A Fault Classification Method Based on One-Dimensional Multi-way Convolutional Neural Network

A technology of convolutional neural network and fault classification, which is applied in the field of fault classification based on one-dimensional multi-channel convolutional neural network, can solve problems such as low accuracy and precision of fault identification, sensitive variable order, etc., and achieve the goal of overcoming the order of variables The effect of sensitivity, feature diversity, and high generalization ability

Inactive Publication Date: 2021-04-06
HUAZHONG UNIV OF SCI & TECH
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Problems solved by technology

[0006] Aiming at the application defects of the existing two-dimensional convolutional neural network technology, the purpose of the present invention is to deconstruct the correlation between variables by using the multi-channel operation of one-dimensional convolution and pooling, and independently analyze the local timing characteristics of process variables For fault classification, it aims to solve the problem that the two-dimensional convolutional network is sensitive to the order of variables, resulting in low accuracy and precision of fault identification

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  • A Fault Classification Method Based on One-Dimensional Multi-way Convolutional Neural Network
  • A Fault Classification Method Based on One-Dimensional Multi-way Convolutional Neural Network
  • A Fault Classification Method Based on One-Dimensional Multi-way Convolutional Neural Network

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[0052]In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0053] refer to figure 1 A fault classification method based on a one-dimensional multi-channel convolutional neural network model provided by an embodiment of the present invention includes the following steps:

[0054] (1) Collect data under various fault states in the industrial process, and perform fault marking and standardization processing on these data to construct a data set;

[0055] (2) Build a multi-channel one-dimensional convolutional neural network model, and perform feature extraction on the data set; the multi-channel one-dimensional convolutional neural network model includes sequ...

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Abstract

The invention discloses a fault classification method based on a one-dimensional multi-channel convolutional neural network, belonging to the technical field of industrial process monitoring. This method improves the traditional two-dimensional convolutional neural network, uses multi-channel parallel one-dimensional convolutional neural network along the variable direction, deconstructs the mutual correlation between variables, and performs convolution on each variable independently , pooling and extracting time series feature information, the extracted features are more diversified and more robust, overcoming the sensitivity of the traditional two-dimensional convolutional neural network to the prior arrangement of variables in the input data, and more suitable for complex, high-level industrial process data; experiments show that the fault classification model based on the one-dimensional multi-channel convolutional neural network training provided by the present invention can effectively classify the faults of industrial process data, and has a higher generality than commonly used models. ability.

Description

technical field [0001] The invention belongs to the technical field of industrial process monitoring, and more specifically relates to a fault classification method based on a one-dimensional multi-channel convolutional neural network. Background technique [0002] Fault classification technology plays an important role in root cause diagnosis and troubleshooting after industrial process accidents. It has become a cross-cutting technology, mainly involving the fields of statistics, applied mathematics, signal analysis and machine learning. With the in-depth research and continuous development across fields, the different fault classification methods that many experts and scholars have applied can be summarized into two categories: mathematical modeling and data-driven. Among the data-driven methods, the deep learning method has achieved performance beyond the traditional signal analysis, multivariate statistical learning and other algorithms and has been widely used. [000...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/2431
Inventor 郑英金淼张洪徐琦王彦伟
Owner HUAZHONG UNIV OF SCI & TECH
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