Process fault detection method and device based on introspection orthogonal auto-encoder, and medium

Abstract

The invention relates to a process fault detection method and device based on an introspection orthogonal auto-encoder and a medium, data samples to be detected are collected in a target manufacturing process test set, random noise is generated and input into a fault detection network, and in the fault detection network, an encoder obtains potential features of a hidden layer through the data samples and the random noise; the decoder obtains reconstruction data according to the potential features; and calculating a square prediction error of the data sample and the reconstructed data, and comparing the square prediction error with a fault warning threshold to obtain a fault detection result. Network parameters are updated by performing introspection training on an encoder module and a decoder module in an introspection orthogonal training module of the fault detection network, and negative sample data is added as a training numerical value. Compared with the prior art, the method has the advantages of high accuracy, strong adaptability, robustness and the like.

Description

Technical Field

[0001] The present invention relates to the field of multivariable fault detection in manufacturing processes, and in particular to a process fault detection method, device and medium based on an introspective orthogonal autoencoder. Background Art

[0002] Modern industrial manufacturing systems, such as chemical, steel, and semiconductor manufacturing, are becoming more complex and information-based in their manufacturing processes. This places higher demands on the reliability and safety of the manufacturing process, and requires timely and accurate fault detection to improve production efficiency and product quality, as well as to prevent machine damage and casualties. With the advancement of sensing technology and data storage technology, existing fault detection methods are trained by collecting a large amount of historical multivariate fault data. In the actual manufacturing process, the collected data has the characteristics of nonlinearity, high redundancy, and noise, and it is difficult to collect a large amount of fault type data. Therefore, in the absence of fault data, there are challenges in the effective training and accurate detection of fault detection models.

[0003] The autoencoder model is an unsupervised deep model, which mainly consists of three parts: encoder, hidden layer, and decoder. The training goal is to transform the input into the potential features of the hidden layer, and then reconstruct the original input from the potential features. The autoencoder model can train a model that can restore normal data and cannot restore fault data by only inputting normal manufacturing process data. In the fault detection process, if the fault detection index of the autoencoder model exceeds the fault warning threshold, it is judged as a fault. Although the autoencoder can perform fault detection without fault process data, in real manufacturing scenarios, there are problems such as noise interference and differences between normal and fault data only in individual variables, which makes it difficult for models that lack negative samples to participate in training to detect inconspicuous fault data. Therefore, how to improve the sensitivity of the model to normal data and improve the accuracy of fault detection in the absence of fault samples is a technical problem that needs to be solved. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a process fault detection method, equipment and medium based on introspective orthogonal autoencoder. In the introspective orthogonal training module of the fault detection network, the encoder module and the decoder module are introspectively trained to update the network parameters, and negative sample data are added to improve the sensitivity to abnormal data in the absence of fault samples, thereby achieving a higher fault detection accuracy.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] According to one aspect of the present invention, a process fault detection method based on an introspective orthogonal autoencoder is provided, comprising the following steps:

[0007] S1. Collect data samples to be tested in the target manufacturing process test set, generate random noise, and input it into the fault detection network;

[0008] S2. In the fault detection network, the encoder obtains the potential features of the hidden layer through data samples and random noise;

[0009] S3, the decoder obtains the reconstructed data according to the latent features;

[0010] S4. Calculate the square prediction error of the data sample and the reconstructed data, compare the square prediction error with the fault warning threshold, and obtain the fault detection result.

[0011] Furthermore, the encoder in S2 is composed of convolutional layers, and the decoder in S3 is composed of transposed convolutional layers.

[0012] Furthermore, the reconstructed data includes reconstructed positive samples and reconstructed negative samples, wherein the reconstructed negative samples are obtained according to random noise.

[0013] Furthermore, in the introspective orthogonal training module of the fault detection network, the encoder module and the decoder module are introspectively trained to update the network parameters.

