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CN116203907B - Chemical process fault diagnosis alarm method and system - Google Patents

Chemical process fault diagnosis alarm method and system Download PDF

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CN116203907B
CN116203907B CN202310304189.2A CN202310304189A CN116203907B CN 116203907 B CN116203907 B CN 116203907B CN 202310304189 A CN202310304189 A CN 202310304189A CN 116203907 B CN116203907 B CN 116203907B
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CN116203907A (en
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索雷明
孙文
彭甜
赵环宇
蒋雄杰
张楚
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Taian Ruitai New Material Co ltd
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Huaiyin Institute of Technology
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    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by quality surveillance of production
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32252—Scheduling production, machining, job shop
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract

本发明公开了一种化工过程故障诊断报警方法及系统。由数据采集模块、数据预处理模块、模型训练模块、故障诊断模块和报警模块组成。数据采集模块对原始化工过程数据进行采集。数据预处理模块对采集到的原始数据集进行分解和降维处理，提高数据利用率。模型训练模块构建基于STGCN的化工过程故障诊断模型，并通过化工过程的历史数据与改进ChOA算法对建立的故障诊断模型进行训练。故障诊断模块，用于训练故障模型并诊断故障是否发生，并在诊断出故障时判定故障类型。报警模块，用于在诊断出故障时，报警模块会发出警报并显示故障类型，从而提醒工厂与工人及时处理，提高工厂运行效率、使工厂维持安全稳定的生产过程。

The invention discloses a chemical process fault diagnosis and alarm method and system. It consists of data acquisition module, data preprocessing module, model training module, fault diagnosis module and alarm module. The data acquisition module collects original chemical process data. The data preprocessing module decomposes and reduces the dimensionality of the collected original data sets to improve data utilization. The model training module constructs a chemical process fault diagnosis model based on STGCN, and trains the established fault diagnosis model through historical data of the chemical process and the improved ChOA algorithm. The fault diagnosis module is used to train the fault model and diagnose whether a fault occurs, and determine the fault type when a fault is diagnosed. The alarm module is used when a fault is diagnosed. The alarm module will issue an alarm and display the fault type, thereby reminding the factory and workers to deal with it in a timely manner, improving factory operation efficiency and maintaining a safe and stable production process.

Description

Chemical process fault diagnosis alarm method and system
Technical Field
The invention belongs to the field of fault diagnosis of chemical processes, and particularly relates to a fault diagnosis alarm method and system for the chemical processes.
Background
Along with the increasing expansion of the modern industrial production scale and the increasing complexity of process equipment, the safety and stable operation of the chemical production process are also faced with the increasing complexity test. Although the safety management of the chemical production process is continuously improved in recent years in China, and the accident occurrence of the chemical production process is striven for, the chemical production accident still happens. This is mainly affected by two factors: 1. the probability of being smaller under the huge production volume of the second major economic body and the first major industrial country in the world is continuously amplified, so that the chemical production accidents show larger total quantity. 2. The chemical production process is a very complex system, and has the characteristics of large scale, multiple interference, complex fault types, strong real-time performance and the like, and the traditional diagnosis method is difficult to simultaneously meet the accuracy, stability and diagnosis efficiency of diagnosis. And most serious accidents are gradually evolved from tiny problems in production and management, and the teaching and training is not used for reminding all people of the safety problem in the field of chemical production at any time.
The problem of fault diagnosis of industrial processes has been widely studied in the last decades and has achieved great research results. Therefore, a learner classifies these fault diagnosis methods according to different attributes: based on analytical models, qualitative experience, and data driving. In the current big data age background, data-driven fault diagnosis systems are increasingly favored by expert students. In the face of a complex chemical production process, deep learning can process a large amount of nonlinear and multidimensional data more efficiently, and has higher classifying and learning capabilities on the data. Thus, deep learning is becoming a major research direction in data driving.
The existing chemical production process has high data dimension and large data volume, and the diagnosis efficiency of the simple deep learning model is difficult to fully develop. Meanwhile, the deep learning network parameters are complex, and the parameters need to be adjusted according to different data. Therefore, the search for more valuable data and more efficient methods of optimizing parameters is one of the key steps to improve diagnostic efficiency and improve diagnostic accuracy.
Disclosure of Invention
The invention aims to: aiming at the problems pointed out in the background art, the invention discloses a chemical process fault diagnosis alarm method and a system, which build a chemical process fault diagnosis model by utilizing improved ChOA (gas turbine oil turbine) optimized STGCN (gas turbine oil turbine) and aim at improving the utilization of fault information and effectively improving the fault diagnosis efficiency of the chemical process.
The technical scheme is as follows: the invention provides a fault diagnosis and alarm method for a chemical process, which comprises the following steps:
step 1: acquiring an operation variable and a process variable in the Tennex Issman TE process as historical data; according to different fault types, adding fault type labels, and constructing original data sets of different faults;
step 2: decomposing and dimension-reducing the collected original data set; the acquired data sets of different fault types are used as the input of a Multivariate Empirical Mode Decomposition (MEMD), the MEMD decomposition method projects the multivariate input data to a higher dimension, and the signals are decomposed into different components after the multivariate input is cooperatively considered; the decomposed components are further analyzed by adopting kernel principal components to analyze KPCA, important components in the component signals are extracted, and the data dimension is reduced;
step 3: establishing a chemical process fault diagnosis model based on STGCN, and optimizing key parameters of the chemical process fault diagnosis model based on STGCN by utilizing an improved chimpanzee optimization algorithm ChOA; the improved chimpanzee optimization algorithm ChOA uses chaos to initialize population and adds double self-adaptive weight;
step 4: training the established chemical process fault diagnosis model based on the STGCN by utilizing the acquired Tenn Islaman original data set and an improved ChOA algorithm, solving the optimal parameters of the STGCN network and minimizing the loss function error; diagnosing the Tennesie Issmann process data by using the trained and optimized chemical process fault diagnosis model to obtain a diagnosis result and calculate the accuracy rate;
step 5: and (3) judging whether to send out an alarm according to the diagnosis result in the step (4), thereby reminding factories and workers to process in time and displaying the fault type when giving an alarm.
