A method and device for bearing fault diagnosis based on supervised lle algorithm

Abstract

A bearing fault diagnosis method and apparatus based on a supervised LLE algorithm, the method comprising: acquiring training data, the training data being historical data representing bearing vibration signals, and extracting feature values of the training data and fault types corresponding to the feature values (S100); determining optimal dimensionality reduction training data of the training data and calculating the mean value and covariance matrix corresponding to each fault type in the optimal dimensionality reduction training data (S300); performing dimensionality reduction on test data received in real time to obtain dimensionality reduction test data (S400); and, on the basis of the mean values and the covariance matrices, calculating the probability value of the dimensionality reduction data in each fault type, and using the fault type with the greatest probability value as the fault type for the bearing fault diagnosis (S500). Thus, the online prediction rate of bearing fault diagnosis is improved.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis, in particular to a bearing fault diagnosis method and device based on a supervised LLE algorithm. Background technique [0002] As an emerging comprehensive marginal subject, bearing fault diagnosis technology has initially formed a relatively complete subject system. As far as its technical means are concerned, vibration diagnosis technology has become the mainstream technology of bearing fault diagnosis. The rapid progress of computer technology and signal information processing technology has greatly promoted the development of bearing fault diagnosis and monitoring technology in a scientific and practical direction. [0003] However, in the current field of bearing fault diagnosis, there are often large-scale concurrent data, which brings great challenges to the real-time requirements of fault diagnosis, and it is urgent to improve the online prediction rate of bearing fault diagnosi...

AI Technical Summary

Problems solved by technology

[0003] However, in the current field of bearing fault diagnosis, there are often large-scale concurrent data, which brings great challenges to the real-time requirements of fault diagnosis, and it is urgent to improve the online prediction rate of bearing fault diagnosis

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