Intelligent gearbox fault diagnosis method based on multi-channel self-calibration convolutional neural network

Abstract

The invention discloses a gearbox intelligent fault diagnosis method based on a multichannel self-calibration convolutional neural network. The gearbox intelligent fault diagnosis method comprises the following steps: increasing dimension of glassmeter angle field data, converting one-dimensional vibration signals of a plurality of sensors into two-dimensional data, converting the two-dimensional data into gray level images as input, establishing a data set, and dividing the data set into a training set and a test set. And constructing a self-calibration convolutional neural network, and extracting data features. And setting a fusion layer, converting the output of the self-calibration convolutional neural network into one-dimensional data, and fusing feature information. And setting a full connection layer, and mapping the distributed features to a sample marking space. And constructing a Softmax feature classifier to classify the extracted features. And learning the network by using the training set, and testing the trained network by using the test set to realize fault diagnosis of the gearbox. The self-calibration convolutional neural network model provided by the invention is combined with an information fusion method, and can effectively diagnose a single fault of the gearbox under the same rotating speed working condition.

Description

technical field [0001] The invention belongs to the technical field of gearbox vibration signal intelligent fault diagnosis, and relates to a gearbox intelligent fault diagnosis method based on a multi-channel self-calibration convolutional neural network. Background technique [0002] With the rapid development of China's information technology and industrial generation technology, modern mechanical industrial equipment presents the characteristics of large-scale, high-speed, complex, intelligent and integrated, its functions are constantly improved, and its internal structure is increasingly sophisticated. Gearbox is the most widely used important component in mechanical equipment, and its smooth operation is crucial to the normal operation of the entire equipment. However, because the gearbox is often in harsh working conditions such as variable speed and high load, it is very prone to damage or even failure, resulting in huge economic losses and casualties. [0003] Cla...

AI Technical Summary

Problems solved by technology

[0005] The traditional convolutional neural network cannot differentiate the features of each channel, which may cause the network to learn similar feature forms

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