Further, the decomposing step of decomposing the history data into a plurality of different frequencies by using the MEMD decomposition model in the step 2 is as follows:
step 2.1: for the original polynomial process data s (t) = [ s ] 1 (t)，s 2 (t)…，s n (t)] T The projection vector is whereinIs the kth projection vector along the angle on the unit sphere of (n-1), k=1, 2., K, K is the total number of projection vectors;
step 2.2: after obtaining the projection vector U of the direction vector set, s (t) is calculated along the projection vectorMapping value of (2), recorded as->The calculation formula is as follows:
step 2.3: extracting the mapping signalInstantaneous time when taking local extremum +.>
Step 2.4: for a pair ofInterpolation operation is carried out on the extreme points by using a multi-element spline interpolation method to obtain a multi-element projection envelope line +.> Is->Vector direction signal s l The envelope of (t) is such that, l=1, 2,. -%, n;
step 2.5: calculating the local mean value of the multi-element signals:
step 2.6: calculating the difference d (t) between the multi-element input sequence and the local mean value:
d(t)＝s(t)-m(t)
step 2.7: if d (t) meets the requirement of multi-variable IMF, the condition is marked as d i (t), d i (t) is the component after the ith decomposition, and the component in the original signal is removed to obtain a new original signal k i (t)，i＝1，2，...，m：
k i (t)＝s(t)-d(t)
Step 2.8: repeating the steps 2.2-2.7 until d (t) does not meet the IMF requirement, and recording d (t) as residual error r (t) to obtain final multi-component decomposition signal S (t) = [ k ] 1 (t)，k 2 (t)，...，k m (t)，r(t)]。
Further, the improved chimpanzee optimization algorithm in step 3 includes the steps of:
step 3.1: setting an objective function of the ChOA algorithm as a diagnosis accuracy and initializing related parameters, including: initial position, population scale, iteration number;
step 3.2: in the original chimpanzee algorithm, the initialization is randomly generated according to the dimension and the number of input parameters, so that the logical chaos initialization is introduced in the chimpanzee population initialization process, 4 chimpanzee populations are subjected to wider preliminary search, the search efficiency of the algorithm is improved, and the expression is as follows:
where k (n+1) is the updated individual position and λ is the control variable;
step 3.3: according to the labor division of chimpanzees, the population is divided into 4 classes: common members responsible for driving (Driver) and intercepting (barrer) prey; the primary chase (Chaser) process responsible for young adult chimpanzees; a leader (Attacker) of the prey; chimpanzees have the ability to independently think in a population, and in some cases, confusing hunting behavior;
step 3.4: during the process of chimpanzee hunting, the chimpanzee needs to determine the direction and distance of the next action based on the distance between itself and the prey:
d＝|cx prey (t)-mx chimp (t)|
x chimp (t+1)＝x prey (t)-ad
wherein d is the distance between the prey and the chimpanzee; t is the current number of iterations is the distance between the prey and the chimpanzee; x is x prey (t) is the current location of the prey; x is x chimp (t) is the current location of the chimpanzee; a, m, c are coefficient vectors;
step 3.5: each chimpanzee independently determines the hunting process, i.e. the position vector between each chimpanzee and the prey, according to its own labor division; after the 4 chimpanzees and the prey have determined their position vectors, each chimpanzee updates its position based on the best chimpanzee position and estimates the position of the prey based on the best chimpanzee individual position as follows:
wherein ,dAttacker ,d Barrier ,d Chaser ,d Driver Distance from the prey at the stages of attacking, intercepting, chase and driving the chimpanzee, respectively; x is x Attacker ,x Barrier ,x Chaser ,x Driver Is the vector of the position of the attacking chimpanzee, intercepting chimpanzee, chasing chimpanzee, driving chimpanzee relative to the prey, a 1 ～a 4 ，c 1 ～c 4 ，m 1 ～m 4 Vector coefficients of four chimpanzees, respectively;
step 3.6: in the optimization process, the normal position update of chimpanzees or the position update through a chaotic model is selected, the probability of selection is 50%, and the formula is as follows:
wherein μ is a random number within the range of [0,1 ];
step 3.7: judging whether the maximum iteration times are reached, if not, entering a step 3.2 by the ChOA algorithm; otherwise, ending the operation and outputting a final result.
