KPI (Key Performance Indicator) related fault diagnosis method and device for multi-mode manufacturing process

Abstract

The invention relates to the technical field of control and monitoring of an industrial production process, in particular to a KPI (Key Performance Indicator) related fault diagnosis method and device for a multi-mode manufacturing process. The method comprises the step of using supervised variant SMGU of MGU for KPI related feature extraction. The process variables and the KPI are used as inputs of the SMGU, and the SMGU effectively keeps the relation between the process variables and the KPI information by learning potential hidden states. The sparse variant SBLS of the BLS is used for fault diagnosis, so that network structure redundancy is reduced, and model overfitting is avoided. According to the method, the problems of poor universality and low diagnosis accuracy of a traditional fault diagnosis method based on deep learning are solved, and the timeliness and precision of fault diagnosis are improved. The fault diagnosis method has good expansibility, a complete training process does not need to be repeated when new training data is added, and the fault diagnosis method can be effectively applied to multi-mode manufacturing processes such as strip steel hot rolling.

Description

Technical Field

[0001] The present invention relates to the technical field of control and monitoring of industrial production processes, and in particular to a KPI-related fault diagnosis method and device for a multi-mode manufacturing process. Background Art

[0002] With the rapid advancement of computer and sensor technology, manufacturing processes are increasingly integrating large-scale information integration and strong interaction. This trend is due to the need to adapt to frequent adjustments in product specifications, changes in raw material quality, and other uncertain external factors. Compared with single-mode manufacturing processes, multi-mode manufacturing processes bring huge challenges due to the expansion of production scale and increased complexity. Fault diagnosis technology for multi-mode manufacturing processes is a key technology to improve device safety, reliability, and reduce accident risks.

[0003] In modern industrial production, safety and stability are the top priorities. Accidents or failures in the industrial production process may be detrimental to product quality and production efficiency, and may even cause casualties. Therefore, it is of great significance to develop a safe and efficient fault diagnosis technology.

[0004] Multimodal fault diagnosis can be divided into methods based on multivariate statistical analysis, machine learning, and deep learning. Early research focused on multivariate statistical analysis and machine learning methods, including mode transition probability (MTP), principal component analysis (PCA), K-nearest neighbor (KNN), K-means, etc. Methods based on statistics and machine learning have limited effectiveness for high-dimensional nonlinear and non-stationary data and cannot meet actual industrial needs. Compared with other fields, the application of deep learning methods in multimodal fault diagnosis is relatively limited. Related research uses transfer learning-based methods to deal with multimodal manufacturing processes. KPI-related fault diagnosis includes methods such as partial least squares (APLS) and autoencoders.

[0005] Most of the existing multi-mode manufacturing process fault diagnosis methods based on deep learning use static deep networks such as autoencoders. However, multi-mode manufacturing processes are dynamic in nature, and existing methods are difficult to fully extract the dynamic features of data. When extracting features, there is a lack of focus on extracting KPI-related features. At the same time, the huge network structure of the deep learning model reduces the adaptability and flexibility of the model, and the model overfits, resulting in low fault diagnosis accuracy. Summary of the invention

[0006] In order to solve the technical problem that the existing needles lack the focus on extracting KPI-related features when performing feature extraction. At the same time, the huge network structure of the deep learning model reduces the adaptability and flexibility of the model, and the model has overfitting phenomenon, resulting in low fault diagnosis accuracy, the embodiment of the present invention provides a KPI-related fault diagnosis method and device for multi-mode manufacturing processes. The technical solution is as follows:

[0007] On the one hand, a KPI-related fault diagnosis method for a multi-mode manufacturing process is provided, characterized in that the method comprises:

[0008] S1. Obtain the process variables and key performance indicator (KPI) data retained during the hot rolling process of the strip steel, and establish a historical sample database;

