System modeling and fault detection method and system for strip steel hot continuous rolling loop

Abstract

The invention discloses a strip steel hot continuous rolling loop system modeling and fault detection method and system, and relates to the technical field of industrial steel rolling process modeling and fault detection, and the method comprises the steps: building a state space model of a strip steel hot continuous rolling loop system at a plurality of stable working points based on least square identification; fuzzifying the state space model based on a preset fuzzy rule to obtain a T-S fuzzy model of the strip steel hot continuous rolling loop system; optimizing the T-S fuzzy model based on a neural network, and establishing a target system model in which the neural network and fuzzy kernel representation are combined; and performing fault detection on the strip steel hot continuous rolling loop system based on the sum of residual signals between the model output of the target system model in the preset time period and the real output of the strip steel hot continuous rolling loop system. The technical problems that in the prior art, an accurate mathematical model of a loop is difficult to establish in a model-based fault detection method, and a data-driven fault detection method is low in accuracy and difficult in fault interpretation are solved.

Description

Technical Field

[0001] The invention relates to the technical field of industrial steel rolling process modeling and fault detection, and in particular to a system modeling and fault detection method and system for a strip hot rolling looper. Background Art

[0002] Manufacturing is an important pillar industry for social development and national economy. Among them, process industries such as petrochemicals and steel metallurgy provide important support for economic growth and national major engineering construction. With the increasing requirements for product quality, production efficiency and process safety, the structure of modern process industry is becoming increasingly complex and large in scale, and is developing in the direction of large-scale and multi-process collaboration. Taking the production process of hot strip rolling as an example, its production process consists of many production processes such as heating, rough rolling, flying shear, finishing rolling, laminar cooling, and coiling. In the finishing process, the looper located between adjacent frames of the finishing mill is crucial to the stable operation of the hot rolling process and the quality of steel. On the one hand, the looper height control is used to keep the looper constant, maintain the metal flow rate equal, and ensure the stable hot rolling process; on the other hand, the looper tension control is used to maintain the constant small tension rolling of the strip, avoid the phenomenon of steel pulling and steel piling, and alleviate the influence of various disturbances on the tension. Therefore, it is very necessary to properly monitor the operation status of the looper during hot rolling. If the fault cannot be detected in time and handled effectively, it may cause the spread of faults and even a chain reaction, affecting the normal operation of the entire hot rolling process, affecting product quality at the least, and causing equipment damage, environmental pollution, property loss and casualties at the worst. Therefore, how to design a suitable fault detection method to monitor the operation status of the looper in real time is of great significance for the safe and reliable operation of the hot rolling process.

[0003] In recent years, driven by the dual drive of scientific and technological progress and market expansion, the research on fault detection technology has achieved rich results. The main research methods can be divided into the following two categories: model-based fault detection and data-driven fault detection. Model-based fault detection uses the established model to describe and predict the behavior of the system, and evaluates and makes decisions on faults by comparing the residual signal between the model prediction value and the actual output of the system. There are mainly parameter estimation method, state estimation method and Kalman filtering method. Data-driven fault detection is achieved by extracting the knowledge contained in historical data and analyzing the correlation between measurement signals through data mining or machine learning. It can be mainly divided into signal processing method, machine learning method, information fusion method, multivariate statistical analysis method, etc. In contrast, model-based fault detection has strong explanatory power, high accuracy, and does not require the accumulation of a large amount of historical data, so it has been widely used in the industrial field. However, today's complex industrial processes often have serious nonlinear characteristics such as strong coupling, hysteresis, and time-varying parameters, which makes it difficult to establish a more accurate mathematical model, thus restricting the application and development of this method. Summary of the invention

[0004] In order to solve the above technical problems existing in the prior art, the embodiment of the present invention provides a system modeling and fault detection method and system for hot strip rolling loopers. The technical solution is as follows:

[0005] On the one hand, a system modeling and fault detection method for a hot strip rolling looper is provided, the method comprising: establishing a state space model of a hot strip rolling looper system at multiple stable working points based on least squares identification; fuzzifying the state space model based on preset fuzzy rules to obtain a TS fuzzy model of the hot strip rolling looper system; optimizing the TS fuzzy model based on a neural network to establish a target system model that combines a neural network and a fuzzy kernel characterization; and performing fault detection on the hot strip rolling looper system based on a residual signal sum between a model output of the target system model within a preset time period and a true output of the hot strip rolling looper system.

