CN114115198A - Assembly production line-oriented distributed diagnosis and optimization control method and control system - Google Patents

Assembly production line-oriented distributed diagnosis and optimization control method and control system Download PDF

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CN114115198A
CN114115198A CN202111417480.8A CN202111417480A CN114115198A CN 114115198 A CN114115198 A CN 114115198A CN 202111417480 A CN202111417480 A CN 202111417480A CN 114115198 A CN114115198 A CN 114115198A
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罗浩
霍明夷
王豪
李款
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Harbin Institute of Technology
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    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
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    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • G—PHYSICS
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    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract

The invention discloses a distributed diagnosis and optimization control method and a distributed diagnosis and optimization control system for an assembly production line, and relates to the distributed diagnosis and optimization control method and the distributed diagnosis and optimization control system. The invention aims to solve the problems that the diagnosis and control of an assembly production line are seriously influenced by external disturbance commonly existing in the existing production and manufacturing process, and the production efficiency is reduced. The process is as follows: step one, designing an assembly production line distributed system, diagnosing faults of the assembly production line distributed system, and executing step two when the assembly production line distributed system is diagnosed to have faults; when the assembly production line distributed system is diagnosed not to have a fault, continuously diagnosing the fault of the assembly production line distributed system; and step two, designing an optimal control method of the assembly production line distributed system. The invention is used for the field of distributed system fault diagnosis and fault-tolerant control.

Description

Assembly production line-oriented distributed diagnosis and optimization control method and control system
Technical Field
The invention belongs to the field of distributed system fault diagnosis and fault-tolerant control, and particularly relates to a distributed diagnosis and optimal control method and a distributed diagnosis and optimal control system.
Background
Due to the fact that complexity and uncertainty are higher and higher, the guarantee of safe operation and efficient output of an assembly production line becomes an important problem to be solved urgently for current intelligent production and manufacturing enterprises. The external disturbance commonly existing in the production and manufacturing process can cause serious influence on the diagnosis and control of the assembly production line, and the production efficiency is greatly restricted. Therefore, it is necessary to provide an assembly line-oriented distributed diagnosis and optimal control method, which realizes the function integration of online diagnosis and optimal control performance for the distributed system.
Disclosure of Invention
The invention aims to solve the problems that the diagnosis and control of an assembly production line are seriously influenced and the production efficiency is reduced due to external disturbance commonly existing in the existing production and manufacturing process, and provides a distributed diagnosis and optimal control method and a distributed diagnosis and optimal control system for the assembly production line.
The distributed diagnosis and optimization control method for the assembly production line comprises the following specific processes:
step one, designing an assembly production line distributed system, diagnosing faults of the assembly production line distributed system, and executing step two when the assembly production line distributed system is diagnosed to have faults; when the assembly production line distributed system is diagnosed not to have a fault, continuously diagnosing the fault of the assembly production line distributed system;
and step two, designing an optimal control method of the assembly production line distributed system.
A distributed diagnosis and optimization control system for an assembly production line is used for executing a distributed diagnosis and optimization control method for the assembly production line.
The invention has the beneficial effects that:
the invention provides an assembly production line-oriented distributed diagnosis and optimization control method and a control system, wherein an assembly production line distributed system is designed, the fault of the assembly production line distributed system is diagnosed, and when the fault of the assembly production line distributed system is diagnosed, the optimization control method of the assembly production line distributed system is designed; when the assembly production line distributed system is diagnosed not to have a fault, continuously diagnosing the fault of the assembly production line distributed system;
the method can achieve the purpose of online diagnosis and optimization of the control performance of the whole system only by designing and adjusting the diagnosis module and the control module of the subsystem, and the original sub-control system does not need to be redesigned, so that the problem that the parameters of the controller cannot be changed due to packaging and the like is solved, and meanwhile, a distributed diagnosis and optimization control integrated framework is established.