Further, key parameters of the chemical process fault diagnosis model of the STGCN include: the learning rate, the number of hidden layer nodes and the training iteration number of the STGCN model are used for optimizing key parameters of the chemical process fault diagnosis model based on the STGCN by utilizing an improved chimpanzee optimization algorithm ChOA, and the population is the key parameters to be optimized;
step 4.1: dividing the data in the step 2 into a training set and a testing set for model training and iteratively optimizing key parameters of the STGCN model;
step 4.2: key parameters of the STGCN model are fed into ChOA: the dimension is 3, the dimension is determined according to the kind of key parameters, and the initial value is obtained by chaotic initialization in the step 3.2;
step 4.3: the STGCN model carries out model training according to the training set and the key parameters in the step 4.2, records the diagnosis training result and the accuracy and transmits the diagnosis training result and the accuracy back to the ChOA algorithm, and the accuracy calculation formula is as follows;
the Accuracy Accuracy is the ratio of the fault type of correct classification to the total fault sample ALL, the correct classification refers to the fact that the positive sample TP is searched and the positive sample TN is not searched, the Accuracy is sent to the ChOA and recorded as an adaptability value, and the Accuracy is used as an algorithm iterative optimization index;
step 4.4: repeating the step 4.3, comparing and finding out the optimal fitness value, and recording the key parameters optimized by the iteration until the iteration of the ChOA algorithm is finished;
step 4.5: and (3) taking the optimal key parameters in the step (4.4) as the final use parameters of the model, and sending the final use parameters into a test set for testing to obtain the diagnosis result of the chemical process fault diagnosis model of the final STGCN.
Further, the training of the established STGCN-based chemical process fault diagnosis model by using the collected tennessee iman original data set and the improved ChOA algorithm in the step 4 specifically includes the following steps:
step 4.1: constructing an adjacent matrix A according to the data structure in the step 2, normalizing node parameters of the adjacent matrix A, and forming graph data needed by a model:
wherein ,representing node x i Mean, sigma of i Representing the standard deviation of the node.
Step 4.2: constructing a multi-node graph G according to chemical process data and time relation i ：
G i ＝(x i ,E,A)
Wherein E represents a set of edges between nodes;
step 4.3: a time convolution module is formed by a gating linear unit GLU and a one-dimensional convolution network and is used for capturing time characteristics; the gating linear unit can select to transfer the needed information to the next node, and the expression formula is as follows:
where M and N are different convolution kernels, c 1 and c2 Is a different bias parameter;
step 4.4: the graph convolution module is used for extracting high-order signs on a space domain, the relevance and the global property of fault data in a chemical process are fully utilized, and a Chebyshev polynomial approximation is adopted in convolution, and a convolution formula is as follows:
where Z is the graph convolution kernel size, T Z Is a polynomial expansion approximation of the Laplace matrix, Θ z Is a polynomial coefficient and the final graph convolution can be expressed as:
wherein ,Di and D0 The size of the feature map is input and output, and D represents dimension features;
step 4.5: and finally, performing inverse normalization processing on the obtained data, and outputting the fault type according to the test result.
The invention also discloses a chemical process fault diagnosis alarm system, which comprises a data acquisition module, a data preprocessing module, a model training module, a fault diagnosis module and an alarm module;
the data acquisition module is used for acquiring an operation variable and a process variable in the Tennex Issmann TE process as historical data; according to different fault types, adding fault type labels, and constructing original data sets of different faults;
the data preprocessing module is used for decomposing and dimension-reducing the acquired original data set; the acquired data sets of different fault types are used as the input of a Multivariate Empirical Mode Decomposition (MEMD), the MEMD decomposition method projects the multivariate input data to a higher dimension, and the signals are decomposed into different components after the multivariate input is cooperatively considered; the decomposed components are further analyzed by adopting kernel principal components to analyze KPCA, important components in the component signals are extracted, and the data dimension is reduced;
the model training module is used for establishing a chemical process fault diagnosis model based on the STGCN, training the established chemical process fault diagnosis model based on the STGCN through the acquired Tennex Issman original data set and an improved ChOA algorithm, solving the optimal parameters of the network and minimizing the loss function error;
and the fault diagnosis module is used for diagnosing the Tenn Islaman process data by using the trained and optimized chemical process fault diagnosis model to obtain a diagnosis result and judging the fault type when the fault is diagnosed.
And the alarm module is used for sending out an alarm and displaying the fault type when the fault is diagnosed, so that a factory and workers are reminded of timely processing.
The beneficial effects are that:
1. the invention uses MEMD decomposition and KPCA to reconstruct the input signal, removes the influence of noise on model precision and effectively reduces the input dimension. 2. Aiming at the defect that the ChOA algorithm is easy to fall into local optimum, the invention provides an improved ChOA algorithm, and the optimization capacity of the ChOA algorithm is enhanced and the diagnosis performance of a model is improved by using chaos to initialize a population and adding double self-adaptive weights. 3. The invention utilizes the improved ChOA algorithm to synchronously optimize the MEMD and the STGCN, and captures the potential relation between the input characteristic factors and the model parameters better.
Drawings
FIG. 1 is a system flow diagram of the present invention;
FIG. 2 is a flow chart of the proposed fault diagnosis method;
fig. 3 is a structural diagram of STGCN;
fig. 4 is a schematic diagram of TE process.
Detailed Description
Embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.
The invention provides a chemical process fault diagnosis alarm system, which is provided with a chemical process fault diagnosis alarm method, as shown in figure 1, and specifically comprises a data acquisition module, a data preprocessing module, a model training module, a fault diagnosis module and an alarm module.
The data acquisition module is used for acquiring operation variables and process variables in the Tennex Issmann (TE) production process as historical data; and adding fault type labels according to different fault types, and constructing original data sets of different faults. Referring to fig. 4, the Tennessee Eastern (TE) production process is a conventional production process in the chemical field, and specific processes are not described herein, wherein a dotted line is a control loop, and a solid line is a pipeline.
There are 21 different types of faults in the TE process: the faults 1 to 15 and 21 are preset fault types, and the faults 16 to 20 are unknown faults caused by uncontrollable factors.
