Vibration fault diagnosis method of wind turbine generator rolling bearing

Abstract

The invention discloses a vibration fault diagnosis method of a wind turbine generator rolling bearing. The method comprises the following steps of step1, using a vibration sensor to collect wind turbine generator rolling bearing inner ring fault vibration signals; step2, using a model of a bistable system to carry out noise reduction processing on the wind turbine generator rolling bearing inner ring fault vibration signals and outputting a noise reduction signal; step3, carrying out variation modal decomposition on the noise reduction signal output by the step2 so as to acquire IMF components of different frequency ranges; and step4, after the step3, carrying out spectral analysis on each IMF component acquired through VMD decomposition, finding a fault characteristic frequency of a rolling bearing and completing vibration fault diagnosis of the wind turbine generator rolling bearing. By using the vibration fault diagnosis method, accurate diagnosis to a vibration fault of the wind turbine generator rolling bearing can be realized.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis methods, and relates to a vibration fault diagnosis method for a wind turbine rolling bearing, in particular to a vibration fault diagnosis method for a wind turbine rolling bearing based on stochastic resonance and variational mode decomposition. Background technique [0002] With the progress of the times, energy shortage and environmental problems have become increasingly prominent, and the development and utilization of renewable energy has attracted widespread attention from all over the world. As a renewable and clean energy, wind power has relatively mature technology, large-scale commercial development conditions and relatively low cost. It has been valued by various countries in recent years. However, wind turbines work in complex environments such as alternating loads, multiple working conditions, and large temperature differences for a long time. Various faults are prone to occu...

AI Technical Summary

Problems solved by technology

Since the vibration fault signal of the rolling bearing of the wind turbine is easily affected by the strong noise environment, the fault characteristic frequency of the extracted IMF component is not obvious, and it is difficult to accurately locate the fault

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