Intermittent process fault detection method based on improved adversarial auto-coding network

Abstract

The invention discloses an intermittent process fault detection method based on an improved adversarial self-encoding network, and belongs to the technical field of intermittent industrial process monitoring, and the method comprises the steps: collecting multiple batches of process data in a normal operation state of an intermittent process, carrying out the preprocessing of the collected data, and taking the preprocessed data as a training sample; adding a prediction loss item in a loss function of the adversarial self-coding network, and constructing a time window enhanced adversarial self-coding network; wherein the prediction loss item is obtained based on a pre-trained time sequence predictor; training the time window enhanced adversarial self-encoding network by using the training sample; and utilizing the trained time window to enhance the adversarial self-coding network to realize intermittent process fault detection. On the basis of a traditional adversarial self-encoding network, the method further considers the front-back dependency relationship of the time sequence, has better adaptability when processing different batches of data in the intermittent process, and improves the accuracy of fault detection.

Description

Technical Field

[0001] The invention relates to the technical field of intermittent industrial process monitoring, and in particular to an intermittent process fault detection method based on an improved adversarial autoencoder network. Background Art

[0002] Batch process is a production method widely used in modern chemical industry, biotechnology, food processing and pharmaceutical industries. Unlike continuous process, batch process is a periodic operation mode, usually with "batch" as the basic production unit. The operation of each batch includes the addition of raw materials, the adjustment of reaction conditions, the extraction of products, etc., and these steps are completed at a specific time point. Such a production method can flexibly adapt to the production needs of various products, and is particularly suitable for production environments with small output, diverse product types or special requirements. In the chemical industry, batch process is often used in synthetic reactions, catalytic processes and the production of fine chemicals. In these processes, the addition of reactants, temperature and pressure regulation are crucial to the quality of the final product. In the field of biotechnology, such as fermentation, enzyme catalysis and cell culture, batch processes are also widely used because these processes require precise control of the growth conditions of microorganisms to improve the yield and quality of products. In the food processing industry, especially in the fields of winemaking and dairy production, batch process is a key way to ensure the stability of product flavor and quality. However, a notable feature of batch process is its discontinuity and periodic changes. In each batch of production, changes in raw materials, fluctuations in the environment and differences in operating conditions will lead to changes in production results. The unpredictability and complexity of this change make monitoring and control a huge challenge. Especially in modern automated production, how to detect and diagnose faults in intermittent processes in real time and effectively has become a key issue in improving production efficiency and ensuring product quality.

[0003] For fault detection in intermittent processes, it is first necessary to collect process data such as temperature, pressure and flow through sensors and preprocess the data, including denoising, filling missing values ​​and standardization. Next, feature extraction methods are used to extract key features from the data, which provide input for model training and help identify the difference between normal and abnormal operations. The trained fault detection model can monitor new data in real time, detect abnormal signals in time and perform fault diagnosis. As the complexity and dynamics of data increase, traditional methods are currently unable to meet the needs, especially when facing large-scale and complex data. To address these challenges, deep learning methods, especially neural network models, are currently the key technology in fault detection in intermittent processes. They can automatically learn complex nonlinear features from large amounts of data and effectively capture time dependencies and hidden patterns in data, thereby significantly improving the accuracy and robustness of fault detection. Common deep learning models currently include deep belief networks (DBNs), autoencoders (AEs), convolutional neural networks (CNNs) and recurrent neural networks (RNNs). However, although these deep learning methods can effectively extract features from complex data, they still face some challenges in fault detection in intermittent processes. First, the intermittent process has obvious dynamic characteristics, and the operation of each batch will introduce certain changes, which puts forward adaptability requirements for traditional deep learning models. Traditional models often find it difficult to adapt to these fluctuations between batches, especially when facing new batches that have not been seen, and often cannot make effective predictions. In addition, as the process continues to evolve, the distribution of data may change significantly, and traditional models lack sufficient generalization capabilities. In this context, researchers have proposed some improvement methods, such as adversarial autoencoder networks (AAEs), which aim to enhance the model's feature learning and generation capabilities through adversarial training, thereby improving the robustness of the model. Nevertheless, in response to the challenges of dynamic data feature extraction, especially in data environments such as intermittent processes with obvious time dependencies, existing unsupervised adversarial autoencoder network methods still face certain difficulties. These models usually lack built-in mechanisms to deal with time dependencies in time series and cannot efficiently respond to changes in time series data. Therefore, in practical applications, further optimization and improvement are still needed. Summary of the invention