[0009] S2. Construct a supervised minimum gated unit SMGU, train the supervised minimum gated unit SMGU based on the historical sample database; use the supervised minimum gated unit SMGU to extract dynamic features related to key performance indicator KPI data;

[0010] S3. Construct a sparse width learning system SBLS, and input the extracted dynamic features related to the key performance indicator KPI data into the sparse width learning system SBLS for fine-grained fault diagnosis training;

[0011] S4, based on the trained supervised minimum gated unit SMGU and sparse width learning system SBLS, obtain the KPI-related fault diagnosis structure based on SMGU-SBLS;

[0012] S5. Obtain the fault data to be detected, input the fault data to be detected into the KPI-related fault diagnosis structure based on SMGU-SBLS, and obtain the fault diagnosis result.

[0013] Optionally, in S1, the process variables include: various data groups generated when steel plates of different steel grades and different plate thicknesses are generated during the hot rolling process of the strip steel;

[0014] Key performance indicator (KPI) data include: width, convexity, thickness and flatness of the strip exiting the finishing mill.

[0015] Optionally, in S2, a supervised minimum gating unit SMGU is constructed, and the supervised minimum gating unit SMGU is trained based on a historical sample database; the supervised minimum gating unit SMGU is used to extract dynamic features related to key performance indicator KPI data, including:

[0016] Construct a supervised minimum gated unit SMGU;

[0017] Based on the key performance indicator KPI data and process variables, the update rules of the supervised minimum gate control unit SMGU are constructed to obtain the network parameters of the supervised minimum gate control unit SMGU;

[0018] The output of the supervised minimum gated unit SMGU is used as the dynamic feature related to the key performance indicator KPI data.

[0019] Optionally, S3, constructing a sparse width learning system SBLS, inputting the extracted dynamic features related to the key performance indicator KPI data into the sparse width learning system SBLS for fine-grained fault diagnosis training, including:

[0020] Construct a sparse width learning system SBLS;

[0021] Based on the dynamic characteristics of the extracted key performance indicator KPI data, the augmented Lagrange multiplier is used to perform unconstrained optimization on the objective function of SBLS; wherein the unconstrained optimization includes: using block coordinate descent (BCD) to solve the intermediate variables and parameters of the objective function;

[0022] Based on the dynamic characteristics related to the key performance indicator KPI data, combined with the intermediate variables and parameters of the objective function, the fault diagnosis results are obtained.

[0023] Optionally, the fault diagnosis result includes:

[0024] Troubleshooting results That is, the output of SBLS is calculated as follows:

[0025]

[0026] in, is the weight matrix of SBLS, represents the final weight obtained after several iterations, and A represents the actual input matrix calculated by the dynamic feature F related to the key performance indicator KPI data.

[0027] Optionally, in S5, the fault data to be detected is obtained, and the fault data to be detected is input into a KPI-related fault diagnosis structure based on SMGU-SBLS to obtain a fault diagnosis result, including:

[0028] Obtaining the fault data to be detected and the fault type of the fault data to be detected;

[0029] Through the KPI-related fault diagnosis structure based on SMGU-SBLS, feature extraction is performed on the fault data to be detected, and the input matrix and weight matrix are calculated;

[0030] Fault diagnosis results are obtained based on the input matrix and the weight matrix.

[0031] On the other hand, a KPI-related fault diagnosis device for a multi-mode manufacturing process is provided, and the device is applied to a KPI-related fault diagnosis method for a multi-mode manufacturing process, and the device includes:

[0032] The data acquisition module is used to obtain the process variables and key performance indicator (KPI) data retained during the hot rolling process of the strip steel and establish a historical sample database;

[0033] The feature extraction module is used to construct a supervised minimum gated unit SMGU, train the supervised minimum gated unit SMGU based on the historical sample database, and use the supervised minimum gated unit SMGU to extract dynamic features related to key performance indicator KPI data;

[0034] The SBLS building module is used to build a sparse width learning system SBLS, and input the extracted dynamic features related to the key performance indicator KPI data into the sparse width learning system SBLS for fine-grained fault diagnosis training;

[0035] SMGU-SBLS structure building module, used to obtain KPI-related fault diagnosis structure based on SMGU-SBLS based on trained supervised minimum gated unit SMGU and sparse width learning system SBLS;

[0036] The fault diagnosis module is used to obtain the fault data to be detected, input the fault data to be detected into the KPI-related fault diagnosis structure based on SMGU-SBLS, and obtain the fault diagnosis result.