[0006] Furthermore, the state space model comprises:

[0007]

[0008] Among them, u and y are the system input vector and output vector respectively. are the estimates of the system state vector and output vector respectively, Ai, Bi, Ci, Di are the system matrices at the stable operating point, i is the stable operating point number, and r is the residual.

[0009] Furthermore, the TS fuzzy model includes:

[0010]

[0011] Among them, μ(t) is the antecedent variable of the preset fuzzy rule, h i (μ(t)) is the fuzzy weight, and

[0012]

[0013] Furthermore, the TS fuzzy model is optimized based on a neural network, and a target system model combining a neural network and a fuzzy kernel representation is established, including: constructing a SKR observer for the hot strip rolling looper system based on the TS fuzzy model and historical data of the hot strip rolling looper system; and establishing a target system model combining a neural network and a fuzzy kernel representation by continuously training and optimizing the neural network to approximate the feedback part of the SKR observer.

[0014] Furthermore, based on the residual signal sum between the model output of the target system model within a preset time period and the actual output of the hot strip rolling looper system, fault detection is performed on the hot strip rolling looper system, including: constructing a fault detection statistic based on the residual signal sum between the model output of the target system model within a preset time period and the actual output of the hot strip rolling looper system; judging whether the fault detection statistic exceeds a preset detection threshold; if so, judging that a fault occurs in the hot strip rolling looper system.

[0015] Furthermore, the fault detection statistics include:

[0016]

[0017] Wherein, J is the fault detection statistic, and r is the residual between the model output and the true output.

[0018] Furthermore, the preset detection threshold includes: a maximum value of a detection statistic obtained by training the target system model based on fault-free data.

[0019] On the other hand, a system modeling and fault detection system for a hot strip rolling looper is provided, comprising: an establishment module, a fuzzy module, an optimization module and a detection module; wherein the establishment module is used to establish a state space model of the hot strip rolling looper system at multiple stable working points based on least squares identification; the fuzzy module is used to fuzzify the state space model based on preset fuzzy rules to obtain a TS fuzzy model of the hot strip rolling looper system; the optimization module is used to optimize the TS fuzzy model based on a neural network to establish a target system model that combines a neural network and a fuzzy kernel representation; the detection module is used to perform fault detection on the hot strip rolling looper system based on the residual signal sum between the model output of the target system model within a preset time period and the actual output of the hot strip rolling looper system.

[0020] On the other hand, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method provided by the present invention when executing the computer program.

[0021] On the other hand, a computer-readable storage medium is provided, in which a program code is stored. The program code can be called by a processor to execute the method provided by the present invention.

[0022] The embodiment of the present invention provides a system modeling and fault detection method and system for hot strip rolling loopers, which combines least squares identification, fuzzy dynamic modeling, neural network and other technologies, establishes a state space model of the system at different working points through least squares identification, and then fuzzifies it, and uses the powerful nonlinear fitting ability of the neural network to approximate the observer feedback part in the kernel representation (SKR) of system stability to establish a relatively accurate mathematical model of the system; on this basis, a statistic is constructed using the residual signal between the model output and the system output, and the statistic is compared with the threshold value obtained by training with fault-free data to realize fault detection of the looper system, thereby alleviating the technical problems that the accurate mathematical model of the looper is difficult to establish in the model-based fault detection method of the prior art, and the data-driven fault detection method has low accuracy and difficult fault interpretation. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0024] Figure 1 It is a flow chart of a system modeling and fault detection method of a strip hot rolling looper provided by an embodiment of the present invention;

[0025] Figure 2 This is a production process flow chart of a strip hot rolling looper system provided according to an embodiment of the present invention;

[0026] Figure 3 It is a neural network structure diagram and a schematic diagram of the cutting and splicing process provided by an embodiment of the present invention;