[0004] The present invention provides an intermittent process fault detection method based on an improved adversarial autoencoder network to solve the technical problem that the existing intermittent process fault detection method cannot efficiently respond to changes in time series data, resulting in unsatisfactory detection effect.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In one aspect, the present invention provides an intermittent process fault detection method based on an improved adversarial autoencoder network, comprising:

[0007] Under normal operation of the intermittent process, multiple batches of process data are collected, and the collected process data are preprocessed, and the preprocessed process data are used as training samples;

[0008] Adding a prediction loss term to the loss function of the adversarial autoencoder network to construct a time window enhanced adversarial autoencoder network; wherein the prediction loss term is obtained based on a pre-trained time series predictor;

[0009] Using training samples to train the time window enhanced adversarial autoencoder network;

[0010] Intermittent process fault detection is achieved using the trained time window enhanced adversarial autoencoder network.

[0011] Furthermore, in the intermittent process, during normal operation, multiple batches of process data are collected, and the collected process data are preprocessed, and the preprocessed process data are used as training samples, including:

[0012] Under normal operation of the intermittent process, multiple batches of process data are collected and the collected data are expressed as a three-dimensional matrix of I×J×K, where I represents the data batch, J represents the number of observed variables, and K represents the number of sampling times;

[0013] The collected process data in the form of a three-dimensional matrix is ​​expanded in the batch direction to obtain a two-dimensional data matrix, and the two-dimensional data matrix is ​​standardized in the batch direction to eliminate the influence of different dimensions.

[0014] The standardized two-dimensional data matrix is ​​rearranged to obtain the two-dimensional time series matrix data expanded according to the observed variables; wherein the data in each column of the two-dimensional time series matrix data reflects the change of a certain variable over time; the two-dimensional time series matrix data is used as a training sample.

[0015] Furthermore, the training process of the time series predictor includes:

[0016] Use sliding time windows to divide the data in the training sample, convert the continuous time series data into multiple time segment data to obtain windowed data; each time segment data consists of observation data in the corresponding time period;

[0017] Construct a time series predictor, use the windowed data as the input of the time series predictor, calculate the mean square error between the output data of the time series predictor and the actual data, and perform the back propagation algorithm to determine the final network parameters of the time series predictor, and obtain a trained time series predictor.

[0018] Furthermore, the predicted loss item is obtained as follows:

[0019] The reconstructed output of the adversarial autoencoder network is input into the pre-trained time series predictor, the mean square error between the output of the time series predictor and the actual data is calculated, and the calculation result is used as the prediction loss term.

[0020] Furthermore, the loss function of the time window enhanced adversarial autoencoder network is calculated as follows:

[0021] Calculate the mean square error between the reconstructed output of the adversarial autoencoder network and the original input of the adversarial autoencoder network, and use the mean square error between the reconstructed output and the original input as the reconstruction loss term;

[0022] The sum of the prediction loss term and the reconstruction loss term is used as the loss function of the time window enhanced adversarial autoencoder network.

[0023] Furthermore, the method of using the trained time window enhanced adversarial autoencoder network to implement intermittent process fault detection includes:

[0024] Based on the trained time window enhanced adversarial autoencoder network, the normal working condition data in the feature space and reconstruction space are counted, the corresponding statistics are calculated and their upper limits are determined, and the control threshold is set;

[0025] Obtain process data during the current intermittent process operation, preprocess it, input the preprocessed current process data into the trained time window enhanced adversarial autoencoder network, and obtain the statistics of the current process data;

[0026] Compare the statistics of current process data with control thresholds to complete fault detection.