[0037] Optionally, the process variables include: various data groups generated when steel plates of different steel grades and different plate thicknesses are generated during the hot rolling process of the strip steel;

[0038] Key performance indicator (KPI) data include: width, convexity, thickness and flatness of the strip exiting the finishing mill.

[0039] On the other hand, a KPI-related fault diagnosis device for a multi-mode manufacturing process is provided, and the KPI-related fault diagnosis device for a multi-mode manufacturing process comprises: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the above-mentioned KPI-related fault diagnosis methods for a multi-mode manufacturing process is implemented.

[0040] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned KPI-related fault diagnosis methods for a multi-mode manufacturing process.

[0041] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0042] KPI-related dynamic feature extraction based on SMGU in the embodiment of the present invention: MGU greatly reduces the training complexity by simplifying the gate structure, and the training speed is significantly improved. However, the unsupervised MGU mainly learns dynamic features from process variables, and these dynamic features may contain information irrelevant to KPI. The use of supervised SMGU can fully extract the dynamic characteristics related to KPI.

[0043] Fine-grained fault diagnosis based on SBLS: The large number of nodes in BLS increases the complexity of the network. Sparse BLS simplifies the structure of traditional BLS by deleting redundant feature nodes and enhancing nodes, effectively avoiding overfitting of the model and enhancing the interpretability of the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 It is a flow chart of a KPI-related fault diagnosis method for a multi-mode manufacturing process provided by an embodiment of the present invention;

[0046] Figure 2 is a structural diagram of an SMGU provided in an embodiment of the present invention;

[0047] Figure 3 It is a KPI-related fault diagnosis structure diagram based on SMGU-SBLS provided in an embodiment of the present invention;

[0048] Figure 4 is a diagram of the expansion capability of SMGU-SBLS provided by an embodiment of the present invention;

[0049] Figure 5 is an accuracy graph under different training samples provided by an embodiment of the present invention;

[0050] Figure 6 It is a block diagram of a KPI-related fault diagnosis device for a multi-mode manufacturing process provided by an embodiment of the present invention;

[0051] Figure 7 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0053] 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.

[0054] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0055] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are consistent.

[0056] 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.

[0057] The embodiment of the present invention provides a KPI-related fault diagnosis method for a multi-mode manufacturing process, which can be implemented by a KPI-related fault diagnosis device for a multi-mode manufacturing process, and the KPI-related fault diagnosis device for a multi-mode manufacturing process can be a terminal or a server. Figure 1 The flowchart of the KPI-related fault diagnosis method for a multi-mode manufacturing process is shown. The processing flow of the method may include the following steps:

[0058] S1. Obtain the process variables and key performance indicator (KPI) data retained during the hot rolling process of the strip steel, and establish a historical sample database;

[0059] In a feasible implementation, in S1, the process variables include: various data groups generated when steel plates of different steel grades and different plate thicknesses are generated during the hot rolling process of the strip steel;

[0060] Key performance indicator (KPI) data include: width, convexity, thickness and flatness of the strip exiting the finishing mill.

[0061] In one possible implementation, key performance indicators (KPIs) are an important concept in the manufacturing process, especially those related to key parameters of production, product content, and key equipment. These KPIs require high attention because they can establish quantitative relationships between the performance of low-level technical cycles / components and high-level indicators such as product quality, production efficiency, energy consumption, and raw material consumption.

