High-speed gear milling machine spindle box fault diagnosis method and system based on fault mechanism simulation and data fusion

By constructing a simulated physical model and data fusion method based on Archard wear model, combining adaptive wavelet transformation and dynamic gated convolutional neural network, the wear state recognition problem in the fault diagnosis of spindle box of high-speed milling machines is solved, the accuracy and adaptability of fault recognition are improved, and data support is provided for the health management system.

CN120448957APending Publication Date: 2025-08-08NANJING TECH UNIV
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Patent Information

Application Number
CN202510475211.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-16
Publication Date
2025-08-08

AI Technical Summary

Technical Problem

The existing fault diagnosis method is difficult to accurately identify the wear status of the gears in the spindle box of the high-speed milling machine, resulting in abnormal vibration of the cutter plate and affecting the processing quality and accuracy.

Method used

By constructing a simulation physics model based on Archard wear model, combining adaptive wavelet transformation and dynamic gated convolutional neural network, a spindle box fault diagnosis model is built, and simulation simulation data and actual vibration data are integrated to realize the health status of the spindle box system.

Benefits of technology

Improves the accuracy and adaptability of fault identification, makes up for the limitations of a single method, and provides reliable criterion for the spindle box health management system.

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Abstract

The invention provides a fault diagnosis method and system for a spindle box of a high-speed gear milling machine based on fault mechanism simulation and data fusion. The method mainly comprises the following steps: S1, calculating a tooth profile wear curve of a gear based on an Archard wear principle; s2, three-dimensional modeling software is used for building system models in different wear states, and simulation data sets under different wear degrees are obtained with the help of dynamics simulation software; s3, establishing a data acquisition system to obtain an experimental vibration data set under the actual working condition of the spindle box; s4, constructing a spindle box fault diagnosis model by using the analogue simulation data set obtained in the step S2; and S5, performing fault diagnosis on the experimental vibration data set obtained in the step S3 by using the spindle box fault diagnosis model constructed in the step S4, and identifying the health condition of the spindle box system. Important data support is provided for a main axle box fault diagnosis and health management system, and the diagnosis accuracy and reliability are improved.
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