[0027] Furthermore, the method of enhancing the adversarial autoencoder network based on the trained time window, counting the normal operating data in the feature space and the reconstruction space, calculating the corresponding statistics and determining the upper limit thereof, and setting the control threshold, includes:

[0028] Input the training sample to the time window enhanced adversarial autoencoder network and calculate the T corresponding to each normal sample 2 After calculating the statistics and SPE statistics, the fault detection control limit T is determined using the kernel density estimation method. α 2 and SPE α .

[0029] Furthermore, the comparison of the statistics of the current process data with the control threshold to complete the fault detection includes:

[0030] If any statistic of the current process data exceeds the corresponding fault detection control limit, it is determined that a fault has occurred.

[0031] On the other hand, the present invention further provides an electronic device, comprising a processor and a memory; wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the above method.

[0032] In yet another aspect, the present invention further provides a computer-readable storage medium, wherein at least one instruction is stored in the storage medium, and the instruction is loaded and executed by a processor to implement the above method.

[0033] The beneficial effects brought about by the technical solution provided by the present invention include at least:

[0034] The method of the present invention fully considers the complexity and dynamic characteristics of intermittent industrial process data, and effectively extracts the feature distribution in the process data through the design of the time window enhanced adversarial autoencoder. At the same time, without substantially increasing the complexity of the model, the pre-trained predictor and adversarial mechanism are used to realize the extraction of dynamic features in the fault detection process. According to the internal time dependency of the extracted data, the method can cope with the changes in process data at different stages, thereby realizing high-precision detection of process faults. Compared with existing fault detection methods, the method not only improves the accuracy of fault detection, but also enhances the sensitivity to different fault modes, providing a more effective and feasible solution for intermittent fault detection in complex industrial processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] 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.

[0036] Figure 1 It is a schematic diagram of the execution flow of the intermittent process fault detection method based on the improved adversarial autoencoder network provided by an embodiment of the present invention;

[0037] Figure 2 is the T of the penicillin fermentation process fault data of batch number 4 applied by the method of the present invention 2 and SPE fault detection diagram;

[0038] Figure 3 It is a system block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0040] First of all, it should be noted that 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 "exemplary" 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 "exemplarily" is intended to present the concept in a concrete 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.

[0041] First embodiment

[0042] This embodiment provides a batch process fault detection method based on an improved adversarial autoencoder network, which aims to overcome the shortcomings of existing adversarial autoencoder networks in batch process monitoring applications. It proposes a time window enhanced adversarial autoencoder network, which extracts predictable dynamic features in the reaction process behavior based on the dynamic characteristics of the batch process and the rapid changes between batches, thereby improving the accuracy of fault detection. The method can be implemented by an electronic device, which can be a terminal or a server. The execution process of the method is as follows: Figure 1 As shown in the figure, based on the feature extraction method of the adversarial autoencoder network, it introduces the ideas related to the predictability feature extraction method in multivariate statistics, and introduces the predictive loss into the training process of the adversarial autoencoder network.

[0043] Specifically, the intermittent process fault detection method of this embodiment includes the following steps:

[0044] S1, under the normal operation state of the intermittent process, collect multiple batches of process data, pre-process the collected process data, and use the pre-processed process data as training samples;

[0045] Specifically, the implementation process of the above S1 is as follows:

[0046] S11, data collection: Under normal operation of the intermittent process, multiple batches of process data are collected as training data, and the collected data are represented as a three-dimensional matrix of I×J×K, where I represents the data batch, J represents the number of observed variables, and K represents the number of sampling times.

[0047] S12, data preprocessing, specifically including:

[0048] S121, standardization processing: expand the three-dimensional data samples collected from the intermittent process in the batch direction to obtain a two-dimensional data matrix X b =[x1,x2,...,x i ] T ∈R I×KJ , where x i =[x i,1 ,xi,2 ,...,x i,KJ ] T , i=1,2,...,I ​​is a column vector. It is normalized in the batch direction as follows:

[0049]

[0050] The above standardization process can eliminate the influence of different dimensions, making the data more comparable. and σ i are batch data matrices X b The column mean and standard deviation of the i-th column. After standardization, the mean of each column is 0 and the variance is 1.