[0062] In a feasible implementation manner, the historical sample data acquired by the present invention are various data groups (normal and with different faults) generated in the past when steel plates of different steel grades and different plate thicknesses were generated, wherein the KPI includes the width, convexity, thickness, and flatness of the strip at the outlet of the finishing mill, and the process variables include the roll gap, rolling force, and rolling speed of the 1-7 stands, and the bending roll force of the 2-7 stands (there is no bending roll force in the 1 stand), etc., as shown in Table 1.

[0063] Table 1 Strip hot rolling process variables

[0064]

[0065] S2. Construct a supervised minimum gated unit SMGU, train the supervised minimum gated unit SMGU based on the historical sample database; use the supervised minimum gated unit SMGU to extract dynamic features related to key performance indicator KPI data;

[0066] In one possible implementation, a minimal gated unit (MGU) is a gated unit of a recurrent neural network (RNN). The MGU contains only one forget gate and is the smallest design among all gated hidden units. The MGU has comparable accuracy to the gated recurrent unit (GRU), but has a simpler structure, fewer parameters, and faster training speed.

[0067] In a feasible implementation, in S2, a supervised minimum gated unit SMGU is used to extract dynamic features related to key performance indicator KPI data. Compared with a conventional MGU, the SMGU uses the KPI data as another input to guide the learning of hidden states. At this time, the feature extraction is supervised learning. The method includes:

[0068] Based on the key performance indicator KPI data, the update rule of the supervised minimum gated unit SMGU is constructed to obtain the network parameters of the supervised minimum gated unit SMGU;

[0069] The update rules of the supervised minimum gated unit are summarized as follows:

[0070]

[0071] In the formula, for Always forget the door, for Time process data, for KPI value at the moment, for The candidate hidden state at time, , and is the training weight of the forget gate, , , for Weight, the first letter of the weight subscript refers to the weight belongs to or , the second letter refers to the weight or wait. and They are and The activation function of .

[0072] In a feasible implementation manner, S2 further includes:

[0073] The back propagation algorithm is used to fine-tune the network parameters of the supervised minimum gated unit SMGU:

[0074]

[0075] In the formula, for or , is the number of variables, is the learning rate. is the loss function. The SMGU structure diagram is as follows Figure 2 shown.

[0076] S3. Construct a sparse width learning system SBLS, and input the extracted dynamic features related to the key performance indicator KPI data into the sparse width learning system SBLS for fine-grained fault diagnosis training;

[0077] In a feasible implementation, the broad learning system (BLS) is an efficient incremental learning system, which includes a three-layer structure of input layer, hidden layer (feature nodes and enhancement nodes) and output layer. Unlike deep learning algorithms that train models through gradient descent back propagation, BLS uses the ridge regression learning algorithm to solve the pseudo-inverse between the hidden layer nodes and the output layer to obtain model parameters, thereby solving regression and classification problems.

[0078] In a feasible implementation, S3, based on the dynamic features related to the extracted key performance indicator KPI data, a sparse width learning system is used to perform fine-grained fault diagnosis. Compared with conventional width learning, the system enhances the interpretability of the network by deleting redundant feature nodes and enhancing nodes. The method includes:

[0079] Based on the dynamic features of the extracted key performance indicator KPI data, an additional term, and the augmented Lagrange multiplier is used to transform the objective function of SBLS into an unconstrained optimization problem:

[0080]

[0081] In the formula, is the output matrix of fault types, The actual input calculated for the KPI-related dynamic feature F, is the weight of SBLS, and are the ridge parameter and the Lasso parameter respectively, , , is the intermediate variable of the objective function, For a square array, is the Lagrange multiplier, is a positive parameter.

[0082] In a feasible implementation manner, S3 further includes:

[0083] Use block coordinate descent (BCD) to solve for intermediate variables and parameters:

[0084]

[0085] in, The calculation of is as follows:

[0086]

[0087] In the formula, , , .