Drawings
FIG. 1 is a flow chart of a distributed diagnostic and optimization control method of the present invention;
FIG. 2 is a block diagram of the distributed diagnostics and optimization control integration framework proposed by the present invention, G1(z) is the 1 st subsystem,
Figure BDA0003375719750000021
is n thsSubsystem, K1(z) is the 1 st sub-control system,
Figure BDA0003375719750000022
is n thsSub-control system, u1Is the input signal of the 1 st subsystem,
Figure BDA0003375719750000023
is n thsSubsystem nsInput signal of y1Is the output signal of the 1 st sub-system,
Figure BDA0003375719750000024
is n thsOutput signals of the sub-systems, RG1For the 1 st residual generator, the residual signal is,
Figure BDA0003375719750000025
is n thsA residual generator r1For the residuals generated by the 1 st residual generator,
Figure BDA0003375719750000026
is n thsA residual generated by a residual generator, r: (1) (z) is the total residual error generated by the 1 st subsystem,
Figure BDA0003375719750000027
is n thsTotal residual error, R, produced by the subsystemf,1(z) is the 1 st post-filter,
Figure BDA0003375719750000028
is n thsA post filter; r isf (1)For the resulting 1 st residual post-filter,
Figure BDA0003375719750000029
to produce the n-thsA residual postfilter; q1(z) is the own additional controller for the 1 st subsystem,
Figure BDA00033757197500000210
is n thsThe subsystem's own additional controller; qi(i-1)(z) to consider the effect of subsystem i-1 on subsystem i, according to ri-1An additional controller designed for subsystem i;
FIG. 3a1 is a diagram of a distributed system subsystem 1 residual error detection in a distributed system, r, according to an embodiment of the present invention1 Residual signal 1, r generated for subsystem 12A residual signal 2 generated for the subsystem 1;
FIG. 3a2 is a diagram of a sub-system 1 statistic fault detection in a distributed system, J, of an embodiment of the invention1Statistics generated for subsystem 1, Jth,1A threshold designed for statistics generated from subsystem 1;
FIG. 3b1 is a diagram of subsystem 2 residual error detection in a distributed system, r, according to an embodiment of the present invention1 Residual signal 1, r generated for subsystem 22A residual signal 2 generated for subsystem 2;
FIG. 3b2 is a diagram of sub-system 2 statistic fault detection in a distributed system, J, an embodiment of the invention2Statistics generated for subsystem 2, Jth,2A threshold designed for statistics generated from subsystem 2;
FIG. 3c1 is a block diagram of a distributed system subsystem 3 residual error detection in accordance with an embodiment of the present inventionMapping, r1 Residual signals 1, r generated for the subsystem 32A residual signal 2 generated for the subsystem 3;
FIG. 3c2 is a diagram of sub-system 3 statistic fault detection in a distributed system, J, an embodiment of the invention3Statistics generated for subsystem 3, Jth,3A threshold designed for statistics generated from the subsystem 3;
FIG. 3d1 is a diagram of subsystem 4 residual error detection in a distributed system, r, according to an embodiment of the present invention1 Residual signals 1, r generated for the subsystem 42A residual signal 2 generated for the subsystem 4;
FIG. 3d2 is a diagram of a sub-system 4 statistic fault detection in a distributed system, J, of an embodiment of the present invention4Statistics generated for subsystem 4, Jth,4A threshold designed for statistics generated from subsystem 4;
FIG. 3e1 is a diagram of subsystem 5 residual fault detection in a distributed system, r, according to an embodiment of the present invention1 Residual signals 1, r generated for the subsystem 52A residual signal 2 generated for the subsystem 5;
FIG. 3e2 is a diagram of a sub-system 5 statistic fault detection in a distributed system, J, of an embodiment of the invention5Statistics generated for subsystem 5, Jth,5A threshold designed for statistics generated from subsystem 5;
FIG. 4 is a diagram of the effect of tracking error index of the subsystem 3 according to the embodiment of the present invention, e1And e2Two tracking error signals representative of each subsystem;
FIG. 5 is a diagram illustrating the tracking error indicator effect of the subsystem 4 according to an embodiment of the present invention;
FIG. 6 is a diagram illustrating the tracking error indicator effect of the subsystem 5 according to an embodiment of the present invention;
FIG. 7 is a diagram illustrating the effect of updating the optimization index of the overall system according to an embodiment of the present invention;
figure 8a is a diagram of the effect of updating the optimization index of the subsystem 3 according to the embodiment of the present invention,
figure 8b is a diagram of the effect of updating the optimization index of the subsystem 4 according to the embodiment of the present invention,
fig. 8c is a diagram of the effect of updating the optimization index of the subsystem 5 according to the embodiment of the present invention.
Detailed Description
The first embodiment is as follows: the distributed diagnosis and optimal control method for the assembly production line comprises the following specific processes:
step one, designing an assembly production line distributed system, diagnosing faults of the assembly production line distributed system, and executing step two when the assembly production line distributed system is diagnosed to have faults; when the assembly production line distributed system is diagnosed not to have a fault, continuously diagnosing the fault of the assembly production line distributed system;
and step two, designing an optimization control method of the assembly production line distributed system (subsystem).
