Fault diagnosis method based on improved convolutional neural network

A convolutional neural network and fault diagnosis model technology, applied to biological neural network models, neural architectures, instruments, etc., can solve the problems of gradient disappearance, large amount of calculation, weak model generalization ability, etc., to reduce size and enhance generalization The effect of the ability

Active Publication Date: 2020-08-25
JIANGNAN UNIV
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Problems solved by technology

CNN combines the convolution operation with the backpropagation algorithm to complete the self-learning training of the convolution kernel parameters, but CNN has the inherent shortcomings of deep learning and traditional neural networks: gradient disappearance, over-fitting, large amount of calculation and model generalization Weak ability

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  • Fault diagnosis method based on improved convolutional neural network
  • Fault diagnosis method based on improved convolutional neural network
  • Fault diagnosis method based on improved convolutional neural network

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Embodiment Construction

[0012] The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0013] The application provides a fault diagnosis method based on an improved convolutional neural network, the method comprising the steps of:

[0014] Step S1, obtain a sample data set, perform data preprocessing on the sample data set to obtain a training set and a test set, wherein the sample data set includes time series data of each state type, and the state types of this application include normal state and q-type fault state, If q is a positive integer, the obtained sample data set can be expressed as {time series data of normal state, time series data of fault state 1, time series data of fault state 2...time series data of fault state q}, each A set of time series data consists of several data points.

[0015] The method for preparing a training set and a test set based on a sample data set includes the following steps S1a-S1d:

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Abstract

The invention discloses a fault diagnosis method based on an improved convolutional neural network, and relates to the technical field of fault diagnosis. The method includes: establishing an improvedconvolutional neural network model, wherein the improved convolutional neural network model sequentially comprises an input layer, a plurality of feature extraction layers, a newly added convolutional layer, a full connection layer and an output layer, each feature extraction layer sequentially comprises a convolution layer and a pooling layer; performing training by using the training set and the test set based on an improved convolutional neural network model to obtain a fault diagnosis model for automatic fault diagnosis. According to the method, a newly-added convolution layer is furtherarranged between a feature extraction layer and a full connection layer, the newly-added convolution layer can extract deep features of the model, the generalization ability of the model is effectively enhanced, meanwhile, the feature extraction layer is improved, the risk of over-fitting can be reduced to a certain extent, and the calculation speed is increased.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis, in particular to a fault diagnosis method based on an improved convolutional neural network. Background technique [0002] In recent years, the application of machine learning in the fields of fingerprint recognition, text recognition, speech recognition, fault diagnosis and image classification has become more and more extensive, basically meeting the requirements of commercialization. As a branch of machine learning, deep learning solves the difficult problems of traditional deep neural network training. [0003] As a member of the deep learning model, CNN (Convolutional Neural Network) is widely used in the field of image recognition and fault diagnosis. It can realize adaptive feature extraction and intelligent classification, and has a strong processing ability for high-dimensional and nonlinear data. In addition, , CNN can ensure the invariance of feature extraction while maintaini...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/214
Inventor 沈艳霞常淼赵芝璞
Owner JIANGNAN UNIV
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