[0027] Figure 4 is a schematic diagram of a looper object provided by an embodiment of the present invention;

[0028] Figure 5 This is a diagram showing the effect of the least squares identification of a working point provided by an embodiment of the present invention on the looper angle fitting;

[0029] Figure 6 This is a strip tension fitting effect diagram of least squares identification at a working point provided by an embodiment of the present invention;

[0030] Figure 7 is a schematic diagram of fuzzy rules provided by an embodiment of the present invention;

[0031] Figure 8 It is a loop angle fitting effect diagram of the TS fuzzy model provided by an embodiment of the present invention;

[0032] Fig. 9 It is a strip tension fitting effect diagram of the TS fuzzy model provided by an embodiment of the present invention;

[0033] Fig.10 It is a looper angle fitting effect diagram of the TS fuzzy model and the kernel representation-based model after neural network optimization provided by the embodiment of the present invention;

[0034] Fig.11 It is a strip tension fitting effect diagram of the TS fuzzy model and the kernel characterization-based model after neural network optimization provided by the embodiment of the present invention;

[0035] Fig.12 It is a looper angle fitting error diagram of the TS fuzzy model and the kernel representation-based model after neural network optimization provided by the embodiment of the present invention;

[0036] Fig.13 is a strip tension fitting error diagram of a model based on kernel characterization after the TS fuzzy model and neural network optimization provided by an embodiment of the present invention;

[0037] Fig.14 is a fault detection curve diagram of the strip tension sensor provided by the embodiment of the present invention after a fault is introduced;

[0038] Fig.15 is a fault detection curve diagram for a rolling process with large fluctuations provided by an embodiment of the present invention;

[0039] Fig.16 It is a schematic diagram of a system modeling and fault detection system for a strip hot rolling looper provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0041] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0042] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0043] Embodiment 1

[0044] Figure 1The present invention provides a flow chart of a system modeling and fault detection method for a hot strip rolling looper according to an embodiment of the present invention, and the method is applied to a hot strip rolling looper system. Figure 2 The present invention provides a production process flow chart of a strip hot rolling looper system according to an embodiment of the present invention.

[0045] like Figure 1 As shown, the method specifically comprises the following steps:

[0046] Step S102, establishing a state space model of the hot strip rolling looper system at multiple stable working points based on least squares identification.

[0047] Step S104, fuzzifying the state space model based on preset fuzzy rules to obtain the TS fuzzy model of the strip hot rolling looper system.

[0048] Step S106, optimizing the TS fuzzy model based on the neural network, and establishing a target system model combining the neural network and the fuzzy kernel representation.

[0049] Step S108, performing fault detection on the hot strip rolling looper system based on the residual signal sum between the model output of the target system model within a preset time period and the actual output of the hot strip rolling looper system.

[0050] In step S102, considering that the looper angle output in the looper process data is stable at about 25°, the process data is divided into three stable working points according to the size of the strip tension, and the state space model of the process at different stable working points is established by least squares identification:

[0051]

[0052] Among them, u and y are the system input vector and output vector respectively. are the estimates of the system state vector and output vector, respectively. i , B i , C i , D i is the system matrix at the stable operating point, i is the stable operating point number, and r is the residual.

[0053] Then, according to the established state space model at different stable working points, the model is fuzzified using appropriate preset fuzzy rules to obtain the TS fuzzy model, including:

[0054]

[0055] Among them, μ(t) is the antecedent variable of the preset fuzzy rule, h i (μ(t)) is the fuzzy weight, and

[0056]

[0057] Specifically, step S106 also includes the following steps:

[0058] Step S1061, constructing a SKR observer of the hot strip rolling looper system based on the TS fuzzy model and the historical data of the hot strip rolling looper system;

[0059] Step S1062, by continuously training and optimizing the neural network to approximate the feedback part of the SKR observer, a target system model combining the neural network and the fuzzy kernel representation is established.

[0060] Specifically, for a complex industrial process, assume that its true model is:

[0061]

[0062] y(t)=g(x(t),u(t))+Δ g (x(t),u(t))

[0063] Among them, x, u, y are the system state vector, input vector, and output vector respectively, f and g are known model structures, Δ f , Δ g It’s the unknown part of the process.