[0014] Furthermore, the encoder updates the network parameters through introspective orthogonal training, and the encoder loss function Loss E The expression is:

[0015]

[0016] Among them, L R is the basic training function of positive samples, α is the weight parameter, L F is the basic training function for negative samples.

[0017] Furthermore, the decoder updates the network function through introspective orthogonal training, and the decoder loss function Loss D The expression is:

[0018] Loss D =L R +L F ,

[0019] Among them, L R is the basic training function of positive samples, L F is the basic training function for negative samples.

[0020] Furthermore, the positive sample basic training function L R The expression is:

[0021]

[0022] Among them, K is the number of positive samples, x k is the kth positive sample, y k represents the kth positive sample reconstructed data, λ represents the orthogonal regularization term weight coefficient, I is the unit matrix, and Z is the potential feature of the hidden layer corresponding to the positive sample;

[0023] Negative sample basic training function L F The expression is:

[0024]

[0025] Among them, K' is the number of negative samples, x k ' is the kth negative sample, y k ' represents the kth negative sample reconstruction data, λ represents the orthogonal regularization term weight coefficient, I is the unit matrix, and Z' is the potential feature of the hidden layer corresponding to the negative sample.

[0026] Furthermore, in S3, the expression for reconstructing data according to the potential features is:

[0027]

[0028] Among them, Y is the reconstructed data, a j represents the jth transposed convolution kernel, represents the transposed convolution operation, Z is the potential feature, and the expression is:

[0029] Z=σ(ΣX*w i +b),

[0030] Among them, σ is the hyperbolic tangent activation function, X is the input data sample to be detected, and w i is the i-th convolution kernel, * is the convolution operation, and b is the bias value.

[0031] According to a second aspect of the present invention, there is provided an electronic device, comprising a memory and a processor, wherein a computer program is stored in the memory, and the method described above is implemented when the processor executes the program.

[0032] According to a third aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, wherein the program implements the method described above when executed by a processor.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] (1) Improving the accuracy of fault detection: By introducing introspective orthogonal training, the encoder module and decoder module are introspectively trained and the network parameters are updated. In the introspective training process, the input noise is used as a negative sample through the decoder output to participate in the training, which makes up for the problem that the model is not sensitive enough to non-obvious abnormal data. At the same time, an orthogonal regularization term is added to the training function to improve the orthogonality of each potential feature and reduce the redundancy of the model feature learning information, so that the model can better identify potential fault data, improve the model feature learning ability, and improve the accuracy of fault detection.

[0035] (2) Adapting to conditions with a lack of fault samples: The process fault detection method based on the introspective orthogonal autoencoder can be trained using only normal manufacturing process data. By introducing introspective training and orthogonal regularization terms, the model’s sensitivity to abnormal data is improved, solving the problem of model training under conditions with a lack of fault samples.

[0036] (3) Improving the efficiency and robustness of fault detection: An autoencoder network model based on convolutional layers and transposed convolutional layers is used to improve the efficiency and robustness of fault detection by optimizing the network structure and training process. The convolutional layer can extract the local features of the input data, while the transposed convolutional layer restores these features to an approximate representation of the original input data. This network structure enables the model to process large amounts of data more quickly and accurately identify faulty data. At the same time, the introduction of introspective orthogonal training also enhances the robustness of the model, enabling it to maintain stable performance in the face of noise interference and data changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a flow chart of a process fault detection method based on introspective orthogonal autoencoder;

[0038] Figure 2 Schematic diagram of fault detection network training. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0040] like Figure 1 As shown, it is a flow chart of a process fault detection method based on introspective orthogonal autoencoder, which includes the following steps:

[0041] S1. Collect data samples to be tested in the target manufacturing process test set, generate random noise, and input it into the fault detection network;

[0042] S2. In the fault detection network, the encoder obtains the potential features of the hidden layer through data samples and random noise;

[0043] S3, the decoder obtains the reconstructed data according to the latent features;

[0044] S4. Calculate the square prediction error of the data sample and the reconstructed data, compare the square prediction error with the fault warning threshold, and obtain the fault detection result.

