Establishing method of rolling bearing intelligent diagnosis model based on convolutional neural network

Abstract

The invention relates to a design method of an establishing method of a rolling bearing intelligent diagnosis model based on a convolutional neural network. Firstly a one-dimensional vibration signalis mapped into two-dimensional image information, and the two-dimensional image data are used for training a network model; and then the structural parameters of the convolutional neural network in the application process are analyzed and the better network parameters are selected so that the convolutional neural network structure having high mechanical fault classification capacity is obtained. Accurate identification and classification of the mechanical fault under the complex condition of different loads and different rotating speed can be realized; and the convolutional neural network model can greatly enhance the feature extraction capacity of the neural network by establishing the multilayer network, mastering of manual technology for mass signal processing and dependence on the diagnosis experience can be overcome, the fault features can be directly acquired from the original time domain signal through the learning mode to perform diagnosis, and adaptive extraction of the faultfeatures and intelligent diagnosis of the state of health under the condition of TB level data volume per hour can be realized.

Description

technical field [0001] The invention belongs to the field of intelligent diagnosis of large rotating mechanical equipment, and in particular relates to a method for establishing an intelligent diagnosis model of a rolling bearing based on a convolutional neural network. Background technique [0002] In the field of machinery, many large-scale equipment such as automotive gearboxes, large-scale wind power equipment, and aeroengines are developing in the direction of high efficiency, high precision, and high speed. In order to ensure that these equipment can run stably and safely, they are often equipped with corresponding fault detection systems. Due to the large number of equipment detection points, long detection time, and high sampling frequency of detection points, the detection system has obtained a large amount of equipment data, and the field of mechanical fault diagnosis is gradually moving towards the era of big data. For example, a hot rolling steel plate productio...

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Problems solved by technology

[0003] In order to solve the problems existing in the prior art, the present invention discloses a method for establishing a rolling bearing intelligent diagnosis model based on a convolutional neural network, which overcomes the need for manual mastery of a large number of signal processing technologies and dependence on diagnostic experience in the past, It can directly obtain fault features in the original time-domain signal through learning, so as to make a diagnosis, and realize adaptive extraction of fault features and intelligent diagnosis of health status in the case of terabytes of data per hour

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