[0051] S122, generate training data: rearrange the standardized two-dimensional matrix to obtain a two-dimensional time series matrix X expanded by the observed variables t =[x1,x2,...,x J ] T ∈R IK×J , where x j ,j=1,2,…,J is X t The column vector of each column reflects the change of a certain variable over time. Through the arrangement of time series, each column of data can reflect the change of the process, providing a basis for subsequent modeling.

[0052] S2, adding a prediction loss term to the loss function of the adversarial autoencoder network to construct a time window enhanced adversarial autoencoder network; wherein the prediction loss term is obtained based on a pre-trained time series predictor;

[0053] Specifically, the implementation process of the above S2 is as follows:

[0054] S21, establish a time series vector prediction network: use a sliding time window to divide the data, use the divided three-dimensional time window data to train the time series predictor, extract its time dependency, and predict future process changes. Specifically, the process of establishing the time series vector prediction network is as follows:

[0055] S211, windowed training data input: a batch of continuous time series data is divided into time periods (windows). Specifically, for each batch of input data, a fixed time window length s is selected, that is, each window contains data from s consecutive moments before a certain moment t. In this way, we can convert continuous time data into multiple time segments. For example, for batch data X t =[x1,x2,...,x J ] T ∈RIK×J , X i =[x i-s+1 ,...,x i-1 ,x i ] T That is, it represents the time window composed of the observation data of time t = i and the previous s consecutive time points. This batch of data contains a total of (IK-s) time windows, so the new data form X can be obtained. w =[X s ,X s+1 ,...,X IK-1 ] T ∈R (IK-s)×s×J , where X j ∈R s×J ,j=1,2,...,IK-s is the time window at different moments.

[0056] S212, training time series predictor: And initialize the weight parameter β i , bias r, the order is consistent with the length of the time window, where the column vector x k is the observation data collected at time t = k, x k-i With x k have the same dimensions, is the model prediction result at time t=k; therefore, the windowed data X w As prediction input, the network will output the prediction result matrix The prediction model can be established based on a regression model or a deep neural network such as RNN or LSTM. s =[x s+1 ,x s+2 ,...,x n ] T The mean square error is calculated and the back propagation algorithm is performed to determine the final network parameters and obtain the time series prediction model.

[0057] S22, build an adversarial autoencoder model, including encoder G e , decoder G d , the discriminator G y Structure, and initialize the network parameters θ e ,θ d ,θ y , and the data prior distribution. The training samples are represented by row vector x r =[x1,x2,...,x J ], we can get the hidden layer z r =G e (x;θ e ), Reconstruct output Discrimination result y fake , in, It comes from sampling from the prior distribution.

[0058] S3, using training samples to train the time window enhanced adversarial autoencoder network;

[0059] Specifically, the above S3 is based on the time series predictor obtained by pre-training in the previous step, and trains the adversarial autoencoder model to extract the features of the time series. The composite loss function is used to ensure that the model can accurately capture the change pattern in the time series while extracting key features, and the introduction of the adversarial mechanism enhances the robustness of the model, enabling it to cope with complex process dynamics; its specific implementation process is as follows:

[0060] S31, input the training data into the batch adversarial autoencoder network, for the input data X t ∈R IK×J , we can get the hidden layer output Z t ∈R IK×h and reconstruct the output Among them, h is the hidden layer feature dimension. Generally speaking, the original input X t With reconstructed output The mean square error is the reconstruction loss term of the adversarial autoencoder network. In this method, the reconstructed output Input to the time series predictor obtained in the previous step and calculate the predicted output With the actual data x t+1 The mean square error is calculated and the result is used as the prediction loss term of the adversarial autoencoder network. The reconstructed output is then compared with the original input. t And calculate the mean square error to obtain the reconstruction loss term of the adversarial autoencoder network; based on the prediction loss term and the reconstruction loss term, a composite loss function for training the adversarial autoencoder network is established to complete the adversarial autoencoder reconstruction training phase encoder G e , decoder G d The model parameters are updated and the model parameters after training are saved. Specifically, in this embodiment, the sum of the reconstruction loss term and the prediction loss term is taken as the total loss of the reconstruction training process.

