CN109799802B - Sensor fault diagnosis and fault tolerance control method in molecular weight distribution control - Google Patents

Sensor fault diagnosis and fault tolerance control method in molecular weight distribution control Download PDF

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CN109799802B
CN109799802B CN201811489086.3A CN201811489086A CN109799802B CN 109799802 B CN109799802 B CN 109799802B CN 201811489086 A CN201811489086 A CN 201811489086A CN 109799802 B CN109799802 B CN 109799802B
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CN109799802A (en
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姚利娜
王豪
李立凡
梁占红
武亚威
顾照玉
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Zhengzhou University
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Abstract

The invention provides a sensor fault diagnosis and fault tolerance control method in molecular weight distribution control, which comprises the following steps: obtaining the molecular weight distribution in the chemical reaction process on line by a mass spectrometer or a laser test technology to obtain an output probability density function, and approximating the output probability density function by a B-spline neural network; establishing a state space model of the system, obtaining parameters of the state space model after transformation by a scanning identification method, and designing a learning fault diagnosis observer; monitoring whether a sensor has a fault or not in real time by using a learning fault diagnosis observer, and estimating fault information in time when the sensor has the fault; compensating the output weight of the system by using the estimated fault information; and inputting the designed control signal into the system to enable the system to output the molecular weight distribution to track the distribution of the expected molecular weight. The invention can provide guarantee for reliability and safety in the system operation process and protect the life safety and property safety of related enterprise personnel.

Description

Sensor fault diagnosis and fault tolerance control method in molecular weight distribution control
Technical Field
The invention relates to the technical field of sensor fault diagnosis and fault-tolerant control, in particular to a sensor fault diagnosis and fault-tolerant control method in molecular weight distribution control.
Background
The molecular weight distribution control of the system in chemical process control is the application of a typical random distribution control system and is paid much attention in the related fields. The concept of the random distribution system is provided by the production process of the paper industry, and is extended to the fields of ore grinding, boiler combustion, chemical reaction and the like, and because the random distribution system is widely applied, the random distribution system is paid attention by relevant experts and scholars in relevant fields. Because the related industries limited by production environments mostly adopt mechanized and intelligent production lines, a large number of components such as sensors and actuators are needed, and the long-time uninterrupted work in the production environments is difficult to avoid faults, and serious consequences are caused if the faults cannot be diagnosed in time and fault-tolerant control is carried out. The control object of the random distribution system is an output probability density function of the whole system, the input, noise and fault types of the random distribution system do not necessarily obey Gaussian distribution, and the random variable of the random distribution system is non-Gaussian and is a non-Gaussian random distribution system. The disturbance of the random distribution system is mostly non-gaussian, the distribution of the disturbance is difficult to describe, and the performance of fault diagnosis is influenced.
The traditional fault diagnosis method is mainly based on a model design observer or a filter to carry out fault diagnosis. The existing fault diagnosis and fault tolerance control method only aims at the actuator of a non-Gaussian random distribution system to carry out fault diagnosis and fault tolerance control on the sensor, and does not consider the fault diagnosis and fault tolerance control on the sensor when the sensor fails.
Disclosure of Invention
The invention provides a sensor fault diagnosis and fault-tolerant control method in molecular weight distribution control, aiming at the technical problems that the existing non-Gaussian random distribution system only carries out fault diagnosis and fault-tolerant control on an actuator and does not consider the current situation that a sensor has faults, the problem of sensor fault diagnosis of the non-Gaussian random distribution control system is solved, and the problem of fault-tolerant control when the sensor of the non-Gaussian random distribution control system has faults is solved.
In order to achieve the purpose, the technical scheme of the invention is realized as follows: a sensor fault diagnosis and fault tolerance control method in molecular weight distribution control comprises the following steps:
step 100: obtaining the molecular weight distribution in the chemical reaction process on line by a mass spectrometer or a laser test technology to obtain an output probability density function, and approximating the output probability density function by a B-spline neural network;
step 200: establishing a state space model of the system according to the approximated output probability density function, obtaining parameters of the state space model after transformation by a scanning identification method, and designing a learning fault diagnosis observer;
step 300: the designed learning fault diagnosis observer is implemented on a computer, whether a fault occurs in a sensor is monitored in real time, and when the fault occurs, the learning fault diagnosis observer estimates fault information in time;
step 400: when the system has a fault, the fault diagnosis observer accurately estimates fault information, and compensates the output weight of the system by using the estimated fault information;
step 500: and inputting a designed control signal into the system to enable the system to output a molecular weight distribution tracking the distribution of the expected molecular weight.