The data preprocessing module is used for decomposing and dimension-reducing the acquired original data set; the acquired data sets of different fault types are used as the input of multi-element empirical mode decomposition (MEMD), the MEMD decomposition method can project multi-element input data to a higher dimension, and the signals are decomposed into different components after the multi-element input is considered cooperatively; the decomposed components are further subjected to Kernel Principal Component Analysis (KPCA), important components in the component signals are extracted, and the data dimension is reduced.
The decomposition steps of the MEMD decomposition model are as follows:
for the original polynomial process data s (t) = [ s ] 1 (t)，s 2 (t)…，s n (t)] T The projection vector is whereinIs the kth projection vector along the angle on the unit sphere of (n-1), k=1, 2.
After obtaining the projection vector U of the direction vector set, s (t) is calculated along the projection vectorIs recorded as the mapping value of (2)The calculation formula is as follows:
extracting the mapping signalInstantaneous time when taking local extremum +.>
For a pair ofInterpolation operation is carried out on the extreme points by using a multi-element spline interpolation method to obtain a multi-element projection envelope line +.> Is->Vector direction signal s l Envelope of (t), l=1, 2,..n.
Calculating the local mean value of the multi-element signals:
calculating the difference d (t) between the multi-element input sequence and the local mean value:
d(t)＝s(t)-m(t)
if d (t) meets the requirement of multi-variable IMF, the condition is marked as d i (t), d i (t) is the component after the ith decomposition. And removing the component in the original signal to obtain a new original signal k i (t)，i＝1，2，...，m：
k i (t)＝s(t)-d(t)
Repeating the steps until d (t) does not meet the requirement of IMF, and recording d (t) as residual error r (t). Obtaining a final multivariate decomposition signal S (t) = [ k ] 1 (t)，k 2 (t)，...，k m (t)，r(t)]。
The model training module is used for establishing a chemical process fault diagnosis model based on the STGCN, training the established chemical process fault diagnosis model based on the STGCN through the acquired Tenn Islaman original data set and an improved chimpanzee optimization algorithm ChOA, solving the optimal key parameters of the network and minimizing the loss function error.
The fault diagnosis module is used for diagnosing the tennessee Issmann process data by using the trained and optimized chemical process fault diagnosis model to obtain a diagnosis result, and judging the fault type when the fault is diagnosed. And finally, performing inverse normalization processing on the obtained data, and outputting the fault type according to the test result.
The STGCN model comprises the following specific steps:
and 2, constructing an adjacent matrix A according to the data structure in the step 2, and normalizing node parameters of the adjacent matrix A to form graph data required by the model.
wherein ,representing node x i Mean, sigma of i Representing the standard deviation of the node.
Constructing a multi-node graph G according to chemical process data and time relation i 。
G i ＝(x i ,E,A)
Where E represents the set of edges between nodes.
A time convolution module is formed by a Gated Linear Unit (GLU) and a one-dimensional convolution network for capturing time characteristics. The gating linear unit can select to transfer the needed information to the next node, and the expression formula is as follows:
where M and N are different convolution kernels, c 1 and c2 Are different bias parameters.
The graph convolution module is used for extracting high-order signs on a spatial domain, and the relevance and the global property of the fault data of the chemical process are fully utilized. In the convolution, chebyshev polynomial approximation is adopted, and the convolution formula is as follows:
where Z is the graph convolution kernel size, T Z Is a polynomial expansion approximation of the Laplace matrix, Θ z Is a polynomial coefficient and the final graph convolution can be expressed as:
wherein ,Di and D0 Is the size of the input and output feature map, D represents the dimension feature.
The improved ChOA algorithm comprises the following steps:
setting an objective function of the ChOA algorithm as a diagnosis accuracy and initializing related parameters, including: initial position, population size, iteration number. The parameters to be optimized are input into the improved chimpanzee optimization algorithm and then exist in the form of population, so that the population scale and the iteration number of the optimized real content are selected by experience.
In the original chimpanzee algorithm, the initialization is randomly generated from the dimensions and number of input parameters. Therefore, the Logistic chaos initialization is introduced in the chimpanzee population initialization process, 4 chimpanzee populations can be subjected to wider preliminary search, the search efficiency of an algorithm is improved, and the expression is as follows:
where k (n+1) is the updated individual position and λ is the control variable.
According to the labor division of chimpanzees, the population is divided into 4 classes: common members responsible for driving (Driver) and intercepting (barrer) prey; the primary chase (Chaser) process responsible for young adult chimpanzees; a leader (Attacker) of hunting. Chimpanzees have the ability to independently think in a population, and in some cases, confusing hunting behavior.
During the process of chimpanzee hunting, the chimpanzee needs to determine the direction and distance of the next action based on the distance between itself and the prey.
d＝|cx prey (t)-mx chimp (t)|
x chimp (t+1)＝x prey (t)-ad
Wherein d is the distance between the prey and the chimpanzee; t is the current number of iterations is the distance between the prey and the chimpanzee; x is x prey (t) is the current location of the prey; x is x chimp (t) is a chimpanzeeA current location; a, m, c are coefficient vectors. Since the aggregation of chimpanzees is different for each class, its coefficient vector update needs to be iterated using different formulas.
Each chimpanzee independently determines the course of the hunting, i.e. the position vector between each chimpanzee and the prey, based on its own labor division. After the 4 chimpanzees and the prey have determined their position vectors, each chimpanzee updates its position based on the best chimpanzee position and estimates the position of the prey based on the best chimpanzee individual position as follows:
wherein ,dAttacker ,d Barrier ,d Chaser ,d Driver Distance from the prey at the stages of attacking, intercepting, chase and driving the chimpanzee, respectively; x is x Attacker ,x Barrier ,x Chaser ,x Driver Is a vector of the positions of the chimpanzee relative to the prey that attacks the chimpanzee, intercepts the chimpanzee, chases the chimpanzee, and drives the chimpanzee. a, a 1 ～a 4 ，c 1 ～c 4 ，m 1 ～m 4 The vector coefficients of the four chimpanzees are respectively.
