Bearing fault diagnosis method

Abstract

The invention relates to a bearing fault diagnosis method which is characterized by including the following steps: (1) obtaining multiple groups of vibration acceleration signal data of the spindle bearing of a wind generating set in different states, randomly extracting a plurality of groups of vibration acceleration signal data as standard sample data, and taking the other groups of vibration acceleration signal data as to-be-detected sample data; (2) adaptively decomposing the standard sample data and the to-be-detected sample data to obtain a series of intrinsic rotational components; (3)making a time-domain analysis of the instantaneous amplitude and instantaneous phase information of the first intrinsic rotational components in the standard sample data and the to-be-detected sampledata, and extracting bearing fault feature vectors; (4) inputting the extracted fault feature vector of the standard sample data to a neighbor distance classifier for training to get a trained fault diagnosis model; and (5) inputting the extracted bearing fault feature vector of the to-be-detected sample data to the trained fault diagnosis model for fault identification to obtain the fault state of the bearing. The bearing fault diagnosis method of the invention can be widely applied to bearing fault diagnosis.

Description

technical field [0001] The invention relates to the technical field of equipment fault diagnosis, in particular to a bearing fault diagnosis method. Background technique [0002] Wind energy, as a renewable new energy with the greatest potential for large-scale development, has developed rapidly in recent years. Due to the harsh operating environment of wind turbines, with the increase of the accumulated running time of the wind turbines, the components of the wind turbines are prone to failure. Therefore, it is necessary to carry out online monitoring of wind turbines in order to grasp the operating status of the wind turbines in time and ensure safe and stable operation. Bearing failures account for a high proportion of wind turbine failures, and their vibration signals are very complex. [0003] When a rolling bearing fails, its vibration signal is non-stationary. Since the time-frequency analysis method can provide localized information of the vibration signal in the ...

AI Technical Summary

Problems solved by technology

EMD can adaptively decompose complex multi-component signals into the sum of several IMF (Intrinsic modefunction) components, but this method has problems such as over-envelope, under-envelope, modal confusion, endpoint effects, and slow decomposition speed.

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