[0088] In a feasible implementation manner, S3 further includes:

[0089] Based on the dynamic characteristics of key performance indicator (KPI) data, as well as the intermediate variables and parameters solved, the fault diagnosis results are obtained:

[0090] Troubleshooting results That is, the output of SBLS is calculated as follows:

[0091]

[0092] In the formula, is the optimized weight matrix, A represents the actual input matrix calculated by the dynamic feature F related to the key performance indicator KPI data; the KPI-related fault diagnosis structure diagram based on SMGU-SBLS is shown in Figure 3 shown.

[0093] S4. Based on the trained supervised minimum gated unit SMGU and sparse width learning system SBLS, the KPI-related fault diagnosis structure based on SMGU-SBLS is obtained.

[0094] S5. Obtain the fault data to be detected, input the fault data to be detected into the KPI-related fault diagnosis structure based on SMGU-SBLS, and obtain the fault diagnosis result.

[0095] In a feasible implementation manner, in S5, the to-be-detected fault data is obtained, and the to-be-detected fault data is input into the KPI-related fault diagnosis structure based on SMGU-SBLS to obtain the fault diagnosis result, including:

[0096] Obtaining the fault data to be detected and the fault type of the fault data to be detected;

[0097] Calculate the input matrix and pseudo-inverse matrix of the fault data to be detected;

[0098] Based on the input matrix and pseudo-inverse matrix of the fault data to be detected, a weight matrix of the fault data to be detected is calculated;

[0099] Calculate the fault diagnosis results based on the input matrix and the weight matrix.

[0100] In a feasible implementation, when new fault data is added, SMGU-SBLS does not need to go through a complicated retraining process and has good scalability for multi-mode manufacturing processes.

[0101] In one feasible implementation, the new fault data Include A new fault type, input matrix Its pseudo-inverse matrix is ​​calculated as follows:

[0102]

[0103] In the formula, is the augmented matrix of the input matrix, , ,if is 0, ,otherwise ;

[0104] The new weight matrix is ​​calculated as follows:

[0105]

[0106] In the formula, is the augmented matrix of the new fault, is a zero matrix, the extended structure diagram of SMGU-SBLS is as follows Figure 4 As shown. Figure 5 As shown in the figure, it can be seen that under different sample numbers, the fault diagnosis accuracy based on SMGU-SBLS is higher than that of the other four methods.

[0107] In the embodiments of the present invention, the KPI-related fault diagnosis method for multi-mode manufacturing processes provided by the present invention will provide new ideas and approaches for the hot rolling fault diagnosis of strip steel based on deep neural networks, making up for the multi-mode manufacturing process fault diagnosis problem with complex characteristics such as poor versatility and low diagnostic accuracy that are difficult to solve by existing fault diagnosis methods. It aims to explore practical and effective solutions to key challenging problems in modern industrial process fault diagnosis, and has important practical application and promotion value.

[0108] Compared with existing fault diagnosis methods, the fault diagnosis accuracy is improved by 3% to 5%.

[0109] KPI-related dynamic feature extraction based on SMGU: MGU greatly reduces the training complexity by simplifying the gate structure, and the training speed is significantly improved. However, the unsupervised MGU mainly learns dynamic features from process variables, which may contain information irrelevant to KPI. The use of supervised SMGU can fully extract the dynamic characteristics related to KPI.

[0110] Fine-grained fault diagnosis based on SBLS: The large number of nodes in BLS increases the complexity of the network. Sparse BLS simplifies the structure of traditional BLS by deleting redundant feature nodes and enhancing nodes, effectively avoiding overfitting of the model and enhancing the interpretability of the network.