Driven by instinct and other factors, chimpanzees may also break through the current territory to hunting, avoiding ChOA from falling into local optima and slow convergence when solving high-dimensional problems. In the optimization process, the normal position update of chimpanzees or the position update through a chaotic model is selected, the probability of selection is 50%, and the formula is as follows:
wherein μ is a random number in the range of [0,1 ].
Judging whether the maximum iteration times are reached, if not, entering a chimpanzee population initialization process by the ChOA algorithm; otherwise, ending the operation and outputting a final result.
The learning rate, the number of hidden layer nodes and the training iteration number of the STGCN model are used for optimizing key parameters of the chemical process fault diagnosis model based on the STGCN by utilizing an improved chimpanzee optimization algorithm ChOA, and the population is the key parameters to be optimized, and specifically comprises the following steps:
step 4.1: dividing the data after the data preprocessing module into a training set and a testing set for model training and key parameters of the iterative optimization STGCN model.
Step 4.2: key parameters of the STGCN model are fed into ChOA: the dimension is 3, and the dimension is determined according to the type of the key parameters, in this embodiment, the key parameters are the learning rate, the number of hidden layer nodes and the training iteration number, and the initial value is obtained by chaotic initialization.
Step 4.3: the STGCN model carries out model training according to the training set and the key parameters in the step 4.2, records the diagnosis training result and the accuracy and transmits the diagnosis training result and the accuracy back to the ChOA algorithm, and the accuracy calculation formula is as follows;
the Accuracy Accuracy is the ratio of the fault type of correct classification to the total fault sample ALL, the correct classification refers to the fact that the positive sample TP is searched and the positive sample TN is not searched, and the Accuracy is sent to the ChOA and recorded as an adaptability value and used as an algorithm iterative optimization index.
Step 4.4: repeating the step 4.3, comparing and finding out the optimal fitness value, and recording the key parameters optimized by the iteration until the iteration of the ChOA algorithm is finished;
step 4.5: and (3) taking the optimal key parameters in the step (4.4) as the final use parameters of the model, and sending the final use parameters into a test set for testing to obtain the diagnosis result of the chemical process fault diagnosis model of the final STGCN.
And the alarm module is used for sending out an alarm and displaying the fault type when the fault is diagnosed, so that a factory and workers are reminded of timely processing.
The present invention is not limited to the above-described embodiments, and any equivalent or modified embodiments according to the technical solution of the present invention and the inventive concept thereof are included in the scope of the present invention within the knowledge of those skilled in the art.

Claims (4)

1.一种化工过程故障诊断报警方法，其特征在于，包括如下步骤：1. A method for diagnosing and alarming faults in a chemical process, characterized by comprising the following steps: 步骤1：获取田纳西伊斯曼TE过程中的操作变量和过程变量作为历史数据；根据故障种类的不同，添加故障种类标签，构建不同故障的原始数据集；Step 1: Obtain the operational and process variables in the Tennessee Eastman TE process as historical data; add fault type labels according to different fault types to construct the original datasets for different faults; 步骤2：对采集到的原始数据集进行分解和降维处理；采集到的不同故障种类数据集作为多元经验模式分解MEMD的输入，MEMD分解方法将多元输入数据投影到一个更高的维度，协同考虑多元输入后再将信号分解为不同的分量；分解后的分量进一步采用核主成分分析KPCA，提取分量信号中的重要成分，降低数据维度；Step 2: Decompose and reduce the dimensionality of the collected raw dataset; the collected datasets of different fault types are used as inputs for multivariate empirical pattern decomposition MEMD. The MEMD decomposition method projects the multivariate input data to a higher dimension, and after considering the multivariate inputs together, the signal is decomposed into different components; the decomposed components are further subjected to kernel principal component analysis (KPCA) to extract important components in the component signals and reduce the data dimensionality. 