The output probability density function in step 100 is given by:
Figure BDA0001895245970000021
wherein phi isi(y) represents the ith B-spline basis function, n represents the number of B-spline basis functions, y represents the output of the system, t represents time, u (t) is the control input, ω isi(t) is the weight of the ith B-spline basis function;
since the integral of the output probability density function γ (y, u (t)) in its defined interval [ a, B ] is equal to 1, the output probability density function is approximated by a linear B-spline basis function neural network using a B-spline neural network as follows:
γ(y,u(t))＝C(y)V(t)+T(y)；
wherein the linear radial basis function
Figure BDA0001895245970000022
And
Figure BDA0001895245970000023
parameter(s)
Figure BDA0001895245970000024
φ1(y),φ2(y),φ3(y) are linear B-spline basis functions of the system output y; v (t) ═ ω1(t),ω1(t),…ω3(t)]TTo output a weight vector, omegai(t) is the ith B-spline basis functionThe weight, i, is 1,2, 3.
The establishment of the state space model of the system according to the approximated probability density function in step 200 is as follows:
Figure BDA0001895245970000025
introducing a new state variable by using the output weight of the system
Figure BDA0001895245970000026
The state space model of the system becomes:
Figure BDA0001895245970000027
wherein x (t) is the state variable of the original system, u (t) is the control input, f (t) is the fault vector, V (t) is the weight vector,
Figure BDA0001895245970000028
representing the first derivative, A, of the state variable x (t)sIdentity matrix, x, representing the same dimension as the state variables(t) represents a state variable related to the output weight,
Figure BDA0001895245970000031
represents a state variable xs(t) first derivative, A, B, D and G are parameter matrices, respectively;
new state
Figure BDA0001895245970000032
The state space model of the system is:
Figure BDA0001895245970000033
wherein the state variable of the new system is
Figure BDA0001895245970000034
The parameter matrices are respectively
Figure BDA0001895245970000035
IpRepresenting a P-dimensional identity matrix;
the design learning fault diagnosis observer is as follows:
Figure BDA0001895245970000036
wherein the content of the first and second substances,
Figure BDA0001895245970000037
representing the first derivative of the new system state variable observations,
Figure BDA0001895245970000038
an observed value representing a new system state variable,
Figure BDA0001895245970000039
representing the observer gain matrix, epsilon (t) representing the residual between the observed value and the actual value,
Figure BDA00018952459700000310
the weight value of the observation is shown,
Figure BDA00018952459700000311
representing an observed probability density function, Z (t) representing a vector with the same dimension as the fault, Z (t-tau) representing the value of the vector Z (t) before time tau, epsilon (t-tau) representing the residual error before time tau, K1、K2And W represents an observation system parameter,
Figure BDA00018952459700000312
representing the derivative of the fault observation.
In step 400, the estimated fault information is used to compensate the output weight of the system as follows:
Figure BDA00018952459700000313
wherein, vc (t) is the compensated output weight.
In the step 500, the probability density function of the desired molecular weight is represented as γ (y, u (t)) ═ c (y) Vg + t (y), and Vg is the desired weight; the following switching function and control law are designed to realize the molecular weight distribution tracking expected distribution after the fault:
the switching function is designed as follows:
Figure BDA00018952459700000314
where H and K both represent parameters of the switching function, S (t) represents the switching function,
Figure BDA00018952459700000315
representing a weight error vector;
when the system fault is detected to occur, the control law of the system is adjusted to be as follows:
Figure BDA0001895245970000041
wherein, K3M is a parameter satisfying a system stability condition,
Figure BDA0001895245970000042
alpha represents a parameter that satisfies a stable condition as a derivative of the fault observation;
solving, by a computer, the following linear matrix inequalities:
Figure BDA0001895245970000043
0＜(6+3σ)K1 TK1≤I；
Figure BDA0001895245970000044
P2(A1+MK3)+(A1+MK3)TP2+Q2≤0；
solving parameters of the fault diagnosis observer meeting the conditions and parameters meeting control input conditions for tracking given distribution, and achieving the purposes of fault-tolerant control and tracking expected molecular weight distribution; wherein R is1,Q1,Q2,P1,P2For proper dimension positive determination of the symmetric matrix, sigma, gamma1Is a small normal number, A1＝DAD-1I is a one-dimensional identity matrix; Σ denotes a 1 × 2 matrix.