[0064] Based on the historical data obtained from the process operation, a SKR (Stable Kernel Representation) observer for nonlinear systems can be constructed, namely:

[0065]

[0066] in, is the state estimate vector, is an estimate of the system output.

[0067] Considering that in the traditional sense, observers are mainly used to estimate the behavior of some difficult-to-measure variables in the system, and one of the purposes of the present invention is to establish a mathematical description of complex industrial processes such as loopers, the above SKR observer can be rearranged to:

[0068]

[0069] At this time, the gap between the model and the actual situation of the system is mainly reflected in the residual r. The feedback part of the SKR observer of the process can be designed by robust control and other methods to establish an accurate mathematical model of complex industrial processes such as loopers. Since the output of the model is the true output y of the process, the model can theoretically fully characterize any complex industrial process. However, considering the time-varying parameters and process uncertainty of complex industrial processes, the known parts f and g in the observer are not enough to fully describe the process, that is, there are still some unknown factors in the model at this time. In addition, the establishment of the SKR observer for nonlinear processes often requires solving the Hamilton Jacobi Equation (HJE), which is very difficult or difficult to achieve. In order to solve the above problems, the present invention approximates the feedback part of the SKR observer through a neural network, and continuously optimizes the residual r through the neural network to establish an accurate analytical model for complex industrial processes such as loopers. The specific contents are as follows:

[0070] S1, using Taylor formula expansion, the SKR observer feedback part is refined as Approximation through neural networks

[0071] S2, slices the output vector of the neural network and stacks it row by row to match The matrix output is used as the approximation of the observer feedback gain. The specific structure is as follows Figure 3 As shown;

[0072] S3, taking into account the global accuracy of the model and the high accuracy of the model at the working point, the network loss function is set as follows:

[0073]

[0074] Where n represents the number of working points, y j represents each working point, h represents a nonlinear function with a local function value, which is concretized as a Gaussian function in the present invention, namely:

[0075]

[0076] Among them, σ represents the localization degree of the Gaussian function, and k represents the importance of the additional loss term.

[0077] S4, the fourth-order Runge-Kutta method is used to solve the state differential equation and gradually update the state, that is:

[0078]

[0079] Among them, h represents the update step size.

[0080] S5, use the Xavier initialization method to assign initial weights to the network, the initial state of the system Assign 0 and use the gradient descent method to update the neural network until the Loss drops to a lower level, at which point the model matching is high enough.

[0081] Specifically, step S108 also includes the following steps:

[0082] Step S1081, constructing a fault detection statistic based on the residual signal sum between the model output of the target system model within a preset time period and the actual output of the strip hot rolling looper system;

[0083] Step S1082, determining whether the fault detection statistic exceeds a preset detection threshold; if so, determining that a fault occurs in the hot strip rolling looper system.

[0084] Specifically, the fault detection statistics include:

[0085]

[0086] Where J is the fault detection statistic and r is the residual between the model output and the true output.

[0087] In an optional implementation provided by an embodiment of the present invention, the preset detection threshold includes: the maximum value of the detection statistic obtained by training the target system model based on fault-free data, that is:

[0088]

[0089] Among them, J th is the preset detection threshold.

[0090] During the online detection process, the target system model is applied to the online data to observe whether the change of J(r) exceeds the preset detection threshold to determine whether a fault has occurred in the process, that is:

[0091]

[0092] The detection statistic adopted in the present invention is applicable to general complex industrial processes, avoids the assumption that the Q statistic and the T2 statistic require the process data to obey a certain distribution, and has greater application value.

[0093] From the above description, it can be seen that the present invention provides a system modeling and fault detection method for hot strip rolling loopers, which combines least squares identification, fuzzy dynamic modeling, neural network and other technologies, establishes a state space model of the system at different working points through least squares identification, and then fuzzifies it, and uses the powerful nonlinear fitting ability of the neural network to approximate the observer feedback part in the kernel representation (SKR) of system stability to establish a more accurate mathematical model of the system; on this basis, a statistic is constructed using the residual signal between the model output and the system output, and the statistic is compared with the threshold obtained by training with fault-free data to realize fault detection of the looper system, which alleviates the technical problems that the accurate mathematical model of the looper is difficult to establish in the model-based fault detection method of the prior art and the data-driven fault detection method has low accuracy and difficult fault interpretation.