步骤3：建立基于STGCN的化工过程故障诊断模型，并利用改进的黑猩猩优化算法ChOA优化基于STGCN的化工过程故障诊断模型的关键参数；利用采集到的田纳西伊斯曼原始数据集与改进ChOA算法对建立的基于STGCN的化工过程故障诊断模型进行训练，求出STGCN网络的最优关键参数、最小化损失函数误差；所述改进的黑猩猩优化算法ChOA使用混沌初始化种群并添加双重自适应权重；改进的黑猩猩优化算法步骤为：Step 3: Establish a chemical process fault diagnosis model based on STGCN, and optimize the key parameters of the STGCN-based chemical process fault diagnosis model using the improved chimpanzee optimization algorithm ChOA; train the established STGCN-based chemical process fault diagnosis model using the collected Tennessee Eastman original dataset and the improved ChOA algorithm to find the optimal key parameters of the STGCN network and minimize the loss function error; the improved chimpanzee optimization algorithm ChOA uses chaotic initialization of the population and adds dual adaptive weights; the steps of the improved chimpanzee optimization algorithm are as follows: 步骤3.1：设置ChOA算法的目标函数为诊断准确率并初始化相关参数，包括：初始位置、种群规模、迭代次数；Step 3.1: Set the objective function of the ChOA algorithm to diagnostic accuracy and initialize relevant parameters, including: initial location, population size, and number of iterations; 步骤3.2：在原始的黑猩猩算法中，初始化是根据输入参数的维度和数量随机生成的，故在黑猩猩种群初始化过程中引入Logistic混沌初始化，使4个黑猩猩种群进行更广泛的初步搜索，提高了算法的搜索效率其，表达式为：Step 3.2: In the original chimpanzee algorithm, the initialization is randomly generated based on the dimension and number of input parameters. Therefore, Logistic chaotic initialization is introduced during the chimpanzee population initialization process, allowing the four chimpanzee populations to perform a broader preliminary search, thus improving the algorithm's search efficiency. Its expression is: 其中，k(n+1)是更新后个体位置，λ是控制变量；Where k(n+1) is the updated individual position, and λ is the control variable; 步骤3.3：根据黑猩猩的劳动划分，种群分为4类：攻击黑猩猩、拦截黑猩猩、追逐黑猩猩和驱赶黑猩猩；黑猩猩在群体中有独立思考的能力，在某些情况下会出现混乱的狩猎行为；Step 3.3: Based on the division of labor among chimpanzees, the population is divided into 4 categories: those who attack chimpanzees, those who intercept chimpanzees, those who chase chimpanzees, and those who drive away chimpanzees; chimpanzees have the ability to think independently within the group, but in some cases, they may exhibit chaotic hunting behavior. 步骤3.4：在黑猩猩捕猎的过程中，黑猩猩需要根据自己和猎物之间的距离来判断下一步行动的方向和距离：Step 3.4: During the hunting process, chimpanzees need to determine the direction and distance of their next move based on the distance between themselves and their prey. d＝|cxprey(t)-mxchimp(t)|d＝|cx prey (t)-mx chimp (t)| xchimp(t+1)＝xprey(t)-adx chimp (t+1) = x prey (t) - ad 其中，d是猎物和黑猩猩之间的距离；t是当前的迭代次数是猎物和黑猩猩之间的距离；xprey(t)是猎物当前的位置；xchimp(t)是黑猩猩的当前位置；a，m，c为系数向量；Where d is the distance between the prey and the chimpanzee; t is the current iteration number; xprey (t) is the current position of the prey; xchimp (t) is the current position of the chimpanzee; and a, m, and c are coefficient vectors. 步骤3.5：每只黑猩猩都根据自己的劳动分工来独立地决定捕猎的过程，即每只黑猩猩和猎物之间的位置向量；4种黑猩猩与猎物确定它们的位置向量后，每只黑猩猩根据最佳黑猩猩位置更新其位置，并根据最佳黑猩猩个体位置估计猎物的位置，其表达式如下：Step 3.5: Each chimpanzee independently determines the hunting process based on its own division of labor, i.e., the position vector between each chimpanzee and its prey; after the four types of chimpanzees and their prey determine their position vectors, each chimpanzee updates its position based on the optimal chimpanzee position, and estimates the position of the prey based on the optimal individual chimpanzee position, as expressed below: 其中，dAttacker,dBarrier,dChaser,dDriver分别表示在攻击黑猩猩、拦截黑猩猩、追逐黑猩猩和驱赶黑猩猩阶段与猎物的距离；xAttacker,xBarrier,xChaser,xDriver是攻击黑猩猩、拦截黑猩猩、追逐黑猩猩、驱动黑猩猩相对于猎物的位置向量，a1～a4，c1～c4，m1～m4分别是四种黑猩猩的向量系数；Where dAttacker , dBarrier , dChaser , and dDriver represent the distances to the prey during the stages of attacking, intercepting, chasing, and driving the chimpanzee, respectively; xAttacker , xBarrier , xChaser , and xDriver are the position vectors of the chimpanzee relative to the prey during the stages of attacking, intercepting, chasing, and driving the chimpanzee, respectively; and a1 ～ a4 , c1 ～ c4 , and m1 ～ m4 are the vector coefficients of the four types of chimpanzees. 步骤3.6：在优化过程中，选择黑猩猩的正常位置更新或通过混沌模型进行的位置更新，选择的概率为50％，其公式为：Step 3.6: During the optimization process, the chimpanzee's normal position update or the position update through a chaotic model is selected with a 50% probability. The formula is as follows: 其中，μ为[0,1]范围内的一个随机数；Where μ is a random number in the range [0,1]; 步骤3.7：判断是否达到最大迭代次数，若未达到则ChOA算法进入步骤3.2；否则，结束运行并输出最终结果；Step 3.7: Determine if the maximum number of iterations has been reached. If not, the ChOA algorithm proceeds to step 3.2; otherwise, terminate the process and output the final result. 步骤4：利用训练优化后的化工过程故障诊断模型对田纳西伊斯曼过程数据进行诊断，得到诊断结果并计算其准确率；Step 4: Use the trained and optimized chemical process fault diagnosis model to diagnose the Tennessee Eastman process data, obtain the diagnosis results, and calculate its accuracy. 