[0045] like Figure 2 As shown in Figure 1, it is the training process of the fault detection network. Figure 2 It can be seen that the encoder is composed of convolutional layers and the decoder is composed of transposed convolutional layers. The reconstructed data includes reconstructed positive samples and reconstructed negative samples, where the reconstructed negative samples are obtained according to random noise.

[0046] In the training process of the fault detection network, the historical data of the target manufacturing process is first used as the training set, including pressure, temperature, flow and composition. The data collected at each moment is 52, and it is standardized to obtain a unified order of magnitude and dimension. According to the training set, the fault network model is constructed.

[0047] In the introspective orthogonal training module of the fault detection network, the encoder module and the decoder module are introspectively trained to update the network parameters. The encoder updates the network parameters through introspective orthogonal training, and the encoder loss function Loss E The expression is:

[0048]

[0049] Among them, L R is the basic training function of positive samples, α is the weight parameter, L F is the basic training function for negative samples. The decoder updates the network function through introspective orthogonal training, and the decoder loss function Loss D The expression is:

[0050] Loss D =L R +L F ,

[0051] Among them, L R is the basic training function of positive samples, L F is the basic training function for negative samples.

[0052] Positive sample basic training function L R The expression is:

[0053]

[0054] Among them, K is the number of positive samples, x k is the kth positive sample, y k represents the kth positive sample reconstructed data, λ represents the orthogonal regularization term weight coefficient, I is the unit matrix, and Z is the potential feature of the hidden layer corresponding to the positive sample;

[0055] Negative sample basic training function L F The expression is:

[0056]

[0057] Among them, K' is the number of negative samples, x k ' is the kth negative sample, y k ' represents the kth negative sample reconstruction data, λ represents the orthogonal regularization term weight coefficient, I is the unit matrix, and Z' is the potential feature of the hidden layer corresponding to the negative sample.

[0058] The expression of reconstructed data based on potential features is:

[0059]

[0060] Among them, Y is the reconstructed data, a j represents the jth transposed convolution kernel, represents the transposed convolution operation, Z is the potential feature, and the expression is:

[0061] Z=σ(ΣX*w i +b),

[0062] Among them, σ is the hyperbolic tangent activation function, X is the input data sample to be detected, and w i is the i-th convolution kernel, * is the convolution operation, and b is the bias value.

[0063] The process fault detection method based on introspective orthogonal autoencoder in this embodiment significantly improves the accuracy of fault detection by introducing an introspective orthogonal training module. The model is trained using normal manufacturing process data, and the input data is converted into potential features of the hidden layer by the encoder, and then the decoder reconstructs the data. During the training process, noise is input into the hidden layer to generate negative samples for training, which enhances the sensitivity of the model to non-obvious abnormal data. At the same time, by adding orthogonal regularization terms, the orthogonality of each potential feature is improved, the redundancy of feature learning information is reduced, and the feature learning ability of the model is improved. The method of this embodiment can still achieve effective fault detection of manufacturing process data in the absence of fault samples, and has a high detection accuracy.

[0064] The comparison of the results of fault detection by the method in this embodiment and the traditional method is shown in Table 1. It can be seen from Table 1 that among the results of fault detection obtained by each model method, the detection rate / false alarm rate combination of the fault diagnosis model of the introspective autoencoder is 92.36% / 3.65%. The false alarm rate of the introspective autoencoder is lower than that of the introspective orthogonal autoencoder in this embodiment, but the detection rate is not ideal. The detection rate of the orthogonal autoencoder is close to that of the introspective orthogonal autoencoder, but the false alarm rate is too high. Among the various fault detection methods, the method of this embodiment has the best effect, has excellent detection performance, and has a high application value.