[0094] Embodiment 2

[0095] This example uses the real process data of the looper system during the finishing rolling process to verify the effectiveness of the system modeling and fault detection method for the hot strip rolling looper proposed in the present invention. This example takes the looper between the No. 1 rolling mill and the No. 2 rolling mill during the finishing rolling process as the research object. Figure 4 As shown, the main frame speed and the valve core displacement of the hydraulic control valve are used as process inputs, the angle of the looper and the tension of the slab are used as process outputs, the sampling time is 0.1s, and stable data are selected to carry out the experiment:

[0096] I, least squares identification. According to the variation range of the strip tension during the process, the process data is divided into three stable working points. The system state space model established by the least squares identification of one working point is:

[0097]

[0098] The least squares identification fitting effect at one working point is as follows: Figure 5 , Figure 6 As shown. Among them, Figure 5 This is a looper angle fitting effect diagram of least squares identification at a working point provided by an embodiment of the present invention. Figure 6 It is a strip tension fitting effect diagram of least squares identification at a working point provided by an embodiment of the present invention.

[0099] II, TS fuzzy modeling. Figure 7 The triangle fuzzy rule shown in the figure fuzzifies the state space model at the three working points. The fuzzy model fitting effect is shown in Figure 8 , Fig. 9 As shown. Among them, Figure 8 is a loop angle fitting effect diagram of the TS fuzzy model provided by an embodiment of the present invention, Fig. 9It is a strip tension fitting effect diagram of the TS fuzzy model provided according to an embodiment of the present invention.

[0100] III. Neural network optimization. Based on the TS fuzzy model, the neural network is used to optimize the model and a system model combining neural network and fuzzy kernel representation is established. The model fitting effect is as follows: Fig.10 , Fig.11 As shown, the model fitting error is Fig.12 , Fig.13 As shown. Among them, Fig.10 is a looper angle fitting effect diagram of a model based on kernel representation after TS fuzzy model and neural network optimization provided by an embodiment of the present invention, Fig.11 is a strip tension fitting effect diagram of a model based on kernel characterization after TS fuzzy model and neural network optimization provided by an embodiment of the present invention, Fig.12 is a looper angle fitting error diagram of a model based on kernel representation after TS fuzzy model and neural network optimization provided by an embodiment of the present invention, Fig.13 It is a strip tension fitting error diagram of the TS fuzzy model and the kernel characterization-based model after neural network optimization provided by an embodiment of the present invention.

[0101] In order to quantify the accuracy of the model, the following formula is used to test the model:

[0102]

[0103] After testing, the model's fitting accuracy for the looper angle and strip tension were 93.59% and 94.09% respectively, and the model accuracy was relatively high.

[0104] IV. Fault detection. Since the looping process is sensitive to faults, in order to test the method proposed in the present invention, a fault is introduced into the strip tension sensor after the 400th sampling point. The detection effect is as follows: Fig.14 Similarly, after the 400th sampling point, the data with large fluctuations in the rolling process are considered as faults, and the detection effect is as follows Fig.15 The fault detection effect is excellent, and the accuracy rate is higher than 98%.

[0105] Embodiment 3

[0106] Fig.16 Schematic diagram of a system modeling and fault detection system for a hot strip rolling looper provided according to an embodiment of the present invention. Fig.16 As shown, the system includes: an establishment module 10, a fuzzy module 20, an optimization module 30 and a detection module 40.

[0107] Specifically, a module 10 is established to establish a state space model of the hot strip rolling looper system at multiple stable working points based on least squares identification.

[0108] The fuzzy module 20 is used to fuzzify the state space model based on preset fuzzy rules to obtain the TS fuzzy model of the strip hot rolling looper system.

[0109] The optimization module 30 is used to optimize the TS fuzzy model based on the neural network and establish a target system model combining the neural network and the fuzzy kernel representation.