[0061] S32, execute the adversarial training strategy. In each round of training, the encoder G is first updated by back-propagation of reconstruction loss and prediction loss. e , decoder G d The network parameters of the encoder G are updated by calculating the difference between the feature layer and the prior distribution through the discriminator to obtain the adversarial loss and back propagating. e , and the discriminator G yAfter that, the two stages are performed alternately, and the model parameters after the final training are saved.

[0062] S4, using the trained time window enhanced adversarial autoencoder network to achieve intermittent process fault detection.

[0063] Specifically, the above S4 is based on the time window enhanced adversarial autoencoder model obtained through training, which counts the normal working condition data in the feature space and reconstruction space, calculates the corresponding statistics and determines its upper limit, and sets a suitable control threshold. In this way, when the new input data exceeds this threshold, it indicates the occurrence of a fault, and the model can give a warning in time. Specifically, its implementation process is as follows:

[0064] S41, set fault detection control limits: establish Hotelling statistics T 2 The mean square error statistic SPE monitors the feature space and reconstruction space data. For example, at a certain moment, the training sample data is x r , the feature data and reconstructed data obtained after inputting the model are z r , The statistical calculation method can be expressed as:

[0065] T 2 =z r z r T

[0066]

[0067] Input training data to the model to calculate T corresponding to each normal sample 2 After calculating the statistics and SPE statistics, the kernel density estimation method can be used to determine the control limit α and obtain the fault detection control limit T α 2 and SPE α .

[0068] S42, online monitoring: collect online data of intermittent process and standardize it according to S1, then input it into the model to obtain the corresponding statistics and SPE test If SPE test >SPE α or This indicates a malfunction.

[0069] In summary, in order to solve the problem of fault detection in intermittent industrial processes, this embodiment provides an intermittent process fault detection method. First, this method collects intermittent process data under normal working conditions as training samples, and performs batch normalization on the observed variables; secondly, a sliding time window is used on the training samples to obtain three-dimensional time series data samples, train the time series predictor, extract the time series dependency, and achieve the effect of being able to predict the next moment through historical data; then the time window enhanced adversarial autoencoder network is trained, and in the reconstruction training process, a prediction loss term based on the time series predictor is introduced, and it is used together with the reconstruction loss as the total loss of the training process, and then in the adversarial training process, the model takes into account the distribution characteristics and time dependency of the data; finally, the statistics corresponding to the normal working condition data are calculated, and the fault detection control limit is determined by the kernel density estimation method, and the model is used for online monitoring. Based on this, the intermittent process fault detection method provided by this embodiment further considers the front-back dependency of the time series on the basis of the traditional adversarial autoencoder network, has better adaptability when processing different batches of data of the intermittent process, and improves the accuracy of fault detection.

[0070] Second embodiment

[0071] This embodiment uses the application of the method of the present invention to fault detection in a penicillin fermentation process as an example to illustrate the implementation process and implementation effect of the intermittent process fault detection method of the present invention.

[0072] The industrial process of penicillin fermentation with batch feeding is a typical intermittent industrial production process, which is of great significance in fault detection. A monitoring and control group of Illinois Institute of Technology in the United States developed a modular fermentation batch feeding simulator Pensim to simulate this process. During the process, penicillin colonies carry out metabolic activities to produce antibiotics under suitable fermentation conditions such as temperature, pH, and concentration. Because its model is complex and changeable, and usually has strong nonlinear characteristics, the difficulty of fault detection is further increased. The present invention takes the penicillin fermentation process as the simulation research object, and uses the Pensim2.0 standard simulation platform of the penicillin fermentation process to generate intermittent process data. Next, the method steps of the present invention will be further explained in conjunction with the specific process:

[0073] 1) Data collection: The present invention sets the reaction time of each batch of the process to 400h, which includes a pre-culture phase of about 45h and a feeding phase of about 355h. The sampling interval is set to 0.5h, so each batch of data contains 800 sampling points, and each sampling point contains 16 monitored process variables, as shown in Table 1. Therefore, the present invention generates a total of 20 batches of normal operating data under different initial conditions and control parameters, which can constitute a three-dimensional data matrix X (20×800×16) as a training sample. In addition, in order to verify the effectiveness of the method proposed in the present invention, 6 batches of fault data were selected for fault detection effect verification. The specific fault setting method is shown in Table 2. The dimension of each batch of fault data is the same as that of the normal operating data.