The invention has the beneficial effects that: the designed fault diagnosis observer carries out fault diagnosis on the non-Gaussian random distribution system sensor, and the designed fault-tolerant control scheme can effectively compensate after the system has sensor faults, so that the probability density function output by the system tracks expected output. The invention can be used for the fault diagnosis and fault-tolerant control of the sensor in the random process described by the output available probability density function of chemical reaction, paper making, ore grinding, boiler combustion and the like, and can provide guarantee for the reliability and safety in the system operation process and protect the life safety and property safety of related enterprise personnel because the invention relates to the production environments of high temperature, high pressure, toxicity and the like mostly.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic view of the closed-loop control of molecular weight distribution in a polymerization process.
FIG. 3 is a fault and fault estimation diagram of the present invention;
FIG. 4 is a graph of a desired output Probability Density Function (PDF) for the system of the present invention;
FIG. 5 is a graph of the Probability Density Function (PDF) of the output of the system without fault tolerance control of the present invention;
FIG. 6 is a graph of the output Probability Density Function (PDF) with fault tolerance control for the system of the present invention;
FIG. 7 is a two-dimensional effect graph of the expected probability density function and the actual output probability density function for fault-tolerant control of the system of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1, a method for diagnosing and fault-tolerant molecular weight distribution control sensor faults in a chemical process includes the following steps:
step 100: the probability density function of the molecular weight distribution in the chemical reaction process is obtained on line by a mass spectrometer or a laser test technology, and the probability density function is approximated by a B-spline neural network.
The approximation principle is as follows, and the probability density function is given by:
Figure BDA0001895245970000051
wherein phi isi(y) represents the ith B-spline basis function, n represents the number of B-spline basis functions, y represents the output of the system, t represents time, u (t) is the control input, ω isiAnd (t) is the weight of the ith B spline basis function. Because the output probability density function gamma (y, u (t)) is in the defined interval [ a, b ]]Is equal to 1, i.e.:
ω1(t)φ1(y)+ω2(t)φ2(y)…+ωn(t)φn(y)＝1 (2)
specifically, the following 3 linear B-spline basis function neural networks are used to approximate the probability density function, that is, n in formula (2) is 3, and the linear B-spline basis function is expressed as follows:
Figure BDA0001895245970000052
wherein the content of the first and second substances,
Figure BDA0001895245970000053
due to the fact that
Figure BDA0001895245970000054
Linear radial basis function
Figure BDA0001895245970000055
And
Figure BDA0001895245970000056
wherein the parameters
Figure BDA0001895245970000057
Figure BDA0001895245970000061
The probability density function can also be expressed as:
γ(y,u(t))＝C(y)V(t)+T(y) (4)
wherein v (t) ═ ω1(t),ω1(t),ω3(t)]TFor outputting the weight vector, a and b represent the defined interval of the probability density function.
Step 200: and establishing a state space model of the system according to the probability density function, obtaining parameters of the state space model after transformation by a scanning identification method, and designing and learning a fault diagnosis observer.
The state space model of the system is as follows:
Figure BDA0001895245970000062
introducing a new state variable by using the output weight of the system
Figure BDA0001895245970000063
Figure BDA0001895245970000064
Wherein x (t) is the state variable of the original system, u (t) is the control input, f (t) is the fault vector, V (t) is the weight vector,
Figure BDA0001895245970000065
representing the first derivative, A, of the state variable x (t)sIdentity matrix, x, representing the same dimension as the state variables(t) represents a state variable related to the output weight,
Figure BDA0001895245970000066
represents a state variable xsThe first derivatives of (t), A, B, D and G are parameter matrices, respectively.