The second embodiment is as follows: the difference between this embodiment and the first embodiment is that, in the first step, an assembly line distributed system is designed, and a fault of the assembly line distributed system is diagnosed, and the specific process is as follows:
a1, n is the total number of assembly line distributed systemssA subsystem;
setting nsThe sub-monitoring system is used for monitoring whether the sub-system is abnormal or not and carrying out optimization control on the sub-system after the abnormality occurs;
sub-system
Figure BDA0003375719750000041
And sub-control system K1(z)…Ki(z) a system of an original structure;
setting nsA sub-control system (the sub-control system is a controller, such as a sliding mode controller and a PID controller) for inputting the output of the sub-system into the controller;
a2, taking the ith subsystem as an example, collecting the 1 st 1 … i subsystems G under normal working conditions1(z)…Gi(z) input signal u1…uiAnd the ith subsystem G under normal operating conditionsi(z) output signal yi；1≤i≤ns；
Ith subsystem Gi(z) output signal yiWill affect the i, i +1, …, nsSub-control systems, i.e. yiRespectively transmitted to the sub-control systems
Figure BDA0003375719750000042
Performing the following steps;
a3, and 1 st 1 … i subsystem G based on A2 acquisition under normal working conditions1(z)…Gi(z) input signal and ith subsystem Gi(z) designing residual error generator RG of ith sub-monitoring system under normal working condition by using left/right co-prime decomposition technologyi；
Residual error generator RG of ith sub-monitoring system under normal working conditioniGenerating a residual error ri；
The ith subsystem Gi(z) affects each of its and subsequent sub-monitoring systems, i.e. the ith sub-system Gi(z) input signal uiWill affect the i, i +1, …, nsSub-monitoring systems, i.e. i-th sub-system G under normal operating conditionsi(z) input signal uiRespectively to the i, i +1, …, nsResidual error generator RG of sub-monitoring systemi,
Figure BDA0003375719750000043
A4, considering the actual information interaction among subsystems, namely the influence of the residual error generated by the residual error generator of each previous sub-monitoring system on the system, and respectively collecting the residual error r generated by the residual error generator of the 1 … i sub-monitoring system under the normal working condition1…riGenerating the total residual r of the ith sub-monitoring system under the normal working condition(i)；
A5, generating the total residual r of the ith sub-monitoring system under the normal working condition according to A4(i)Designing a post filter R of the ith sub-monitoring systemf,i(z), total residual r(i)After the post-filter, residual error r is generatedf (i)So that the total residual r(i)The disturbance signals in the system are filtered as much as possible, and are more sensitive to faults;
a6, residual r generated from A5f (i)Calculating a statistic, designing a threshold value according to the statistic (empirically artificially designing the threshold value according to the statistic);
a7, taking the ith subsystem as an example, collecting the 1 st 1 … i subsystem G under the online working condition1(z)…Gi(z) input signal u1…uiAnd the ith subsystem G under the online working conditioni(z) output signal yi；1≤i≤ns；
Ith subsystem Gi(z) output signal yiWill affect the i, i +1, …, nsSub-control systems, i.e. yiRespectively transmitted to the sub-control systems Ki(z),
Figure BDA0003375719750000051
Performing the following steps;
a8, 1 st 1 … i subsystem G based on A7 acquisition under online working condition1(z)…Gi(z) input signal and ith subsystem Gi(z) designing residual error generator RG of ith sub-monitoring system under on-line working condition by using left/right co-prime decomposition technologyi；
Residual error generator RG of ith sub-monitoring system under online working conditioniGenerating a residual error ri；
The ith subsystem Gi(z) affects each of its and subsequent sub-monitoring systems, i.e. the ith sub-system Gi(z) input signal uiWill affect the i, i +1, …, nsSub-monitoring systems, i.e. i-th sub-system G under on-line operating conditionsi(z) input signal uiRespectively to the i, i +1, …, nsResidual error generator RG of sub-monitoring systemi,
Figure BDA0003375719750000052
A9, considering the actual information interaction among subsystems, namely the influence of the residual generated by the residual generator of each previous sub-monitoring system on the system, and respectively collecting the 1 st 1 … i sub-monitoring system under the online working conditionResidual r generated by residual generator of system1…riGenerating the total residual r of the ith sub-monitoring system under the online working condition(i)；
A10, generating the total residual r of the ith sub-monitoring system under the online working condition according to A9(i)Designing a post filter R of the ith sub-monitoring systemf,i(z), total residual r(i)After the post-filter, residual error r is generatedf (i)So that the total residual r(i)The disturbance signals in the system are filtered as much as possible, and are more sensitive to faults;
a11, residual r generated from A10f (i)Calculating a statistic;
and A12, judging whether the assembly line distributed system has a fault.