[0074] Table 1 Monitoring process variable number and name

[0075]

[0076] Table 2 Fault data number and fault type description

[0077]

[0078]

[0079] 2) Data preprocessing;

[0080] 2-1) Expand the obtained three-dimensional data X(20×800×16) along the batch direction to obtain X b (20×12800), and standardize the matrix so that the column mean is 1 and the standard deviation is 0.

[0081] 2-2) For the standardized X b (20×12800) are rearranged to obtain the two-dimensional data matrix X expanded along the variable direction t (16000×16), as the final training data input of the model.

[0082] 3) Establish a time series prediction network;

[0083] 3-1) Windowed input data: Set the input data batch size to 80, then each batch of input data has a dimension of X (80×16) time series data, and set the window size to 5, which can be divided into 75 time windows X i =[X5,X6,...,X 79 ] T , and get the next moment data X corresponding to each window t =[x6,x7,...,x 80 ] T .

[0084] 3-2) Train the time series predictor, and transform X i As input to a time series forecaster to maximize the forecast output and the actual output X t The correlation is used as the training target, and the network parameters are iteratively optimized through the loss function back propagation and gradient descent method. When the number of iterations reaches the set upper limit or the loss function basically no longer decreases, the iteration process is stopped and the predictor model P is saved.

[0085] 4) Establishing a time window enhanced adversarial autoencoder network model

[0086] 4-1) Create encoder G e , decoder G d , Discriminator G y Network structure and initialize network parameters θ e ,θ d ,θ y . Batch input training data X t (80×16), and set the hidden layer feature dimension to 10, we can get the hidden layer data Z with continuous distribution t (80×10) and reconstructed output For X t =[x1,x2,...,x 80 ], And calculate the reconstruction loss and prediction loss of the data respectively:

[0087]

[0088] Using the composite loss function L1=L rec +βL pre As the total loss of encoding and decoding process, for encoder G e and decoder G d Update of model parameters, where β is a hyperparameter, which can be used as a regulating factor in the loss function to balance the contribution of different loss items to the total loss. The value needs to be adjusted according to the actual task.

[0089] 4-2) Set the prior distribution of each observed variable to the standard normal distribution N(0,1). e and decoder G d After the model parameters are updated, the hidden layer data is input into the discriminator, the difference between the current hidden layer data distribution and the prior distribution is compared, and the adversarial loss is calculated:

[0090]

[0091] In the formula, It comes from sampling the prior distribution. The loss L of the adversarial process is obtained adv After that, the encoder G is trained through the back propagation algorithm. e and the discriminator model G y The model parameters are updated.

[0092] 4-3) Encoder G e and decoder G d The autoencoder network and encoder G e and the discriminator G y The generated adversarial network constructed is trained alternately to achieve the final optimization goal, where λ is the same as β and is an adjustable hyperparameter:

[0093]

[0094] 5) Load normal operating data and calculate T 2 and SPE statistics, setting the confidence level to 95%, determine the fault detection control limit T α 2 and SPE α . Read in the test data set, use a similar preprocessing method as the training set data, input the test data into the trained model, and calculate the statistics corresponding to the test data and SPE test ,judge and SPE test Whether it exceeds the control limit T α 2 and SPE α If one of the items exceeds the control limit, it is determined that a fault has occurred. For the fault data of 6 batches, the fault detection results of the method proposed in the present invention are shown in Table 3.

[0095] Table 3 Test data fault detection results

[0096]

[0097] It can be seen from the above that the method of the present invention fully considers the complexity and dynamic characteristics of intermittent industrial process data, has better adaptability when processing different batches of data of intermittent processes, and improves the accuracy of fault detection.