New state transformed by scanning identification method
Figure BDA0001895245970000067
The system is as follows:
Figure BDA0001895245970000068
wherein the state variable of the new system is
Figure BDA0001895245970000069
The parameter matrices are respectively
Figure BDA00018952459700000610
The design learning fault diagnosis observer is as follows:
Figure BDA0001895245970000071
wherein the content of the first and second substances,
Figure BDA0001895245970000072
representing the first derivative of the new system state variable observations,
Figure BDA0001895245970000073
an observed value representing a new system state variable,
Figure BDA0001895245970000074
representing the observer gain matrix, epsilon (t) representing the residual between the observed value and the actual value,
Figure BDA0001895245970000075
the weight value of the observation is shown,
Figure BDA0001895245970000076
representing an observed probability density function, Z (t) representing a vector with the same dimension as the fault, Z (t-tau) representing the value of the vector Z (t) before time tau, epsilon (t-tau) representing the residual error before time tau, K1、K2And W represents an observation system parameter,
Figure BDA0001895245970000077
representing the derivative of the fault observation.
Step 300: the method is implemented by a designed learning fault diagnosis observer on a computer, and the specific principle is that an observation system is constructed through input and output information of an original system, the state of the observation system approaches the state of the original system, when a fault occurs, a residual error is constructed based on the output difference of the original system and the observation system, and fault information is estimated by utilizing the residual error. And monitoring whether the sensor has faults or not in real time through a computer, and learning a fault diagnosis observer to estimate fault information in time when the sensor has the faults.
Step 400: when the system has a fault, the fault diagnosis observer accurately estimates fault information, and as shown in fig. 3, compensates the output weight of the system by using the estimated fault information:
Figure BDA0001895245970000078
step 500: and inputting a designed control signal into the system to enable the system to output a molecular weight distribution tracking expected distribution. As shown in fig. 4-7.
Vg is a desired weight, the probability density function of the desired molecular weight can be expressed as γ (y, u (t) ═ c (y) Vg + t (y), and the following switching function and control law are designed to realize the tracking of the molecular weight distribution after the failure to the desired distribution:
the switching function is designed as follows:
Figure BDA0001895245970000079
h and K both represent parameters of the switching function, S (t) represents the switching function,
Figure BDA00018952459700000710
representing the weight error vector.
When a system fault is detected, the control input of the system is adjusted to the formula (10), so that the purposes of fault-tolerant control and tracking expected molecular weight distribution are realized:
Figure BDA00018952459700000711
wherein, K3In order to satisfy the parameters of the system stability condition,
Figure BDA00018952459700000712
for the fault observation, α represents a parameter satisfying the stable condition.
Solving linear matrix inequalities (11), (12), (13) and (14) by a computer to obtain the parameters of the fault diagnosis observer satisfying the conditions and the parameters satisfying the control input conditions for tracking the given distribution as follows:
Figure BDA0001895245970000081
0＜(6+3σ)K1 TK1≤I (12)
Figure BDA0001895245970000082
P2(A1+MK3)+(A1+MK3)TP2+Q2≤0 (14)
wherein R is1,Q1,Q2,P1,P2For proper dimension positive determination of the symmetric matrix, sigma, gamma1Is a small normal number, A1＝DAD-1And M is DB, and I is a one-dimensional identity matrix. Solving the parameters of the fault diagnosis observer meeting the conditions and the parameters of the control input conditions meeting the tracking given distribution, realizing the purposes of fault-tolerant control and tracking the expected molecular weight distribution, and obtaining the following results:
K1＝0.07,K2＝-0.5,H＝[0.4 -0.1],K3＝[12.1635 0.0389]
Figure BDA0001895245970000083
the modeling and control method is applied to controlling the molecular weight distribution shape in the dynamic process of styrene bulk polymerization. The molecular weight distribution closed-loop control schematic of the polymerization process is shown in FIG. 2.
The system model may be represented as:
Figure BDA0001895245970000084
wherein the content of the first and second substances,
Figure BDA0001895245970000085
is the average residence time of the reactants; i is0Is the initial concentration (mol. ml) of the initiator-1) (ii) a I is the initiator concentration (mol. ml)-1)；M0Is the initial concentration (mol. ml) of the monomer-1) (ii) a M is the concentration of the monomer (mol. ml)-1)；Ki,Kp,KtrmIs the reaction rate constant; kd,KI,KMIs in phase with the control inputA constant of off; ri(i ═ 1, 2.., q) is a radical.