Other steps and parameters are the same as those in the first embodiment.
The third concrete implementation mode: the present embodiment differs from the first or second embodiment in that the additional controller Qi(z) is the residual ri。
Other steps and parameters are the same as those in the first or second embodiment.
The fourth concrete implementation mode: the present embodiment is different from the first to third embodiments in that the ith sub-control system Ki(z) as input the ith subsystem Gi(z) output signal yi。
Other steps and parameters are the same as those in one of the first to third embodiments.
The fifth concrete implementation mode: the present embodiment is different from one of the first to fourth embodiments in that the additional controller Qi(z) output/sub-control System Ki(z) output and subsystem Gi(z) input signal uiAs subsystem Gi(z) input.
Other steps and parameters are the same as in one of the first to fourth embodiments.
The sixth specific implementation mode: the difference between the present embodiment and one of the first to fifth embodiments is that the 1 st 1 … i children under the normal working condition are collected in the a4Residual r generated by residual generator of monitoring system1…riGenerating the total residual r of the ith sub-monitoring system under the normal working condition(i)(ii) a The expression is as follows:
r(i)＝[r1 T…ri T]T
wherein T is transposition;
respectively collecting residual errors r generated by residual error generators of 1 … i sub-monitoring systems under online working conditions in A91…riGenerating the total residual r of the ith sub-monitoring system under the online working condition(i)(ii) a The expression is as follows:
r(i)＝[r1 T…ri T]T。
other steps and parameters are the same as those in one of the first to fifth embodiments.
The seventh embodiment: the difference between this embodiment and one of the first to sixth embodiments is that in the step a12, it is determined whether the assembly line distributed system has a fault; the process is as follows:
if the statistic value calculated by the A11 is less than or equal to the threshold value designed by the A6, the assembly line distributed system is not in fault, and the diagnosis of the fault of the assembly line distributed system is continuously carried out from A1 to A12;
if the statistic calculated by A11 is greater than the threshold value designed by A6, the assembly line distributed system (subsystem G) is indicatedi(z)) a fault occurs, so far, a diagnosis module of the ith sub-monitoring system is formed, and the step two is executed.
When the subsystem G is diagnosedi(z) when a fault occurs, the following steps are adopted, and the optimization control method of the subsystem is designed to adjust and remedy in time.
Other steps and parameters are the same as those in one of the first to sixth embodiments.
The specific implementation mode is eight: the second step is to design an optimization control method of the assembly line distributed system (subsystem); the specific process is as follows:
b1, i subsystem Gi(z) as an example, design subsystem Gi(z) additional controller Q of its owni(z)；
The subsystem G was designed according to the reference (H.Luo, X.Yang, M.Krueger, S.X.Ding and K.Peng, "A Plug-and-Play Monitoring and Control Architecture for distribution computing in Rolling Mills," in IEEE/ASME Transactions on mechanics, vol.23, No.1, pp.200-210, Feb.2018, doi:10.1109/TMECH.2016.2636337.)i(z) additional controller Q of its owni(z)；
B2, additional controllers Q for respectively designing 1 st 1 … i-1 st subsystemsi1(z)…Qi(i-1)(z)；
The 1 st 1 … i-1 subsystem was designed according to the reference (H.Luo, X.Yang, M.Krueger, S.X.Ding and K.Peng, "A Plug-and-Play Monitoring and Control Architecture for distribution computing in Rolling Mills," in IEEE/ASME Transactions on mechanics, vol.23, No.1, pp.200-210, Feb.2018, doi:10.1109/TMECH.2016.2636337.)i1(z)…Qi(i-1)(z)；
B3 residual error generator RG of ith sub-monitoring system under on-line working condition acquired by A7iResulting residual riInput subsystem Gi(z) additional controller Q of its owni(z)；
Collecting residual r generated by a residual generator of a1 … i-1 sub-monitoring system under the online working condition acquired by A81…ri-1Input subsystem G1(z)…Gi-1(z) additional controller Q of its owni1(z)…Qi(i-1)(z)；
General system Gi(z) additional controller Q of its owni(z) output signal, subsystem G1(z)…Gi-1(z) additional controller Q of its owni1(z)…Qi(i-1)(z) and on-line ith sub-control system Ki(z) the output signal is passed to the subsystem Gi(z) completing the optimization control of the assembly line distributed system (subsystem) with faults;
and when a fault of a certain subsystem is diagnosed through the first step, determining the subsystem which needs to start the additional controller according to the diagnosis result, activating an optimization control program, and adjusting the parameters of the additional controller of the corresponding subsystem in real time to form an optimization control module of the ith sub-monitoring system.
