Motor bearing fault diagnosis method based on recurrence plot and multi-layer convolutional neural network

Abstract

The invention discloses a motor bearing fault diagnosis method based on a recurrence plot and a multilayer convolutional neural network. The method comprises the following steps: collecting one-dimensional vibration signals of motor bearings under different working conditions; converting the one-dimensional vibration signal of the motor bearing into a two-dimensional recurrence plot by adopting arecurrence plot algorithm; carrying out shearing and thumbnail preprocessing on all recursive graphs; marking fault classification labels on all recursive graphs, and dividing into a training set anda test set; constructing a multilayer convolutional neural network model; inputting the training set into the model, performing iterative learning training, and optimizing multilayer convolutional neural network parameters until the multilayer convolutional neural network model converges; deploying the trained multi-layer convolutional neural network model, inputting a test set, determining faultclassification, evaluating diagnosis accuracy, adjusting a model structure, and optimizing parameters to be optimal; storing the optimal model; and collecting one-dimensional vibration signals of themotor bearing under a new working condition, converting the signals into a two-dimensional recursive graph, preprocessing the two-dimensional recursive graph, inputting the two-dimensional recursive graph into the optimal model, and carrying out fault classification and identification on motor bearing operation.

Description

technical field [0001] The invention relates to the field of motor bearing fault diagnosis, and more specifically, relates to a motor bearing fault diagnosis method based on a recursive graph and a multi-layer convolutional neural network. Background technique [0002] Bearing is a key part of the motor. If the motor bearing fails, it will cause equipment damage, shutdown and production, and even threaten life safety. Due to the complex internal structure of the motor, there is a nonlinear relationship between the fault characteristics and the fault type when the bearing fails. Most of the previous methods for motor fault diagnosis were to collect bearing vibration signals and manually extract features, which required rich signal processing experience and a large number of prior rules as support, and the diagnosis efficiency was low, which required a new method for feature extraction and troubleshooting. [0003] The vibration signals of most equipment are non-stationary a...

AI Technical Summary

Problems solved by technology

Most of the previous methods for motor fault diagnosis were to collect bearing vibration signals and manually extract features, which required rich signal processing experience and a large number of prior rules as support, and the diagnosis efficiency was low, which required a new method for feature extraction and troubleshooting

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