[0111] Figure 61 is a block diagram of a KPI-related fault diagnosis device 300 for a multi-mode manufacturing process according to an exemplary embodiment. The device 300 is used for a KPI-related fault diagnosis method for a multi-mode manufacturing process. Figure 6 The device includes a data acquisition module 310, a feature extraction module 320, an SBLS construction module 330, an SMGU-SBLS structure construction module 340 and a fault diagnosis module 350. Among them:

[0112] The data acquisition module 310 is used to acquire the process variables and key performance indicator KPI data retained during the hot rolling process of the strip steel, and establish a historical sample database;

[0113] The feature extraction module 320 is used to construct a supervised minimum gating unit SMGU, train the supervised minimum gating unit SMGU based on the historical sample database, and extract dynamic features related to key performance indicator KPI data using the supervised minimum gating unit SMGU;

[0114] The SBLS construction module 330 is used to construct a sparse width learning system SBLS, and input the extracted dynamic features related to the key performance indicator KPI data into the sparse width learning system SBLS for fine-grained fault diagnosis training;

[0115] An SMGU-SBLS structure building module 340 is used to obtain a KPI-related fault diagnosis structure based on the SMGU-SBLS based on the trained supervised minimum gating unit SMGU and the sparse width learning system SBLS;

[0116] The fault diagnosis module 350 is used to obtain the fault data to be detected, input the fault data to be detected into the KPI-related fault diagnosis structure based on SMGU-SBLS, and obtain the fault diagnosis result.

[0117] Optionally, the process variables include: various data groups generated when steel plates of different steel grades and different plate thicknesses are generated during the hot rolling process of the strip steel;

[0118] Key performance indicator (KPI) data include: width, convexity, thickness and flatness of the strip exiting the finishing mill.

[0119] Optionally, the feature extraction module 320 is used to construct a supervised minimum gating unit SMGU;

[0120] Based on the key performance indicator KPI data and process variables, the update rules of the supervised minimum gate control unit SMGU are constructed to obtain the network parameters of the supervised minimum gate control unit SMGU;

[0121] The output of the supervised minimum gated unit SMGU is used as the dynamic feature related to the key performance indicator KPI data.

[0122] Optionally, the SBLS construction module 330 is used to construct a sparse width learning system SBLS;

[0123] Based on the dynamic characteristics of the extracted key performance indicator KPI data, the augmented Lagrange multiplier is used to perform unconstrained optimization on the objective function of SBLS; wherein the unconstrained optimization includes: using block coordinate descent (BCD) to solve the intermediate variables and parameters of the objective function;

[0124] Based on the dynamic characteristics related to the key performance indicator KPI data, combined with the intermediate variables and parameters of the objective function, the fault diagnosis results are obtained.

[0125] Optionally, the fault diagnosis result includes:

[0126] Troubleshooting results That is, the output of SBLS is calculated as follows:

[0127]

[0128] in, is the weight matrix of SBLS, represents the final weight obtained after several iterations, and A represents the actual input matrix calculated by the dynamic feature F related to the key performance indicator KPI data.

[0129] Optionally, the fault diagnosis module 350 is used to obtain the fault data to be detected and the fault type of the fault data to be detected;

[0130] Through the KPI-related fault diagnosis structure based on SMGU-SBLS, feature extraction is performed on the fault data to be detected, and the input matrix and weight matrix are calculated;

[0131] Fault diagnosis results are obtained based on the input matrix and the weight matrix.

[0132] In the embodiment of the present invention, the KPI-related fault diagnosis device for multi-mode manufacturing process will provide new ideas and approaches for the fault diagnosis of hot rolling of strip steel based on deep neural network, which makes up for the multi-mode manufacturing process fault diagnosis problem with complex characteristics such as poor versatility and low diagnostic accuracy that are difficult to solve by existing fault diagnosis methods. It aims to explore practical and effective solutions to key challenges in modern industrial process fault diagnosis, and has important practical application and promotion value. Compared with the existing fault diagnosis methods, the fault diagnosis accuracy rate is improved by 3% to 5%.

