Intelligent gear defect analysis method based on fractional wavelet transform and BP neutral network

Abstract

The invention discloses an intelligent gear defect analysis method based on fractional wavelet transform and a BP neutral network. The intelligent gear defect analysis method includes: taking transform order as a variable to perform fractional Fourier transform on gear vibration signals to determine optimal order, and performing fractional wavelet transform on the gear vibration signals under the optimal order for denoising to realize separation of useful component and background noise of the gear vibration signals; calculating feature parameters of the signals after being denoised to form a group of feature vectors which are used for representing features of gear vibration after denoising; averagely dividing the feature vectors into two groups which serve as a training sample and a testing sample respectively, and inputting the feature vectors into the BP neutral network for learning and classifying. By the intelligent gear defect analysis method, background noise mixed in the gear meshing vibration signals is inhibited well, useful signal component related to defects is retained, and gear defect features can be extracted effectively; self learning and classifying capability of the BP neutral network is utilized, so that defect mode of gears can be quickly recognized qualitatively with high accuracy.

Description

technical field [0001] The invention relates to an intelligent analysis method for gear defects, in particular to an intelligent analysis method for gear defects based on Fractional Wavelet Transform (FRWT) and BP (Back Propagation) neural network, belonging to the field of gear matching detection and fault diagnosis. Background technique [0002] As an indispensable mechanical component for transmitting power and motion in modern mechanical structures, gears have the advantages of large load-carrying capacity, high transmission precision, and constant transmission power, and are widely used in mechanical equipment. With the development of large-scale, complex, automatic and continuous equipment, the loss of the entire production caused by the defects and failures of the gears will become greater and greater. Therefore, quality testing must be carried out in all aspects of gear production and operation. Especially before the gears are installed, the gears must be paired and...

AI Technical Summary

Problems solved by technology

Although the fractional Fourier transform has many unique properties, it cannot characterize the local characteristics of the signal; the short-time fractional Fourier transform has the defect of resolution, and the resolution of the instant domain and the fractional domain cannot be arbitrarily high at the same time; and the Radon-Wigner transform Belongs to a quadratic transformation with cross terms

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