LQR方法获取的状态反馈控制增益。
所述步骤c中设计的故障诊断决策逻辑如下:
Figure BDA0003872262310000034
其中,残差评估函数为
Figure BDA0003872262310000035
Ω为移动时间窗口,阈值为
Figure BDA0003872262310000036
与现有技术相比,本发明的有益效果为:
1)本发明的系统无故障时可以保持良好的性能,而在系统发生故障时,可以及时检测出系统的微小故障,具有较好的故障检测能力;
2)本发明的故障灵敏性观测器生成的残差信号可以在满足只对某一特定微小故障灵敏的同时又对剩余所有故障和干扰均鲁棒。
附图说明
附图用来提供对本发明的进一步理解,并且构成说明书的一部分,与本发明的实施例一起用于解释本发明,并不构成对本发明的限制。
图1为本发明的故障诊断的智能健康管理方法示意图。
图2为本发明一个周期的切换信号图。
图3为本发明实施例中无故障情况下,四个房间的输出信号图。
图4为本发明实施例中第一个房间风阀出现微小故障时,四个房间的残差信号图;
图5为本发明实施例中第二个房间风阀出现微小故障时,四个房间的残差信号图;
图6为本发明实施例中第三个房间风阀出现微小故障时,四个房间的残差信号图;
图7为本发明实施例中第四个房间风阀出现微小故障时,四个房间的残差信号图。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白,以下结合附图及实施例,对本发明进行进一步详细说明。当然,此处所描述的具体实施例仅用以解释本发明,并不用于限定本发明。
实施例1
参见图1至图7,本实施例提供其技术方案为,一种在线主动诊断故障的智能健康管理方法,包括如下步骤:
步骤a:基于多性能指标权衡优化思想设计故障灵敏性观测器组,使得每个故障灵敏性观测器的输出残差信号对某一特定故障灵敏,同时对其他故障和干扰鲁棒;
所述故障灵敏性观测器如下:
Figure BDA0003872262310000041
/>
其中,
Figure BDA0003872262310000042
yi(t)∈Rs、/>
Figure BDA0003872262310000043
分别是系统的状态估计、输出信号和输出估计,ui(t)∈Rm是系统的控制输入信号,ri(t)∈Rs是系统的残差信号,A、B、C是适维的系统矩阵,L0为极点配置法获取的标称观测器增益矩阵,而Li∈Rn×s,i=1,2,…N为待设计的故障灵敏性观测器增益矩阵,并满足/>
Figure BDA0003872262310000044
:,表示取/>
Figure BDA0003872262310000045
的所有行,1:s表示取/>
Figure BDA0003872262310000046
的第1列至第s列,即Li为/>
Figure BDA0003872262310000047
的前s列;/>
Figure BDA0003872262310000048
的设计需要满足残差信号与特定故障fi的灵敏性指标||ri(t)||2>βi||fi(t)||2以及残差信号与扰动d(t)、剩余其他故障fj的鲁棒性指标
Figure BDA0003872262310000049
即/>
Figure BDA00038722623100000410
通过如下矩阵不等式获得:
Figure BDA00038722623100000411
Figure BDA00038722623100000412
Figure BDA0003872262310000051
其中,符号“*”表示矩阵对称部分,
Figure BDA0003872262310000052
Figure BDA0003872262310000053
Ed、Fd、/>
Figure BDA0003872262310000054
分别是系统状态干扰矩阵、输出干扰矩阵和故障矩阵,I为适维的单位阵,正定对称矩阵Pi=Pi T>0,α1、α2、λi、γi、βi均为任意的正标量,且βi 2=α2i
步骤b:设计智能切换管理机制驱动观测器组主动有序切换,使得观测器组生成的状态估计值可用于反馈控制来保证整体系统稳定;
所述可以使观测器主动有序切换的智能切换管理机制如下:
Lgh=Lk+σ(t)[I-σ(t)(I-H)]-1H(Lk+1-Lk),g,h∈{0,1,…,N}
其中,
Figure BDA0003872262310000055
且Hk T=Hk>0、Hk+1 T=Hk+1>0,I为适维的单位阵,0≤σ(t)≤1为切换信号,具体描述为:/>
Figure BDA0003872262310000056
上述主动有序切换的观测器生成的状态估计值
Figure BDA0003872262310000057
可以用于组成状态反馈控制器
Figure BDA0003872262310000058
其中,F∈Rm×n是由LQR方法获取的状态反馈控制增益。
步骤c:在观测器切换过程中,通过阈值法设计逻辑决策主动诊断微小故障;
所述故障诊断决策逻辑如下:
Figure BDA0003872262310000059
其中,残差评估函数为
Figure BDA00038722623100000510
Ω为移动时间窗口,阈值为
Figure BDA00038722623100000511
本实施例在MatlabR2016b环境下,以由分布在同一楼层四个房间组成的暖通空调系统为例,对本实施例所设计的方法进行验证,具体系统参数如下:
Figure BDA0003872262310000061
Figure BDA0003872262310000062
C=diag{1,1,1,1};
其中,系统输出信号表示四个房间的温度差;控制输入信号为四个房间的风阀开度差;扰动表示四个房间内人员、设备产生的热量差和外部环境的温度差;故障为风阀故障;四个房间的初始温度差均为0℃。
构造每个房间的风阀故障如下:
fi(t)=0.1 0≤t≤170,i=1,2,3,4;
构造如下均匀分布函数作为扰动:
d(t)=[0.17 0.17 0.17 0.17 0.17]T·U[-1 1];
LQR方法获取的状态反馈控制增益F为:
Figure BDA0003872262310000063