[0065] Table 1 Comparison of fault detection results of each model

[0066] Model Detection rate / false alarm rate Introspective Orthogonal Autoencoder Model 92.36 / 3.65 Introspective Autoencoder Model 75.12 / 0.07 Orthogonal Autoencoder Model 92.09 / 22.71 Conventional Autoencoder Model 86.10 / 5.35 Principal Component Analysis Model 78.40 / 3.26

[0067] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0068] The electronic device of the present invention includes a central processing unit (CPU), which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0069] A plurality of components in the device are connected to the I / O interface, including: an input unit, such as a keyboard, a mouse, etc.; an output unit, such as various types of displays, speakers, etc.; a storage unit, such as a disk, an optical disk, etc.; and a communication unit, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit allows the device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks. The processing unit performs the various methods and processes described above, such as the method of the present invention. For example, in some embodiments, the method of the present invention may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via a ROM and / or a communication unit. When the computer program is loaded into the RAM and executed by the CPU, one or more steps of the method of the present invention described above may be performed. Alternatively, in other embodiments, the CPU may be configured to perform the method of the present invention in any other appropriate manner (e.g., by means of firmware).

[0070] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0071] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0072] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0073] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A process fault detection method based on introspective orthogonal autoencoder, characterized in that: The following steps are involved: S1. Collect data samples to be tested in the target manufacturing process test set, generate random noise, and input it into the fault detection network; S2. In the fault detection network, the encoder obtains the potential features of the hidden layer through data samples and random noise; S3, the decoder obtains the reconstructed data according to the latent features; S4. Calculate the square prediction error of the data sample and the reconstructed data, compare the square prediction error with the fault warning threshold, and obtain the fault detection result.

2. A process fault detection method based on introspective orthogonal autoencoder according to claim 1, characterized in that: The encoder in S2 is composed of convolutional layers, and the decoder in S3 is composed of transposed convolutional layers.

3. The process fault detection method based on introspective orthogonal autoencoder according to claim 1 is characterized in that: The reconstructed data includes reconstructed positive samples and reconstructed negative samples, wherein the reconstructed negative samples are obtained according to random noise.

4. The process fault detection method based on introspective orthogonal autoencoder according to claim 1 is characterized in that: In the introspective orthogonal training module of the fault detection network, the encoder module and the decoder module are introspectively trained to update the network parameters.

5. A process fault detection method based on introspective orthogonal autoencoder according to claim 4, characterized in that: The encoder updates the network parameters through introspective orthogonal training, and the encoder loss function Loss E The expression is: Among them, L R is the basic training function of positive samples, α is the weight parameter, L F is the basic training function for negative samples.

6. A process fault detection method based on introspective orthogonal autoencoder according to claim 4, characterized in that: The decoder updates the network function through introspective orthogonal training, and the decoder loss function Loss D The expression is: Loss D =L R +L F , Among them, L R is the basic training function of positive samples, L F is the basic training function for negative samples.

7. A process fault detection method based on introspective orthogonal autoencoder according to claim 5 or 6, characterized in that: Positive sample basic training function L R The expression is: Among them, K is the number of positive samples, x k is the kth positive sample, y k represents the kth positive sample reconstructed data, λ represents the orthogonal regularization term weight coefficient, I is the unit matrix, and Z is the potential feature of the hidden layer corresponding to the positive sample; Negative sample basic training function L F The expression is: Among them, K' is the number of negative samples, x k ' is the kth negative sample, y k ' represents the kth negative sample reconstruction data, λ represents the orthogonal regularization term weight coefficient, I is the unit matrix, and Z' is the potential feature of the hidden layer corresponding to the negative sample.

8. The process fault detection method based on introspective orthogonal autoencoder according to claim 1 is characterized in that: In S3, the expression for reconstructing data according to the potential features is: Among them, Y is the reconstructed data, a j represents the jth transposed convolution kernel, represents the transposed convolution operation, Z is the potential feature, and the expression is: Z=σ(ΣX*w i +b), Among them, σ is the hyperbolic tangent activation function, X is the input data sample to be detected, and w i is the i-th convolution kernel, * is the convolution operation, and b is the bias value.

9. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the program, the method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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