Selecting state variables
Figure BDA0001895245970000086
The system parameter matrix is respectively expressed as
Figure BDA0001895245970000087
And
Figure BDA0001895245970000088
K1,K2is a fault-related parameter. The parameters of the state space model obtained by the scan recognition method are as follows:
Figure BDA0001895245970000091
if the temperature sensor in the reaction kettle breaks down, the measured temperature is not consistent with the actual temperature in the kettle, the injection amount of each reaction component or the dosage of heating oil can be influenced, and the molecular weight distribution of products in the kettle is further influenced. When a fault occurs, the observer can quickly acquire the fault information and feed back the fault information to the control system. The control system will switch to the pre-designed control input (10) for fault tolerant control. The molecular weight distribution of the reaction product is adjusted to a desired distribution by adjusting the temperature and the ratio of the reactants in the chemical reaction vessel by changing the amount of monomer or initiator injected, the amount of heating oil used, the stirring rate, and the like.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (4)

1. A sensor fault diagnosis and fault tolerance control method in molecular weight distribution control is characterized by comprising the following steps:
step 100: obtaining the molecular weight distribution in the chemical reaction process on line by a mass spectrometer or a laser test technology to obtain an output probability density function, and approximating the output probability density function by a B-spline neural network;
step 200: establishing a state space model of the system according to the approximated output probability density function, obtaining parameters of the state space model after transformation by a scanning identification method, and designing a learning fault diagnosis observer;
step 300: the designed learning fault diagnosis observer is implemented on a computer, whether a fault occurs in a sensor is monitored in real time, and when the fault occurs, the learning fault diagnosis observer estimates fault information in time;
step 400: when the system has a fault, the fault diagnosis observer accurately estimates fault information, and compensates the output weight of the system by using the estimated fault information;
step 500: inputting the designed control signal into the system to enable the system to output molecular weight distribution to track the distribution of the expected molecular weight;
the establishment of the state space model of the system according to the approximated probability density function in step 200 is as follows:
Figure FDA0003060188040000011
introducing a new state variable by using the output weight of the system
Figure FDA0003060188040000012
The state space model of the system becomes:
Figure FDA0003060188040000013
wherein x (t) is the state variable of the original system, u (t) is the control input, f (t) is the fault vector, V (t) is the weight vector,
Figure FDA0003060188040000014
representing the first derivative of the state variable x (t)，AsIdentity matrix, x, representing the same dimension as the state variables(t) represents a state variable related to the output weight,
Figure FDA0003060188040000015
represents a state variable xs(t) first derivative, A, B, D and G are parameter matrices, respectively;
new state
Figure FDA0003060188040000016
The state space model of the system is:
Figure FDA0003060188040000017
wherein the state variable of the new system is
Figure FDA0003060188040000018
The parameter matrices are respectively
Figure FDA0003060188040000021
IpRepresenting a P-dimensional identity matrix;
the design learning fault diagnosis observer is as follows:
Figure FDA0003060188040000022
wherein the content of the first and second substances,
Figure FDA0003060188040000023
representing the first derivative of the new system state variable observations,
Figure FDA0003060188040000024
an observed value representing a new system state variable,
Figure FDA0003060188040000025
representing the observer gain matrix, epsilon (t) representing the residual between the observed value and the actual value,
Figure FDA0003060188040000026
the weight value of the observation is shown,
Figure FDA0003060188040000027
representing an observed probability density function, Z (t) representing a vector with the same dimension as the fault, Z (t-tau) representing the value of the vector Z (t) before time tau, epsilon (t-tau) representing the residual error before time tau, K1、K2And W represents an observation system parameter,
Figure FDA0003060188040000028
representing the derivative of the fault observation.
2. The method for diagnosing and controlling the failure of a sensor in the molecular weight distribution control according to claim 1, wherein the step 100 outputs a probability density function given by:
Figure FDA0003060188040000029
wherein phi isi(y) represents the ith B-spline basis function, n represents the number of B-spline basis functions, y represents the output of the system, t represents time, u (t) is the control input, ω isi(t) is the weight of the ith B-spline basis function;
since the integral of the output probability density function γ (y, u (t)) in its defined interval [ a, B ] is equal to 1, the output probability density function is approximated by a linear B-spline basis function neural network using a B-spline neural network as follows:
γ(y,u(t))＝C(y)V(t)+T(y)；
wherein the linear radial basis function
Figure FDA00030601880400000210
And
Figure FDA00030601880400000211
parameter(s)
Figure FDA00030601880400000212
φ1(y),φ2(y),φ3(y) are linear B-spline basis functions of the system output y; v (t) ═ ω1(t),ω1(t),…ω3(t)]TTo output a weight vector, omegaiAnd (t) is the weight of the ith B spline basis function, and i is 1,2 and 3.
