Bearing health state evaluation method and device based on neural network

Abstract

The invention relates to the technical field of fault diagnosis and particularly relates to a bearing health state evaluation method and device based on a neural network. The method comprises the steps of acquiring training data which is historical data representing a bearing vibration signal, extracting a characteristic value of the training data and a fault type corresponding to the characteristic value, determining optimal dimension reduction training data of the training data, calculating a mean value and a covariance matrix corresponding to each fault type in the optimal dimension reduction training data, obtaining dimension reduction test data by performing dimension reduction on the test data received in real time, calculating the probability value of the dimension reduction data under each fault type according to the mean value and the covariance matrix, and taking a fault type with a maximum probability value as the fault type of bearing fault diagnosis. The online predictionrate of the bearing fault diagnosis is improved.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis, in particular to a neural network-based bearing health status evaluation method and device. 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 diagnosis. C...

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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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