And completing a distributed diagnosis and control integrated framework based on the first step and the second step, as shown in FIG. 2.
Other steps and parameters are the same as those in one of the first to seventh embodiments.
The specific implementation method nine: the assembly line-oriented distributed diagnosis and optimization control system of the present embodiment is used for executing an assembly line-oriented distributed diagnosis and optimization control method according to one of the first to eighth embodiments.
Examples
The effectiveness of the method will be described below in conjunction with specific simulation results.
Step one, designing a distributed system of an assembly production line (the assembly production line of the invention is an automobile assembly production line, and can also be a robot, an instrument assembly production line and the like), diagnosing the fault of the distributed system of the assembly production line, and executing step two when the fault of the distributed system of the assembly production line is diagnosed; when the assembly production line distributed system is diagnosed not to have a fault, continuing to diagnose the fault of the assembly production line distributed system;
a1, adopting a distributed structure of an assembly production line under a normal working condition and an on-line working condition, wherein the distributed structure consists of 5 linear time-invariant subsystems, and the subsystems can be communicated with each other;
assuming that a fault occurs in the subsystem 3, based on the structure of the system topology connection of the simulation, fault information can be transmitted from the subsystem 3 to the subsystem 4 and the subsystem 5, and cannot be transmitted reversely;
assuming that each subsystem is an isomorphic system, the subsystem parameter matrix is as follows:
Figure BDA0003375719750000081
Figure BDA0003375719750000082
the communication connection strength matrix of each subsystem is as follows:
Figure BDA0003375719750000083
in addition, the sub-control systems (controllers) can be designed by the euler parameterization method under the condition that the closed-loop subsystems are ensured to be proper and stable internally.
A2, taking the 3 rd subsystem as an example, collecting the input signals u of the 1 st to the 3 rd subsystems under the normal working condition1,u2,u3And the output signal y of the 3 rd subsystem under the normal working condition3；
Output signal y of the 3 rd subsystem under normal working condition3Will affect the i, i +1, …, nsSub-control systems, i.e. yiRespectively transmitted to the sub-control systems K3(z),K4(z),K5(z) in (z);
a3, and the input signal u of the subsystem 1-3 collected by A2 under the normal condition1,u2,u3And the output signal y of the subsystem 3 under normal conditions3Designing residual error generator RG of the 3 rd sub-monitoring system by using left/right co-prime decomposition technology3And collecting the residual r of the 1 st to 3 rd sub-monitoring systems according to the actual information interaction among the subsystems1,r2,r3Generating the total residual r of the 3 rd sub-monitoring system(3)；
A4, Total residual r of sub-monitoring System 3 generated from A3(3)Designing a post filter R of the 3 rd sub-monitoring systemf,3(z), total residual r(3)After the post-filter, residual error r is generatedf (3)So that the disturbance signal is filtered as much as possible, and the residual error is more sensitive to faults;
a5, residual r generated from A4f (3)Computing systemMetering, designing a threshold value according to the statistic (empirically, artificially designing the threshold value according to the statistic);
a6, at the 3000 th sampling point, sudden change fault occurs in the subsystem 3, the sudden change occurs in the system matrix due to the fault, and the fault rate frIs set to 0.1 (i.e., f)r0.1); taking the 3 rd subsystem as an example, the input signals u of the subsystems 1-3 under the online working condition are collected1,u2,u3And the output signal y of the subsystem 3 in the on-line operating mode3；
Output signal y of subsystem 3 under on-line condition3Will affect the i, i +1, …, nsSub-control systems, i.e. yiRespectively transmitted to the sub-control systems K3(z),K4(z),K5(z) in (z);
a7, and input signals u of subsystems 1-3 collected through A6 under online working conditions1,u2,u3And the output signal y of the subsystem 3 in the on-line operating mode3Designing residual error generator RG of the 3 rd sub-monitoring system by using left/right co-prime decomposition technology3And collecting the residual r of the 1 st to 3 rd sub-monitoring systems according to the actual information interaction among the subsystems1,r2,r3Generating the total residual r of the 3 rd sub-monitoring system(3)；
A8, Total residual r of sub-monitoring System 3 generated from A7(3)Designing a post filter R of the 3 rd sub-monitoring systemf,3(z), total residual r(3)After the post-filter, residual error r is generatedf (3)So that the disturbance signal is filtered as much as possible, and the residual error is more sensitive to faults;
a9, residual r generated from A8f (3)Calculating a statistic;
a10 judging whether the assembly line distributed system has a fault; the specific process is as follows:
if the statistic value calculated by the A9 is less than or equal to the threshold value designed by the A5, the assembly line distributed system does not have a fault; if the statistic calculated by A9 is larger than the threshold designed by A5, the assembly line distributed system is indicated to be in fault, and thus, a diagnostic module of the 3 rd sub-monitoring system is formed.