[0110] The detection module 40 is used to perform fault detection on the hot strip rolling looper system based on the residual signal sum between the model output of the target system model within a preset time period and the actual output of the hot strip rolling looper system.

[0111] Specifically, the optimization module 30 is also used to: construct an SKR observer for the hot strip rolling looper system based on the TS fuzzy model and the historical data of the hot strip rolling looper system; and establish a target system model combining the neural network and the fuzzy kernel representation by continuously training and optimizing the neural network to approximate the feedback part of the SKR observer.

[0112] Specifically, the detection module 40 is also used to: construct a fault detection statistic based on the residual signal sum between the model output of the target system model within a preset time period and the actual output of the hot strip rolling looper system; determine whether the fault detection statistic exceeds a preset detection threshold; if so, determine that a fault has occurred in the hot strip rolling looper system.

[0113] The present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the method provided by the present invention is implemented when the processor executes the computer program.

[0114] The present invention also provides a computer-readable storage medium, in which program codes are stored. The program codes can be called by a processor to execute the method provided by the present invention.

[0115] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0116] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0117] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0118] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0119] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0120] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0121] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0122] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A system modeling and fault detection method for hot strip rolling looper, characterized in that: The method comprises: The state space model of the hot strip rolling looper system at multiple stable working points is established based on least squares identification. Fuzzifying the state space model based on preset fuzzy rules to obtain a TS fuzzy model of the strip hot rolling looper system; The TS fuzzy model is optimized based on a neural network, and a target system model combining a neural network and a fuzzy kernel representation is established; Based on the residual signal sum between the model output of the target system model within a preset time period and the actual output of the hot strip rolling looper system, fault detection is performed on the hot strip rolling looper system.

2. The method according to claim 1, characterized in that The state space model comprises: Among them, u and y are the system input vector and output vector respectively. are the estimates of the system state vector and output vector respectively, Ai, Bi, Ci, Di are the system matrices at the stable operating point, i is the stable operating point number, and r is the residual.

3. The method according to claim 2, characterized in that The TS fuzzy model includes: Among them, μ(t) is the antecedent variable of the preset fuzzy rule, h i (μ(t)) is the fuzzy weight, and 4. The method according to claim 1, characterized in that: The TS fuzzy model is optimized based on the neural network, and a target system model combining the neural network and the fuzzy kernel representation is established, including: Based on the TS fuzzy model and the historical data of the strip hot rolling looper system, constructing the SKR observer of the strip hot rolling looper system; By continuously training and optimizing the neural network to approximate the feedback part of the SKR observer, a target system model combining the neural network and the fuzzy kernel representation is established.

5. The method according to claim 1, characterized in that: Based on the residual signal sum between the model output of the target system model within a preset time period and the real output of the strip hot rolling looper system, fault detection is performed on the strip hot rolling looper system, including: Constructing a fault detection statistic based on the residual signal sum between the model output of the target system model within a preset time period and the real output of the strip hot rolling looper system; Determining whether the fault detection statistic exceeds a preset detection threshold; If yes, it is determined that the strip hot rolling looper system fails.

6. The method according to claim 5, characterized in that The fault detection statistics include: Wherein, J is the fault detection statistic, and r is the residual between the model output and the true output.

7. The method according to claim 5, characterized in that The preset detection threshold includes: a maximum value of a detection statistic obtained by training the target system model based on fault-free data.

8. A system modeling and fault detection system for hot strip rolling loopers, characterized in that: include: Establishing module, fuzzy module, optimization module and detection module; among them, The establishment module is used to establish a state space model of the strip hot rolling looper system at multiple stable working points based on least squares identification; The fuzzy module is used to fuzzify the state space model based on preset fuzzy rules to obtain the TS fuzzy model of the strip hot rolling looper system; The optimization module is used to optimize the TS fuzzy model based on a neural network and establish a target system model that combines a neural network and a fuzzy kernel representation; The detection module is used to perform fault detection on the hot strip rolling looper system based on the residual signal sum between the model output of the target system model within a preset time period and the actual output of the hot strip rolling looper system.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the method according to any one of claims 1 to 7.

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