Gear case fault detection method based on deep transfer learning

Abstract

The invention provides a gearbox fault detection method based on deep transfer learning, and the method comprises the steps: collecting original signal data of a gearbox, carrying out the noise reduction and data cleaning of an original signal, carrying out the extraction through a feature extractor, obtaining a new image, building a model, and carrying out the result analysis and verification of the model through training data and verification data. According to the method, the technical problem that the type of gearbox fault diagnosis is single is solved, the image data of the vibration signals, the temperature signals and the oil liquid signals of the gearbox are innovatively combined in a crossed mode, multiple sets of composite image data are obtained, a composite fault model is finally constructed, and the fault diagnosis accuracy is improved. The obtained convolutional neural network model is migrated to gearbox diagnosis detection data, the migration diagnosis fault rate is calculated, the diagnosis rate is 94%-100%, high-precision diagnosis is achieved, and rapid fault detection of the gearbox under the composite fault condition is achieved.

Description

[0001] field of invention [0002] The present invention relates to the technical field of gearbox detection, in particular, the present invention relates to the technical field of gearbox fault detection based on transfer learning. Background technique [0003] The gearbox is an important functional component for transmitting motion and deploying speed in the modern equipment manufacturing industry. The gearbox system generally includes four parts: gears, bearings, shafts and boxes. Gearbox failures can be divided into mechanical failures, electrical failures, and auxiliary system failures. , Mechanical failures are mainly gear failures, bearing failures, box failures, electrical failures and auxiliary system failures mainly include cooling failures, oil supply failures, and sensor failures. Among these three types of faults, electrical faults and auxiliary system faults occur more frequently, but the consequences are relatively not serious, and the handling is more convenien...

AI Technical Summary

Problems solved by technology

[0006] However, the category of gearbox fault diagnosis is relatively single at present. In fact, the working environment of the gearbox and the failure situation are complex. When the gearbox fails, it is often not a single component failure, but usually a composite failure. Therefore, in the present On the basis of some vibration detection, it is necessary to consider making full use of various signals in the gearbox to provide a gearbox fault detection method based on deep transfer learning