The steps of the diagnosis modules of the rest subsystems are the same as those of the subsystem 3, and are not described again here.
The results of the fault diagnosis are shown in fig. 3a1, 3a2, 3b1, 3b2, 3c1, 3c2, 3d1, 3d2, 3e1, and 3e 2. Fig. 3a1, 3a2, 3b1, 3b2, 3c1, 3c2, 3d1, 3d2, 3e1, 3e2 represent the residual curve results and statistic curve results for subsystems 1-5, respectively.
At the 3000 th sampling point, the residual signals of the subsystem 3, the subsystem 4 and the subsystem 5 all change, and the detection statistic of each subsystem is higher than the fault threshold, indicating that sudden fault (f) occursr0.1) an early warning is given.
Designing an optimization control method of a distributed system (subsystem) of an assembly production line;
b1, the adopted distributed structure is composed of 5 linear time-invariant subsystems, and all the subsystems can communicate with each other;
assuming that a fault occurs in the subsystem 3, based on the structure of the system topology connection of the simulation, fault information can be transmitted from the subsystem 3 to the subsystem 4 and the subsystem 5, and cannot be transmitted reversely;
the simulation therefore shows only the effect of optimal control of the subsystems 3, 4 and 5 affected by the fault.
Assuming that each subsystem is an isomorphic system, the parameter matrix is as follows:
Figure BDA0003375719750000101
Figure BDA0003375719750000102
the communication connection strength matrix of each subsystem is as follows:
Figure BDA0003375719750000103
in addition, the sub-control systems can be designed by euler parameterization under the condition of ensuring that each closed-loop subsystem is proper and stable internally.
Additional controllers Q provided for the subsystemsi(z) has an initial value of
Figure BDA0003375719750000104
And the additional controller is not started when the assembly production line distributed system is not in fault;
design subsystem Gi(z) additional controller Q of its owni(z)；
The subsystem G was designed according to the reference (H.Luo, X.Yang, M.Krueger, S.X.Ding and K.Peng, "A Plug-and-Play Monitoring and Control Architecture for distribution computing in Rolling Mills," in IEEE/ASME Transactions on mechanics, vol.23, No.1, pp.200-210, Feb.2018, doi:10.1109/TMECH.2016.2636337.)i(z) additional controller Q of its owni(z)；
Additional controller Q of 1 st 1 … th i-1 st subsystem respectively corresponding to designi1(z)…Qi(i-1)(z)；
The 1 st 1 … i-1 subsystem was designed according to the reference (H.Luo, X.Yang, M.Krueger, S.X.Ding and K.Peng, "A Plug-and-Play Monitoring and Control Architecture for distribution computing in Rolling Mills," in IEEE/ASME Transactions on mechanics, vol.23, No.1, pp.200-210, Feb.2018, doi:10.1109/TMECH.2016.2636337.)i1(z)…Qi(i-1)(z)；
Inputting the residual error generated by the residual error generator of the 1 st sub-monitoring system under the on-line working condition into the extra controller Q of the sub-system 131(z)；
Inputting the residual error generated by the residual error generator of the 2 nd sub-monitoring system under the acquired online working condition into the additional controller Q of the sub-system 232(z)；
Inputting the residual error generated by the residual error generator of the 3 rd sub-monitoring system under the on-line working condition into the extra controller Q of the sub-system 33(z); (based on the previous subsystem's effect on subsystem 3, soExternal controller Q32(z),Q31The subscripts of (z) are 32 and 31);
the output signals of the additional controllers of the subsystems 1, 2 and 3 are compared with the online 3 rd sub-control system Ki(z) transmitting the output signal to the subsystem 3 to complete the optimization control of the assembly line distributed system (subsystem) with faults;
and when a fault of a certain subsystem is diagnosed through the first step, determining the subsystem which needs to start the additional controller according to the diagnosis result, activating an optimization control program, and adjusting the parameters of the additional controller of the corresponding subsystem in real time to form an optimization control module of the 3 rd sub-monitoring system.
The steps of the optimization control modules of the other subsystems are the same as those of the subsystem 3, and are not described again here.