[0133] Figure 7is a schematic diagram of a KPI-related fault diagnosis device for a multi-mode manufacturing process provided by an embodiment of the present invention, such as Figure 7 As shown, the KPI-related fault diagnosis device for multi-mode manufacturing process may include the above Figure 6 The KPI-related fault diagnosis device for a multi-mode manufacturing process is shown. Optionally, the KPI-related fault diagnosis device 410 for a multi-mode manufacturing process may include a first processor 2001 .

[0134] Optionally, the KPI-related fault diagnosis device 410 for a multi-mode manufacturing process may further include a memory 2002 and a transceiver 2003 .

[0135] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0136] Combine the following Figure 7 The components of the KPI-related fault diagnosis device 410 for a multi-mode manufacturing process are specifically introduced:

[0137] The first processor 2001 is the control center of the KPI-related fault diagnosis device 410 for a multi-mode manufacturing process, and can be a processor or a general term for multiple processing elements. For example, the first processor 2001 is one or more central processing units (CPUs), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiment of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (field programmable gate arrays, FPGAs).

[0138] Optionally, the first processor 2001 may perform various functions of the KPI-related fault diagnosis device 410 for a multi-mode manufacturing process by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002 .

[0139] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 7 CPU0 and CPU1 are shown in FIG.

[0140] In a specific implementation, as an embodiment, the KPI-related fault diagnosis device 410 for a multi-mode manufacturing process may also include multiple processors, such as Figure 7 The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0141] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled to be executed by the first processor 2001. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0142] Optionally, the memory 2002 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compressed optical disc, a laser disc, an optical disc, a digital versatile disc, a Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001, or may exist independently, and may be connected to the first processor 2001 through the interface circuit ( Figure 7 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0143] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0144] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 7 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0145] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently, and may be connected to the first processor 2001 through the interface circuit ( Figure 7(not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0146] It should be noted that Figure 7 The structure of the KPI-related fault diagnosis device 410 for a multi-mode manufacturing process shown in the figure does not constitute a limitation on the router, and the actual knowledge structure identification device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0147] In addition, the technical effects of the KPI-related fault diagnosis device 410 for a multi-mode manufacturing process can refer to the technical effects of the KPI-related fault diagnosis method for a multi-mode manufacturing process described in the above method embodiment, which will not be repeated here.

[0148] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0149] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0150] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0151] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which 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.

[0157] 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 KPI-related fault diagnosis method for a multi-mode manufacturing process, characterized in that: The method comprises: S1. Obtain the process variables and key performance indicator (KPI) data retained during the hot rolling process of the strip steel, and establish a historical sample database; S2. Construct a supervised minimum gating unit SMGU, and train the supervised minimum gating unit SMGU based on the historical sample database; use the supervised minimum gating unit SMGU to extract dynamic features related to key performance indicator KPI data; S3. Construct a sparse width learning system SBLS, and input the extracted dynamic features related to the key performance indicator KPI data into the sparse width learning system SBLS for fine-grained fault diagnosis training; S4, based on the trained supervised minimum gated unit SMGU and sparse width learning system SBLS, obtain the KPI-related fault diagnosis structure based on SMGU-SBLS; S5. Acquire the fault data to be detected, input the fault data to be detected into the KPI-related fault diagnosis structure based on SMGU-SBLS, and obtain the fault diagnosis result.

2. The key performance indicator (KPI) related fault diagnosis method for multi-mode manufacturing process according to claim 1 is characterized in that: In S1, the process variables include: various data groups generated when steel plates of different steel grades and different thicknesses are generated during the hot rolling process of the strip steel; Key performance indicator (KPI) data include: width, convexity, thickness and flatness of the strip exiting the finishing mill.

