Deep-learning-based method for intermittent process fault detection

A technology of fault detection and deep learning, applied in electrical testing/monitoring, testing/monitoring control systems, instruments, etc., can solve problems such as slow modeling speed, slow fault detection speed, complex modeling data, etc., to improve detection accuracy efficiency, rapid modeling and detection

Inactive Publication Date: 2019-01-01
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 mo

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  • Deep-learning-based method for intermittent process fault detection
  • Deep-learning-based method for intermittent process fault detection
  • Deep-learning-based method for intermittent process fault detection

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Embodiment Construction

[0036] 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 .

[0037] 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

The invention relates to a deep-learning-based method for intermittent process fault detection. According to the method not requiring original data hypothesis, equal-length and scaling processing is carried out on original data, training is carried out on a deep neural network with convolution and a plurality of intermediate layers based on a minimum reconstruction error principle, and stage division and feature extraction are carried out in a non-linear manner automatically and precisely; a Gaussian mixture model is built on a coding layer of the network and clustering is carried out, so thatthe model building calculation load is reduced substantially while features are extracted; and then a global probability detection index is put forward by combining a mahalanobis distance, thereby realizing fault detection. On the basis of the simulation experiment of one kind of semiconductor etching process, the result demonstrates that the fault detection rate can be increased effectively by using the method.

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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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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