[0098] Third embodiment

[0099] This embodiment provides an electronic device, such as Figure 3As shown, the electronic device includes: a processor and a memory; wherein the processor and the memory can be connected via a communication bus; the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method of the first embodiment. In addition, the electronic device may also include a transceiver, the processor and the transceiver can be connected via a communication bus, and the transceiver is used to communicate with other devices.

[0100] Next, combine Figure 3 The following is a detailed introduction to the various components of the electronic device:

[0101] Among them, the processor is the control center of the electronic device, and the electronic device may include multiple processors, each of which may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may be a processor or a general term for multiple processing elements. For example, the processor is one or more central processing units (CPUs), or other general-purpose processors, application specific integrated circuits (ASICs), or one or more integrated circuits configured to implement an 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), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor may execute various functions of the electronic device by running or executing software programs stored in the memory and calling data stored in the memory.

[0102] In a specific implementation, as an embodiment, the processor may include one or more CPUs, such as Figure 3 The CPU0 and CPU1 shown in the figure are, of course, only exemplary.

[0103] The memory is used to store the software program for executing the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0104] Optionally, the memory 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, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, 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 instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may be integrated with the processor or exist independently and accessed through the interface circuit ( Figure 3 (not shown) is coupled to the processor, which is not specifically limited in this embodiment of the present invention.

[0105] The transceiver may include a receiver and a transmitter ( Figure 3 The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function. The transceiver can be integrated with the processor or exist independently and communicate with the electronic device through the interface circuit ( Figure 3 (not shown) is coupled to the processor, which is not specifically limited in this embodiment of the present invention.

[0106] In addition, it should be noted that Figure 3 The structure of the electronic device shown in the figure does not constitute a limitation on the device, and the actual device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. In addition, the technical effects achieved by the electronic device when executing the method of the first embodiment above can refer to the technical effects described in the first embodiment above, so they are not repeated here.

[0107] Fourth embodiment

[0108] This embodiment provides a computer-readable storage medium, which stores at least one instruction, and the instruction is loaded and executed by a processor to implement the method of the first embodiment. The computer-readable storage medium may be a ROM, a random access memory, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc. The instructions stored therein may be loaded by a processor in a terminal to execute the method.

[0109] In addition, it should be noted that the present invention can be provided as a method, an apparatus or a computer program product. Therefore, the embodiment of the present invention can be in the form of a full or partial hardware embodiment, a full or partial software embodiment or an embodiment combining software and hardware. Moreover, when implemented using software, the embodiment of the present invention can be in the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. 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 devices. 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 site, computer, server or data center to another website site, computer, server or data center by wired (e.g., 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 containing one or more available media sets. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium. The semiconductor medium may be a solid state hard disk.

[0110] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0111] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable terminal device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0112] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements. In addition, the term "and / or" is only an association relationship describing the associated objects, indicating that there can be three relationships, for example, A and / or B, which can represent: A exists alone, A and B exist at the same time, and B exists alone, wherein A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the 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. "At least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can be represented by: a, b, c, ab, ac, bc or abc, where a, b, c can be single or plural.

[0113] In addition, it can 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.

[0114] 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.

[0115] In several embodiments provided by the present invention, it should be understood that the disclosed equipment, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of functional modules / units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0116] If the method is implemented in the form of a software functional unit and sold or used as an independent product, it 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 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 method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0117] Finally, it should be noted that the above is only a preferred embodiment of the present invention. It should be pointed out that although the preferred embodiment of the present invention has been described, for ordinary technicians in this technical field, once the basic creative concept of the present invention is known, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the protection scope of the present invention. Therefore, the attached claims are intended to be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the embodiments of the present invention.

Claims

1. A method for detecting intermittent process faults based on an improved adversarial autoencoder network, characterized in that: include: Under normal operation of the intermittent process, multiple batches of process data are collected, and the collected process data are preprocessed, and the preprocessed process data are used as training samples; Adding a prediction loss term to the loss function of the adversarial autoencoder network to construct a time window enhanced adversarial autoencoder network; wherein the prediction loss term is obtained based on a pre-trained time series predictor; Using training samples to train the time window enhanced adversarial autoencoder network; Intermittent process fault detection is achieved using the trained time window enhanced adversarial autoencoder network.