3. The key performance indicator (KPI) related fault diagnosis method for multi-mode manufacturing process according to claim 2 is characterized in that: In S2, a supervised minimum gating unit SMGU is constructed, and the supervised minimum gating unit SMGU is trained based on the historical sample database; the supervised minimum gating unit SMGU is used to extract dynamic features related to key performance indicator KPI data, including: Construct a supervised minimum gated unit SMGU; Based on the key performance indicator KPI data and process variables, an update rule of a supervised minimum gating unit SMGU is constructed to obtain network parameters of the supervised minimum gating unit SMGU; The output of the supervised minimum gated unit SMGU is used as the dynamic feature related to the key performance indicator KPI data.

4. The KPI-related fault diagnosis method for a multi-mode manufacturing process according to claim 3 is characterized in that: S3, constructing a sparse width learning system SBLS, inputting the extracted dynamic features related to the key performance indicator KPI data into the sparse width learning system SBLS for fine-grained fault diagnosis training, including: Construct a sparse width learning system SBLS; Based on the extracted dynamic features related to the key performance indicator KPI data, the objective function of SBLS is optimized unconstrainedly using augmented Lagrange multipliers; wherein the unconstrained optimization includes: using block coordinate descent (BCD) to solve the intermediate variables and parameters of the objective function; Based on the dynamic characteristics related to the key performance indicator KPI data, combined with the intermediate variables and parameters of the objective function, the fault diagnosis result is obtained.

5. The KPI-related fault diagnosis method for a multi-mode manufacturing process according to claim 4, characterized in that: The fault diagnosis results include: Troubleshooting results That is, the output of SBLS is calculated as follows: ； in, is the weight matrix of SBLS, represents the final weight obtained after several iterations, and A represents the actual input matrix calculated by the dynamic feature F related to the key performance indicator KPI data.

6. The KPI-related fault diagnosis method for a multi-mode manufacturing process according to claim 5, characterized in that: In S5, the fault data to be detected is obtained, and the fault data to be detected is input into the KPI-related fault diagnosis structure based on SMGU-SBLS to obtain the fault diagnosis result, including: Obtaining the fault data to be detected and the fault type of the fault data to be detected; Through the KPI-related fault diagnosis structure based on SMGU-SBLS, feature extraction is performed on the fault data to be detected, and the input matrix and weight matrix are calculated; Fault diagnosis results are obtained based on the input matrix and the weight matrix.

7. A KPI-related fault diagnosis device for a multi-mode manufacturing process, the KPI-related fault diagnosis device for a multi-mode manufacturing process is used to implement the KPI-related fault diagnosis method for a multi-mode manufacturing process as claimed in any one of claims 1 to 6, characterized in that: The device comprises: The data acquisition module is used to obtain the process variables and key performance indicator (KPI) data retained during the hot rolling process of the strip steel and establish a historical sample database; A feature extraction module is used to construct a supervised minimum gating unit SMGU, train the supervised minimum gating unit SMGU based on the historical sample database, and extract dynamic features related to key performance indicator KPI data using the supervised minimum gating unit SMGU; SBLS building module, used to build a sparse width learning system SBLS, and input the extracted dynamic features related to the key performance indicator KPI data into the sparse width learning system SBLS for fine-grained fault diagnosis training; SMGU-SBLS structure building module, used to obtain KPI-related fault diagnosis structure based on SMGU-SBLS based on trained supervised minimum gated unit SMGU and sparse width learning system SBLS; The fault diagnosis module is used to obtain the fault data to be detected, input the fault data to be detected into the KPI-related fault diagnosis structure based on SMGU-SBLS, and obtain the fault diagnosis result.

8. The KPI-related fault diagnosis device for multi-mode manufacturing process according to claim 7, characterized in that: The process variables include: various data groups generated when steel plates of different steel grades and different plate thicknesses are generated during the hot rolling process of the strip steel; Key performance indicator (KPI) data include: width, convexity, thickness and flatness of the strip exiting the finishing mill.

9. A KPI-related fault diagnosis device for a multi-mode manufacturing process, characterized in that: The KPI-related fault diagnosis device for the multi-mode manufacturing process includes: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 6 is implemented.

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 6.

Images