Fig. 4 to 6 show the effect diagrams of the tracking error indicators of the subsystem 3, the subsystem 4 and the subsystem 5, respectively. Wherein e is1And e2Representing two tracking error signals for each subsystem. As can be seen from the graph, the tracking error of each subsystem is obviously increased after fault injection (before optimal control); after the start of the optimization control at 175s, the tracking error of each subsystem gradually starts to decrease. Note that around 325s, the tracking error e of subsystem 32Has clearly approached 0. It should be noted that the tracking errors of the subsystems 4 and 5 may also change during fault injection (the fault is originally injected into the subsystem 3), because the topological connection causes the subsystem 3 to propagate the effect of the fault to the subsystems 4 and 5.
Fig. 7 shows the updating effect of the optimization index of the whole system in the optimization control. As can be seen from the figure, after 175 th (i.e., after the optimization control is started), the tracking performance optimization index of the overall system gradually starts to decrease, which proves that the tracking performance of the system gradually increases, i.e., the influence of the system on the fault is gradually reduced.
Furthermore, fig. 8a, 8b, 8c show the updating effect of the optimization index of the subsystem 3, the subsystem 4 and the subsystem 5, respectively. As can be seen from fig. 8a, 8b, and 8c, after 175s, the tracking performance index of each subsystem starts to gradually decrease, which proves that the tracking performance of the system gradually increases.
And finishing a distributed diagnosis and control integrated framework based on the first step and the second step.
The simulation results of the distributed diagnosis and optimization control method provided by the invention are shown in fig. 3a1, 3a2, 3b1, 3b2, 3c1, 3c2, 3d1, 3d2, 3e1, 3e2, fig. 4, fig. 5, fig. 6, fig. 7, fig. 8a, 8b, and 8c, fig. 3a1, 3a2, 3b1, 3b2, 3c1, 3c2, 3d1, 3d2, 3e1, and 3e2 are distributed system fault detection diagrams, fig. 4 is a subsystem 3 tracking error index effect diagram, fig. 5 is a subsystem 4 tracking error index effect diagram, fig. 6 is a subsystem 5 tracking error index effect diagram, fig. 7 is an optimization index update effect diagram of the whole system, and fig. 8a, 8b, and 8c are optimization index update effect diagrams of subsystems 3-5.
The present invention is capable of other embodiments and its several details are capable of modifications in various obvious respects, all without departing from the spirit and scope of the present invention.

Claims (9)

1. A distributed diagnosis and optimization control method facing an assembly production line is characterized in that: the method comprises the following specific processes:
step one, designing an assembly production line distributed system, diagnosing faults of the assembly production line distributed system, and executing step two when the assembly production line distributed system is diagnosed to have faults; when the assembly production line distributed system is diagnosed not to have a fault, continuously diagnosing the fault of the assembly production line distributed system;
and step two, designing an optimal control method of the assembly production line distributed system.
2. The assembly line-oriented distributed diagnosis and optimization control method according to claim 1, wherein: designing an assembly production line distributed system in the first step, and diagnosing the faults of the assembly production line distributed system, wherein the specific process is as follows:
a1, dressThe distribution line distributed system has nsA subsystem;
setting nsThe sub-monitoring system is used for monitoring whether the sub-system is abnormal or not and carrying out optimization control on the sub-system after the abnormality occurs;
setting nsThe sub-control system is used for inputting the output of the sub-system into the controller;
a2, collecting the 1 st 1 … i subsystem G under normal working condition1(z)…Gi(z) input signal u1…uiAnd the ith subsystem G under normal operating conditionsi(z) output signal yi；1≤i≤ns；
Ith subsystem Gi(z) output signal yiRespectively transmitted to the sub-control systems
Figure FDA0003375719740000012
Performing the following steps;
a3, and 1 st 1 … i subsystem G based on A2 acquisition under normal working conditions1(z)…Gi(z) input signal and ith subsystem Gi(z) designing residual error generator RG of ith sub-monitoring system under normal working condition by using left/right co-prime decomposition technologyi；
Residual error generator RG of ith sub-monitoring system under normal working conditioniGenerating a residual error ri；
The ith subsystem G under the normal working conditioni(z) input signal uiRespectively to the i, i +1, …, nsResidual generator of sub-monitoring system
Figure FDA0003375719740000011
A4, respectively collecting residual errors r generated by residual error generators of 1 … i sub-monitoring systems under normal working conditions1…riGenerating the total residual r of the ith sub-monitoring system under the normal working condition(i)；