步骤4.1：根据步骤2中数据结构构建邻接矩阵A,并对邻接矩阵A节点参数进行归一化，组成模型需要的图数据：Step 4.1: Construct adjacency matrix A based on the data structure in Step 2, and normalize the node parameters of adjacency matrix A to form the graph data required for the model: 其中，表示节点xi的均值，σi表示该节点的标准差；in, Let σ<sub>i</sub> represent the mean of node x<sub>i</sub> , and σ <sub>i</sub> represent the standard deviation of that node. 步骤4.2：根据化工过程数据，按照时间关系构建多节点的图Gi：Step 4.2: Based on the chemical process data, construct a multi-node graph Gi according to the time relationship: Gi＝(xi,E,A)G <sub>i</sub> = (x <sub>i</sub> , E, A) 其中，E表示节点之间的边集合；Where E represents the set of edges between nodes; 步骤4.3：通过门控线性单元GLU和一个一维卷积网络构成时间卷积模块，用于捕捉时间特征；门控线性单元可以选择传递需要的信息进入下一个节点，其表达公式如下：Step 4.3: A temporal convolution module is constructed using a gated linear unit (GLU) and a one-dimensional convolutional network to capture temporal features. The gated linear unit can selectively pass the required information to the next node, and its expression formula is as follows: 其中，M和N是不同的卷积核，c1和c2是不同的偏置参数；Where M and N are different convolution kernels, and c1 and c2 are different bias parameters; 步骤4.4：通过图卷积模块在空间域上进行高阶体征提取，充分利用化工过程故障数据的关联性和全局性，在卷积是采用切比雪夫多项式近似，其卷积公式如下：Step 4.4: High-order features are extracted in the spatial domain using the graph convolution module, fully utilizing the correlation and globality of chemical process fault data. Chebyshev polynomial approximation is used in the convolution, and the convolution formula is as follows: 其中，Z是图卷积核大小，TZ是拉普拉斯矩阵的多项式展开近似，Θz是多项式系数，最终图卷积可以表示为：Where Z is the graph convolution kernel size, T<sub> Z </sub> is the polynomial expansion approximation of the Laplacian matrix, and Θ <sub>z </sub> are the polynomial coefficients. The final graph convolution can be expressed as: 其中，Di和D0是输入和输出的特征图的大小，D代表维度特征，R为实数集合；Where Di and D0 are the sizes of the input and output feature maps, D represents the dimensionality of the feature map, and R is the set of real numbers; 步骤4.5：最后将得到的数据进行反归一化处理，根据测试结果输出故障类型；Step 4.5: Finally, perform inverse normalization on the obtained data and output the fault type based on the test results; 步骤5：根据步骤4中诊断的结果，判断是否发出警报，从而提醒工厂与工人及时处理，并在报警时展示故障类型。Step 5: Based on the diagnostic results in Step 4, determine whether to issue an alarm to remind the factory and workers to handle the situation in a timely manner, and display the fault type when an alarm is triggered. 2.根据权利要求1所述的一种化工过程故障诊断报警方法，其特征在于，所述步骤2中利用MEMD分解模型把历史数据分解为多个不同频率的分解步骤如下：2. The chemical process fault diagnosis and alarm method according to claim 1, characterized in that, in step 2, the historical data is decomposed into multiple decomposition steps of different frequencies using a MEMD decomposition model as follows: 步骤2.1：对于原始的多元化工过程数据s(t)＝[s1(t)，s2(t)…，sn(t)]T，投影向量为其中是(n-1)的单位球上第k个沿着角度的投影向量，k＝1,2...，K，K是投影向量的总数量；Step 2.1: For the original multi-process data s(t)＝[ s1 (t), s2 (t), …, sn (t)] T , the projection vector is in It is the k-th projection vector along the angle on the (n-1)-th unit sphere, k = 1, 2, ..., K, where K is the total number of projection vectors; 步骤2.2：在得到方向矢量集的投影向量U之后，计算s(t)沿着投影向量的映射值，记为其计算公式为：Step 2.2: After obtaining the projection vector U of the direction vector set, calculate s(t) along the projection vector. The mapping value is denoted as The calculation formula is as follows: 步骤2.3：提取出映射信号在取局部极值时的瞬时时间 Step 2.3: Extract the mapped signal The instantaneous time when taking a local extremum 步骤2.4：对使用多元样条插值获法对极值点进行插值操作，取得多元投影包络线 是矢量方向的信号sl(t)的包络，l＝1，2，...，n；Step 2.4: For Multivariate spline interpolation is used to interpolate extreme points to obtain the multivariate projected envelope. yes The envelope of the vector direction signal s l (t), l = 1, 2, ..., n; 步骤2.5：计算多元信号的局部均值：Step 2.5: Calculate the local mean of the multivariate signal: 步骤2.6：计算多元输入序列与局部均值的差d(t)：Step 2.6: Calculate the difference d(t) between the multivariate input sequence and the local mean: d(t)＝s(t)-m(t)d(t) = s(t) - m(t) 步骤2.7：若d(t)符合多变量IMF的要求，若满足条件记为di(t)，则di(t)为第i次分解后的分量，并将原始信号中的该分量成分去掉，得到新的原始信号ki(t)，i＝1，2，...，m：Step 2.7: If d(t) meets the requirements of the multivariate IMF, and if the condition is satisfied, denoted as d<sub>i</sub> (t), then d <sub>i </sub>(t) is the component after the i-th decomposition. This component is then removed from the original signal to obtain the new original signal ki (t), i = 1, 2, ..., m. ki(t)＝s(t)-d(t)k <sub>i </sub>(t) = s(t) - d(t) 步骤2.8：重复执行步骤2.2-步骤2.7，直至d(t)不满足IMF的要求，并将此时d(t)记为残差r(t)，得到最终多元分解信号S(t)＝[k1(t)，k2(t)，...，km(t)，r(t)]。Step 2.8: Repeat steps 2.2-2.7 until d(t) no longer meets the requirements of IMF, and record d(t) at this time as residual r(t) to obtain the final multivariate decomposition signal S(t) = [ k1 (t), k2 (t), ..., km (t), r(t)]. 3.