3. The method for diagnosing and controlling the failure of a sensor in the molecular weight distribution control according to claim 2, wherein the step 400 of compensating the output weight of the system by using the estimated failure information comprises:
Figure FDA0003060188040000031
wherein, vc (t) is the compensated output weight.
4. The method for diagnosing and controlling the failure of a sensor in the molecular weight distribution control according to claim 1, wherein the probability density function of the desired molecular weight in step 500 is represented by γ (y, u (t)) ═ c (y) Vg + t (y), Vg being a desired weight; the following switching function and control law are designed to realize the molecular weight distribution tracking expected distribution after the fault:
the switching function is designed as follows:
Figure FDA0003060188040000032
where H and K both represent parameters of the switching function, S (t) represents the switching function,
Figure FDA0003060188040000033
representing a weight error vector;
when the system fault is detected to occur, the control law of the system is adjusted to be as follows:
Figure FDA0003060188040000034
wherein, K3M is a parameter satisfying a system stability condition,
Figure FDA0003060188040000035
alpha represents a parameter that satisfies a stable condition as a derivative of the fault observation;
solving, by a computer, the following linear matrix inequalities:
Figure FDA0003060188040000036
0＜(6+3σ)K1 TK1≤I；
Figure FDA0003060188040000037
P2(A1+MK3)+(A1+MK3)TP2+Q2≤0；
solving parameters of the fault diagnosis observer meeting the conditions and parameters meeting control input conditions for tracking given distribution, and achieving the purposes of fault-tolerant control and tracking expected molecular weight distribution; wherein R is1,Q1,Q2,P1,P2For proper dimension positive determination of the symmetric matrix, sigma, gamma1Is a small normal number, A1＝DAD-1I is a one-dimensional identity matrix; Σ denotes a 1 × 2 matrix.
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101481019A (en) * 2009-02-20 2009-07-15 华中科技大学 Fault tolerant observing method of sensor for satellite attitude control system
CN103399493A (en) * 2013-08-07 2013-11-20 长春工业大学 Real-time diagnosis and tolerant system for sensor faults of reconfigurable mechanical arm and method thereof
CN107023367A (en) * 2017-03-29 2017-08-08 北京航空航天大学 A kind of SCR system of diesel engine ammonia input pickup fault diagnosis and fault tolerant control method
CN107035490A (en) * 2017-03-29 2017-08-11 北京航空航天大学 A kind of SCR system of diesel engine nitrogen oxides input pickup method for diagnosing faults
JP2017167599A (en) * 2016-03-14 2017-09-21 日本電気株式会社 Fault diagnosis apparatus, fault diagnosis system, fault diagnosis method, and program
CN107329083A (en) * 2017-07-28 2017-11-07 南京航空航天大学 For the method for diagnosing faults of bullet train traction electric machine nonlinear system sensor
CN108536017A (en) * 2018-05-03 2018-09-14 山东师范大学 Random distribution interacted system cooperation fault tolerant control method based on dynamic feedback control

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101481019A (en) * 2009-02-20 2009-07-15 华中科技大学 Fault tolerant observing method of sensor for satellite attitude control system
CN103399493A (en) * 2013-08-07 2013-11-20 长春工业大学 Real-time diagnosis and tolerant system for sensor faults of reconfigurable mechanical arm and method thereof
JP2017167599A (en) * 2016-03-14 2017-09-21 日本電気株式会社 Fault diagnosis apparatus, fault diagnosis system, fault diagnosis method, and program
CN107023367A (en) * 2017-03-29 2017-08-08 北京航空航天大学 A kind of SCR system of diesel engine ammonia input pickup fault diagnosis and fault tolerant control method
CN107035490A (en) * 2017-03-29 2017-08-11 北京航空航天大学 A kind of SCR system of diesel engine nitrogen oxides input pickup method for diagnosing faults
CN107329083A (en) * 2017-07-28 2017-11-07 南京航空航天大学 For the method for diagnosing faults of bullet train traction electric machine nonlinear system sensor
CN108536017A (en) * 2018-05-03 2018-09-14 山东师范大学 Random distribution interacted system cooperation fault tolerant control method based on dynamic feedback control

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