2. The intermittent process fault detection method based on improved adversarial autoencoder network according to claim 1, characterized in that: The process data of multiple batches are collected under the normal operation state of the intermittent process, and the collected process data are preprocessed, and the preprocessed process data are used as training samples, including: Under normal operation of the intermittent process, multiple batches of process data are collected and the collected data are expressed as a three-dimensional matrix of I×J×K, where I represents the data batch, J represents the number of observed variables, and K represents the number of sampling times; The collected process data in the form of a three-dimensional matrix are expanded in the batch direction to obtain a two-dimensional data matrix, and the two-dimensional data matrix is ​​standardized in the batch direction to eliminate the influence of different dimensions. The standardized two-dimensional data matrix is ​​rearranged to obtain the two-dimensional time series matrix data expanded according to the observed variables; wherein the data in each column of the two-dimensional time series matrix data reflects the change of a certain variable over time; the two-dimensional time series matrix data is used as a training sample.

3. The intermittent process fault detection method based on improved adversarial autoencoder network according to claim 1, characterized in that: The training process of the time series predictor includes: Use sliding time windows to divide the data in the training sample, convert the continuous time series data into multiple time segment data to obtain windowed data; each time segment data consists of observation data in the corresponding time period; Construct a time series predictor, use the windowed data as the input of the time series predictor, calculate the mean square error between the output data of the time series predictor and the actual data, and perform the back propagation algorithm to determine the final network parameters of the time series predictor, and obtain a trained time series predictor.

4. The intermittent process fault detection method based on improved adversarial autoencoder network according to claim 1, characterized in that: The method for obtaining the predicted loss term is as follows: The reconstructed output of the adversarial autoencoder network is input into the pre-trained time series predictor, the mean square error between the output of the time series predictor and the actual data is calculated, and the calculation result is used as the prediction loss term.

5. The intermittent process fault detection method based on improved adversarial autoencoder network according to claim 1, characterized in that: The loss function of the time window enhanced adversarial autoencoder network is calculated as follows: Calculate the mean square error between the reconstructed output of the adversarial autoencoder network and the original input of the adversarial autoencoder network, and use the mean square error between the reconstructed output and the original input as the reconstruction loss term; The sum of the prediction loss term and the reconstruction loss term is used as the loss function of the time window enhanced adversarial autoencoder network.

6. The intermittent process fault detection method based on improved adversarial autoencoder network according to claim 1, characterized in that: The method of using the trained time window enhanced adversarial autoencoder network to realize intermittent process fault detection includes: Based on the trained time window enhanced adversarial autoencoder network, the normal working condition data in the feature space and reconstruction space are counted, the corresponding statistics are calculated and their upper limits are determined, and the control threshold is set; Obtain process data during the current intermittent process operation, preprocess it, input the preprocessed current process data into the trained time window enhanced adversarial autoencoder network, and obtain the statistics of the current process data; Compare the statistics of current process data with control thresholds to complete fault detection.

7. The intermittent process fault detection method based on improved adversarial autoencoder network according to claim 6, characterized in that: The method based on the trained time window enhanced adversarial autoencoder network, statistically analyzing the normal working condition data in the feature space and the reconstruction space, calculating the corresponding statistics and determining the upper limit thereof, and setting the control threshold, includes: Input the training sample to the time window enhanced adversarial autoencoder network and calculate the T corresponding to each normal sample 2 After calculating the statistics and SPE statistics, the fault detection control limit T is determined using the kernel density estimation method. α 2 and SPE α .

8. The intermittent process fault detection method based on improved adversarial autoencoder network according to claim 7, characterized in that: The method of comparing the statistics of the current process data with the control threshold to complete the fault detection includes: If any statistic of the current process data exceeds the corresponding fault detection control limit, it is determined that a fault has occurred.

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