Gear fault diagnosis platform and gear fault diagnosis method

Abstract

The invention discloses a gear fault diagnosis platform and a gear fault diagnosis method. On the platform, gear faults are simulated and vibration signals are collected, the improved local preserving projection algorithm is combined with a Bayes classifier, the mode recognition effect is judged according to the correct classification rate of the Bayes classifier, the vibration signals of the gear faults are measured by a vibration acceleration sensor, the principal component analysis is conducted firstly, and then, kernel transformation, construction of the nearest neighbor graph, solving of mapping space and the like are conducted; and the Bayes classifier is used for recognition in classification according to multi-fault classification. Compared with the principal component analysis, the laplace algorithm and the local preserving projection, the improved local preserving projection fault recognition rate can be greatly increased. According to the improved local preserving projection algorithm and the Bayes classifier combined fault mode recognition method, the fault recognition rate is increased and the accuracy is improved, and the effect of the fault mode recognition of the gear can be improved. The gear fault diagnosis platform has a simple structure, and a high-accuracy diagnosis platform can be provided for the fault recognition of the gear.

Description

technical field [0001] The invention relates to the technical field of gear fault diagnosis, in particular to a gear fault diagnosis platform and a gear fault diagnosis method. Background technique [0002] At present, the gear fault diagnosis test bench is used to simulate gear faults. By loading the gears, collecting fault signal vibration analysis under gear faults, and establishing a diagnostic database, it provides an effective basis for gear fault diagnosis. Vibration analysis is the application of various dynamic testing instruments to collect, record and analyze the vibration time-frequency domain signal changes of the vibrating parts in the gear on the gear fault test bench. Vibration analysis technology determines the location of the fault through the time-frequency domain analysis of the vibration part. Vibration signal processing mainly includes time-domain analysis, frequency-domain analysis and time-frequency domain analysis. [0003] The key to vibration dia...

AI Technical Summary

Problems solved by technology

However, linear dimensionality reduction methods such as principal component analysis are not effective when dealing with nonlinear structural data; nonlinear dimensionality reduction is mainly through manifold learning methods such as local preservation mapping, which only consider the local and global structure of high-dimensional data , without considering the category information between sample data points, it is an unsupervised classification

The fault classification and recognition rate of these methods is not ideal

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