Method and system for diagnosing bearing faults of large-size wind turbine bearing

Abstract

The invention discloses a method and system for diagnosing bearing faults of a large-size wind turbine bearing. The method comprises the following steps of: firstly, measuring a vibration signal of a wind turbine main shaft bearing; secondly, automatically extracting a resonance frequency band of the bearing faults by adopting a self-adaptive spectrum kurtosis technology and obtaining a fault feature frequency by adopting a narrow-band filtering and envelope demodulation technology; and finally, comparing the fault feature frequency with a theoretical calculating value, positioning and recognizing to obtain a bearing fault type. The method and system disclosed by the invention are suitable for actual operation conditions of an actual wind turbine; and according to the method and system, the influence from non-stable work conditions and work condition noises of the wind turbine is overcome, the resonance frequency band caused by the bearing faults can be automatically recognized without manual operation and bearing fault types can be recognized automatically and quickly.

Description

technical field [0001] The invention belongs to the field of fault diagnosis of mechanical structures, and in particular relates to a method and system for fault diagnosis of a large-scale wind turbine bearing. Background technique [0002] With more and more attention paid to green energy, the world's wind power has developed rapidly in recent years. In 2009, China's newly installed wind power capacity ranked first in the world, and the total installed capacity was second only to the United States. However, the normal working hours and power generation of wind turbines in my country are not proportional to the installed capacity, which is far below the world average. The reason is that the main components of the fan drive system, such as gearboxes and bearings, have a high failure rate. In recent years, wind turbines have developed towards megawatt-class large models, and once they fail, they will cause greater economic losses. Therefore, it is urgent to carry out monitori...

AI Technical Summary

Problems solved by technology

In 2009, China's newly installed wind power capacity ranked first in the world, and the total installed capacity was second only to the United States. However, the normal working hours and power generation of wind turbines in my country are not proportional to the installed capacity, which is far below the world average. The reason is that the main components of the fan drive system, such as gearboxes and bearings, have a high failure rate

These methods are difficult to adapt to the actual structure and operation characteristics of large wind turbines, so these methods often cannot achieve the purpose of diagnosis well.

The premise of resonance demodulation and wavelet analysis is to know the position of resonance frequency band in advance, so it is limited in practical application

In addition, some researchers proposed spectral kurtosis technology based on short-time Fourier transform, but this technology uses a fixed window function and lacks adaptability

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