Fault classification method based on one-dimensional multi-path 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 Effects of sensitivity, feature diversity, and high robustness

CN110033021AInactive Publication Date: 2019-07-19HUAZHONG UNIV OF SCI & TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2019-07-19
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses a fault classification method based on a one-dimensional multi-path convolutional neural network, and belongs to the technical field of industrial process monitoring. Accordingto the method, a traditional two-dimensional convolutional neural network is improved; multiple paths of parallel one-dimensional convolutional neural networks are applied in the variable direction;the cross correlation among variables is deconstructed, the variables are independently subjected to convolution and pooling to extract time sequence feature information, the extracted features are more diversified, the robustness is higher, the defect that a traditional two-dimensional convolutional neural network is sensitive to the prior arrangement order of the variables in input data is overcome, and the method is more suitable for complex and high-order industrial process data; experiments show that the fault classification model obtained based on the one-dimensional multi-path convolutional neural network training provided by the invention can effectively perform fault classification of industrial process data, and has higher generalization capability compared with a common model.
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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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Embodiment Construction

[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...