Planetary gear case intelligent fault diagnosis method based on built-in encoder signal

Abstract

The invention provides a planetary gear case intelligent fault diagnosis method based on a built-in encoder signal. Firstly the built-in encoder signal of the output shaft position of a planetary gearcase is read by using an encoder data acquisition card to obtain the angular position signal of a test shaft, then the instantaneous angular acceleration signal of the planetary gear case is acquiredby using a polynomial fitting method, and instantaneous angular acceleration signal is segmented so as to obtain a training set, a verification set and a test set; then a deep convolutional neural network model is constructed; then the deep convolutional neural network model is trained by using the training set, and the parameter of each layer of network is adjusted through the verification set and a batch random gradient descent method so that the final convolutional neural network model is obtained; and finally the test data are inputted to the convolutional neural network model and the fault is identified and classified. The data acquisition program is simplified and the test expense is reduced, and the signal includes lots of state of health information so that extraction of the faultfeatures and intelligent and automatic monitoring and diagnosis can be facilitated by the convolutional neural network.

Description

technical field [0001] The invention relates to the technical field of planetary gearbox fault diagnosis, in particular to an intelligent fault diagnosis method for a planetary gearbox based on built-in encoder signals. Background technique [0002] Due to the advantages of compact structure, large transmission ratio and strong load capacity, planetary gearboxes are widely used in the fields of automobiles, ships, wind power, aviation, etc. Various failures often occur in key components such as rings. Once the planetary gearbox fails, it will cause mechanical equipment downtime, or cause huge property losses or even casualties. Therefore, it is of great significance to monitor the health and fault diagnosis of the planetary gearbox. [0003] Different from the traditional fixed-axis gearbox, the gear motion in the planetary gearbox is a typical compound motion, and its vibration signal is correspondingly more complex than that of the traditional gearbox, which makes it impo...

AI Technical Summary

Problems solved by technology

However, the current use of encoder signals for fault diagnosis usually requires cumbersome signal processing methods to gradually extract fault-related features for diagnosis. This process relies on many subjective factors, professional knowledge, and prior knowledge.

Therefore, these methods may miss some fault features, and easily cause misdiagnosis and missed diagnosis, making the fault diagnosis accuracy of planetary gearboxes low.

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