A5, generating the total residual r of the ith sub-monitoring system under the normal working condition according to A4(i)Designing a post filter R of the ith sub-monitoring systemf,i(z), total residual r(i)After the post-filter, residual error r is generatedf (i)；
A6, residual r generated from A5f (i)Calculating statistic, and designing a threshold value according to the statistic;
a7, collecting the 1 st 1 … i subsystem G under the online working condition1(z)…Gi(z) input signal u1…uiAnd the ith subsystem G under the online working conditioni(z) output signal yi；1≤i≤ns；
Ith subsystem Gi(z) output signal yiRespectively transmitted to the sub-control systems
Figure FDA0003375719740000021
Performing the following steps;
a8, 1 st 1 … i subsystem G based on A7 acquisition under online working condition1(z)…Gi(z) input signal and ith subsystem Gi(z) designing residual error generator RG of ith sub-monitoring system under on-line working condition by using left/right co-prime decomposition technologyi；
Residual error generator RG of ith sub-monitoring system under online working conditioniGenerating a residual error ri；
The ith subsystem G under the online working conditioni(z) input signal uiRespectively to the i, i +1, …, nsResidual generator of sub-monitoring system
Figure FDA0003375719740000022
A9, respectively collecting residual errors r generated by residual error generators of 1 … i sub-monitoring systems under online working conditions1…riGenerating the total residual r of the ith sub-monitoring system under the online working condition(i)；
A10, generating the total residual r of the ith sub-monitoring system under the online working condition according to A9(i)Designing a post filter R of the ith sub-monitoring systemf,i(z), total residual r(i)After the post-filter, residual error r is generatedf (i)；
A11, residual r generated from A10f (i)Calculating a statistic;
and A12, judging whether the assembly line distributed system has a fault.
3. The assembly line-oriented distributed diagnosis and optimization control method according to claim 2, wherein: the additional controller Qi(z) is the residual ri。
4. The assembly line-oriented distributed diagnosis and optimization control method according to claim 3, wherein: the ith sub-control system Ki(z) as input the ith subsystem Gi(z) output signal yi。
5. The assembly line-oriented distributed diagnosis and optimization control method according to claim 4, wherein: the additional controller Qi(z) output/sub-control System Ki(z) output and subsystem Gi(z) input signal uiAs subsystem Gi(z) input.
6. The assembly line-oriented distributed diagnosis and optimization control method according to claim 5, wherein: respectively collecting residual errors r generated by residual error generators of 1 … i sub-monitoring systems under normal working conditions in A41…riGenerating the total residual r of the ith sub-monitoring system under the normal working condition(i)(ii) a The expression is as follows:
r(i)＝[r1 T…ri T]T
wherein T is transposition;
respectively collecting residual errors r generated by residual error generators of 1 … i sub-monitoring systems under online working conditions in A91…riGenerating the ith sub-monitor under the on-line working conditionTotal residual r of the control system(i)(ii) a The expression is as follows:
r(i)＝[r1 T…ri T]T。
7. the assembly line-oriented distributed diagnosis and optimization control method according to claim 6, wherein: judging whether the assembly line distributed system fails in the step A12; the process is as follows:
if the statistic value calculated by the A11 is less than or equal to the threshold value designed by the A6, the assembly line distributed system is not in fault, and the diagnosis of the fault of the assembly line distributed system is continuously carried out from A1 to A12;
and if the statistic value calculated by the A11 is larger than the threshold value designed by the A6, the assembly line distributed system is judged to be in failure, and the step two is executed.
8. The assembly line-oriented distributed diagnosis and optimization control method according to claim 7, wherein: designing an optimal control method of the assembly production line distributed system in the second step; the specific process is as follows:
b1 design subsystem Gi(z) additional controller Qi(z)；
B2, additional controllers Q for respectively designing 1 st 1 … i-1 st subsystemsi1(z)…Qi(i-1)(z)；
B3 residual error generator RG of ith sub-monitoring system under on-line working condition acquired by A7iResulting residual riInput subsystem Gi(z) additional controller Qi(z)；
Collecting residual r generated by a residual generator of a1 … i-1 sub-monitoring system under the online working condition acquired by A81…ri-1Input subsystem G1(z)…Gi-1(z) additional controller Qi1(z)…Qi(i-1)(z)；
General system Gi(z) additional controller Qi(z) output signal, subsystem G1(z)…Gi-1(z) additional controlDevice Qi1(z)…Qi(i-1)(z) and on-line ith sub-control system Ki(z) the output signal is passed to the subsystem Gi(z) in (a).
9. An assembly line-oriented distributed diagnosis and optimization control system is characterized in that: the system is used for executing the distributed diagnosis and optimization control method facing the assembly production line, wherein the method comprises one of the following claims 1 to 8.
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