极点配置方法获取的标称观测器L0为:
Figure BDA0003872262310000064
多性能指标权衡优化下的最优值分别为
Figure BDA0003872262310000065
Figure BDA0003872262310000066
而性能指标权衡优化下的故障灵敏性观测器如下:
Figure BDA0003872262310000067
Figure BDA0003872262310000071
Figure BDA0003872262310000072
Figure BDA0003872262310000073
结果说明:
图1给出了故障诊断的智能健康管理方法示意图,其中观测器组由标称状态观测器和故障灵敏性观测器组成,并且每一个故障灵敏性观测器只对某一特定故障灵敏而对剩余其它故障和扰动都鲁棒,同时各观测器在切换信号的驱动下可以主动有序地相互切换。
图2给出了可以驱动各观测器切换的一个周期切换信号图,该切换信号的幅值在0~1,同时受该切换信号的驱动,观测器增益矩阵每隔10s切换一次,一个周期内具体的切换序列为:L0→L01→L1→L10→L0→L02→L2→L20→L0→L03→L3→L30→L0→L04→L4→L40→L0,其中L01、L10……L04、L40可根据Lgh确定。
图3给出了无故障情况下,暖通空调系统内四个房间的输出信号图,可以发现四个房间的输出信号在-0.17~0.17℃之间动态稳定,即所设计的故障灵敏性观测器组和智能切换机制并不影响系统的性能。
图4~图7分别给出了第一、二、三、四个房间风阀发生故障时,四个房间的残差信号图,可以发现在一个周期的切换过程中,只有当被切换的故障灵敏性观测器与故障房间相对应时,故障房间的残差信号才超出定常阈值0.3864,并发出报警信号,即所设计的故障灵敏性观测器只对与之相应的故障灵敏,而对剩余其它故障均鲁棒。
以上所述仅为本发明的较佳实施例,并不用以限制本发明,凡在本发明的精神和原则之内,所作的任何修改、等同替换、改进等,均应包含在本发明的保护范围之内。

Claims (4)

1.一种在线主动诊断故障的智能健康管理方法,其特征在于,包括以下步骤:
a.基于多性能指标权衡优化思想设计故障灵敏性观测器组,使得每个故障灵敏性观测器的输出残差信号对某一特定故障灵敏,同时对其他故障和干扰鲁棒;
b.设计智能切换管理机制驱动观测器组主动有序切换,使得观测器组生成的状态估计值用于反馈控制来保证整体系统稳定;
c.在观测器切换过程中,通过阈值法设计逻辑决策主动诊断微小故障。
2.根据权利要求1所述的一种在线主动诊断故障的智能健康管理方法,其特征在于,所述步骤a中设计的故障灵敏性观测器如下:
Figure FDA0003872262300000011
其中,
Figure FDA0003872262300000012
yi(t)∈Rs、/>
Figure FDA0003872262300000013
i=0,1,…,N分别是系统的状态估计、输出信号和输出估计,ui(t)∈Rm是系统的控制输入信号,ri(t)∈Rs是系统的残差信号,A、B、C是适维的系统矩阵,L0为极点配置法获取的标称观测器增益矩阵,Li∈Rn×s,i=1,2,…N为待设计的故障灵敏性观测器增益矩阵,并满足/>
Figure FDA0003872262300000014
其中,:,表示取/>
Figure FDA0003872262300000015
的所有行,1:s表示取/>
Figure FDA0003872262300000016
的第1列至第s列,即Li为/>
Figure FDA0003872262300000017
的前s列;/>
Figure FDA0003872262300000018
的设计要满足残差信号与特定故障fi的灵敏性指标||ri(t)||2>βi||fi(t)||2以及残差信号与扰动d(t)、剩余其他故障fj的鲁棒性指标
Figure FDA0003872262300000019
即/>
Figure FDA00038722623000000110
通过如下矩阵不等式获得:
Figure FDA00038722623000000111
Figure FDA00038722623000000112
Figure FDA00038722623000000113
其中,符号“*”表示矩阵对称部分,
Figure FDA00038722623000000114
Figure FDA0003872262300000021
Ed、Fd、/>
Figure FDA0003872262300000028
分别是系统状态干扰矩阵、输出干扰矩阵和故障矩阵,I为适维的单位阵,正定对称矩阵Pi=Pi T>0,α1、α2、λi、γi、βi均为任意的正标量,且βi 2=α2i。/>
3.根据权利要求1所述的一种在线主动诊断故障的智能健康管理方法,其特征在于,所述步骤b中,使观测器主动有序切换的智能切换管理机制如下:
Lgh=Lk+σ(t)[I-σ(t)(I-H)]-1H(Lk+1-Lk),g,h∈{0,1,…,N}
其中,H=Hk -1Hk+1,k=0,…i,…,N-1且Hk T=Hk>0、Hk+1 T=Hk+1>0,I为适维的单位阵,0≤σ(t)≤1为切换信号,具体描述为:
Figure FDA0003872262300000022
上述主动有序切换的观测器生成的状态估计值
Figure FDA0003872262300000026
用于组成状态反馈控制器
Figure FDA0003872262300000027
其中,F∈Rm×n是由LQR方法获取的状态反馈控制增益。
4.根据权利要求1所述的一种在线主动诊断故障的智能健康管理方法,其特征在于,所述步骤c中设计的故障诊断决策逻辑如下:
Figure FDA0003872262300000023
其中,残差评估函数为
Figure FDA0003872262300000024
Ω为移动时间窗口,阈值为
Figure FDA0003872262300000025
/>
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