根据权利要求1所述的一种化工过程故障诊断报警方法，其特征在于，所述STGCN的化工过程故障诊断模型的关键参数包括：STGCN模型的学习率、隐藏层节点数、训练迭代次数，在利用改进的黑猩猩优化算法ChOA优化基于STGCN的化工过程故障诊断模型的关键参数时，种群即为需要优化的关键参数；3. The chemical process fault diagnosis and alarm method according to claim 1, characterized in that the key parameters of the STGCN chemical process fault diagnosis model include: the learning rate of the STGCN model, the number of hidden layer nodes, and the number of training iterations. When optimizing the key parameters of the STGCN-based chemical process fault diagnosis model using the improved chimpanzee optimization algorithm ChOA, the population is the key parameter to be optimized. 步骤4.1：将步骤2中数据划分为训练集和测试集，以供模型训练和迭代寻优STGCN模型的关键参数使用；Step 4.1: Divide the data from Step 2 into training and testing sets for use in model training and iterative optimization of the key parameters of the STGCN model; 步骤4.2：将STGCN模型的关键参数送入ChOA：其维度为3，维度根据关键参数种类确定，其初始值由步骤3.2中混沌初始化得出；Step 4.2: Input the key parameters of the STGCN model into the ChOA: its dimension is 3, the dimension is determined according to the type of key parameter, and its initial value is obtained from the chaos initialization in step 3.2; 步骤4.3：STGCN模型根据训练集和步骤4.2中关键参数进行模型训练，记录诊断训练结果及准确率并将其传回ChOA算法中，其准确率计算公式如下；Step 4.3: The STGCN model is trained based on the training set and the key parameters in Step 4.2. The diagnostic training results and accuracy are recorded and transmitted back to the ChOA algorithm. The accuracy calculation formula is as follows; 其中，准确率Accuracy为正确分类的故障类型与总故障样本ALL的比值，正确分类指被检索到正样本TP和未被检索到正样本TN，准确率送入ChOA记为适应度值，作为为算法迭代寻优指标；Accuracy is the ratio of correctly classified fault types to the total number of fault samples (ALL). Correct classification refers to the positive samples TP that were retrieved and the positive samples TN that were not retrieved. The accuracy is fed into ChOA as the fitness value, which is used as an indicator for algorithm iteration and optimization. 步骤4.4：重复步骤4.3，比较并找出最优适应度值，记录该次迭代优化出的关键参数，直到ChOA算法迭代结束；Step 4.4: Repeat step 4.3, compare and find the optimal fitness value, and record the key parameters optimized in this iteration until the ChOA algorithm iteration ends; 步骤4.5：将步骤4.4中最优关键参数作为模型的最终使用参数，送入测试集进行测试，得到最终STGCN的化工过程故障诊断模型的诊断结果。Step 4.5: Use the optimal key parameters from Step 4.4 as the final parameters for the model, and send them to the test set for testing to obtain the final diagnostic results of the STGCN chemical process fault diagnosis model. 4.一种基于权利要求1至3任一所述的化工过程故障诊断报警方法的系统，其特征在于，所述系统包括数据采集模块、数据预处理模块、模型训练模块、故障诊断模块和报警模块；4. A system based on the chemical process fault diagnosis and alarm method according to any one of claims 1 to 3, characterized in that the system includes a data acquisition module, a data preprocessing module, a model training module, a fault diagnosis module, and an alarm module; 数据采集模块，获取田纳西伊斯曼TE过程中的操作变量和过程变量作为历史数据；根据故障种类的不同，添加故障种类标签，构建不同故障的原始数据集；The data acquisition module obtains the operational and process variables in the Tennessee Eastman TE process as historical data; according to the different types of faults, it adds fault type labels to construct the original datasets for different faults; 数据预处理模块，用于对采集到的原始数据集进行分解和降维处理；采集到的不同故障种类数据集作为多元经验模式分解MEMD的输入，MEMD分解方法将多元输入数据投影到一个更高的维度，协同考虑多元输入后再将信号分解为不同的分量；分解后的分量进一步采用核主成分分析KPCA，提取分量信号中的重要成分，降低数据维度；The data preprocessing module is used to decompose and reduce the dimensionality of the collected raw dataset. The collected datasets of different fault types are used as inputs for multivariate empirical pattern decomposition (MEMD). The MEMD decomposition method projects the multivariate input data to a higher dimension and decomposes the signal into different components after considering the multivariate inputs. The decomposed components are further subjected to kernel principal component analysis (KPCA) to extract important components from the component signals and reduce the data dimensionality. 模型训练模块，用于建模基于STGCN的化工过程故障诊断模型，并通过采集到的田纳西伊斯曼原始数据集与改进ChOA算法对建立的基于STGCN的化工过程故障诊断模型进行训练，求出网络的最优关键参数、最小化损失函数误差；The model training module is used to model the STGCN-based chemical process fault diagnosis model. The model is trained using the collected Tennessee Eastman original dataset and the improved ChOA algorithm to find the optimal key parameters of the network and minimize the error of the loss function. 故障诊断模块，利用训练优化后的化工过程故障诊断模型对田纳西伊斯曼过程数据进行诊断，得到诊断结果，并诊断出故障时判定故障类型；The fault diagnosis module uses the trained and optimized chemical process fault diagnosis model to diagnose the Tennessee Eastman process data, obtain the diagnosis results, and determine the fault type when a fault is diagnosed. 报警模块，用于在诊断出故障时，发出警报并显示故障类型，从而提醒工厂与工人及时处理。The alarm module is used to issue an alarm and display the fault type when a fault is diagnosed, thereby reminding the factory and workers to deal with it in a timely manner.
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