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A fault detection method for batch process based on deep learning

A technology of fault detection and deep learning, applied in electrical test/monitoring, test/monitoring control systems, instruments, etc., can solve the problems of slow fault detection, slow modeling speed, complex modeling data, etc., and achieve rapid modeling and detection, improve the effect of detection accuracy

Inactive Publication Date: 2021-02-19
HUZHOU TEACHERS COLLEGE
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Problems solved by technology

[0005] In order to solve the problem of the existing technology, the present invention is to solve the problems of intermittent process faults, the existing technology has complex modeling data in the detection process, and the slow modeling speed leads to slow fault detection speed, and proposes a deep learning-based intermittent process fault detection method

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  • A fault detection method for batch process based on deep learning
  • A fault detection method for batch process based on deep learning
  • A fault detection method for batch process based on deep learning

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Embodiment Construction

[0035] Assuming that the total number of batches of a batch of equal-length data X is I, the number of samples in each batch is J, and the number of variables is K, then the method of batch expansion is used. Such as figure 1 As shown, the three-dimensional data (J×K×I) ​​is expanded into a two-dimensional matrix (JK×I) in the batch direction. Among them, each column of the expanded matrix is ​​a batch of data, and finally the training data is obtained: X={x 1 ,x 2 ,...,x I}∈R JK×I .

[0036]Unlike Principal Component Analysis (PCA), the self-encoding network uses a nonlinear activation function to perform nonlinear transformations, such as nonlinear functions such as sigmoid function or tanh function, in order to enable the self-encoding network to extract features and reconstruct data, the original data needs to be scaled, otherwise the autoencoder network will not be able to reconstruct the data in a non-linear manner. Take the tanh activation function as an example: ...

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Abstract

A Deep Learning-Based Approach to Fault Detection in Batch Processes. This method does not need to make assumptions about the original data. First, the original data is equal-length and scaled, and the training is carried out on the deep neural network with convolution and multiple intermediate layers with the principle of minimum reconstruction error. The method automatically and accurately performs stage division and feature extraction; then builds a Gaussian mixture model on the coding layer of the network and performs clustering, which greatly reduces the amount of calculation for building a model while extracting features; finally, a global probability is proposed in combination with Mahalanobis distance Detection indicators to realize fault detection. Through simulation experiments in a kind of semiconductor etching process, the results show that the method can effectively improve the fault detection rate.

Description

technical field [0001] The invention relates to the field of fault detection, in particular to a deep learning-based intermittent process fault detection method. Background technique [0002] Batch production process is a kind of complex industrial process, which means that the production process is carried out in batches at the same location and at different times. It has been widely used in industrial production fields such as biopharmaceuticals, food, and semiconductor processing. Changes with time. Compared with continuous production, the process is more complex and changeable. Even a small abnormal condition in any process will affect the quality of the final product. Therefore, it is of great significance to find an effective process monitoring method for fault detection in batch processes. [0003] Since different operating stages have different process characteristics, the monitoring variables will be affected by the time dimension. Therefore, for the batch productio...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B23/02
CPCG05B23/024
Inventor 王培良王硕蔡志端徐静云周哲钱懿
Owner HUZHOU TEACHERS COLLEGE
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