Fault predication and service life evaluation system and method of wind power main bearing

Abstract

The invention discloses a fault predication and service life evaluation system of a wind power main bearing. The fault predication and service life evaluation system comprises a data acquisition unit,a data storage unit, a fault predication unit and a service life evaluation unit, wherein the data acquisition unit comprises a plurality of sensors for detecting a vibration component and a temprature signal of the wind power main bearing and a pressure signal in a cavity of the main bearing, and a data acquisition module connected with the plurality of sensors; the data storage unit is used forreceiving data and storing; the fault predication unit is used for carrying out principle component analysis on the data transmitted by the data storage unit, so as to realize fault predication of the wind power main bearing; the service life evaluation unit is used for predicating a residual service life of the main bearing through carrying out the principle component analysis on the data transmitted by the data storage unit. The invention further discloses a fault predication and service life evaluation method of the wind power main bearing. According to the fault predication and service life evaluation system, after the data is acquired, the data is subjected to PCA (Principal Component Analysis) processing; bearing faults are predicated based on a neural network model; the service life of the bearing is predicted based on a similarity principle, and real-time lubricating adjustment of the bearing is also realized based on a predication result.

Description

technical field [0001] The invention relates to the technical field of wind power main bearings, in particular to a system and method for fault prediction and life evaluation of wind power main bearings. Background technique [0002] The design life of the main bearing of the wind turbine is 20 years, and the main working conditions are characterized by low speed and heavy load. Bearing life is closely related to the lubrication state, and the decrease in sealing performance will lead to grease leakage, temperature rise, and eventually bearing failure. [0003] The existing wind power main bearing fault prediction generally adopts the general bearing fault diagnosis technology "based on vibration signal". The signal is single and cannot fully reflect the operating state of the bearing, and sometimes misjudgment may occur. In addition, the speed of the wind power main bearing is low, and the vibration signal is difficult to identify. It is difficult to identify the early fai...

AI Technical Summary

Problems solved by technology

Bearing life is closely related to the lubrication state, and the sealing performance will decrease, which will lead to grease leakage, temperature rise, and eventually bearing failure

[0003] The existing wind power main bearing fault prediction generally adopts the "vibration signal-based" general bearing fault diagnosis technology. The signal is single and cannot fully reflect the operating status of the bearing, and sometimes misjudgment problems may occur

In addition, the speed of the wind power main bearing is low, the vibration signal is not easy to identify, and the early failure of the main bearing is difficult to identify, but when the vibration signal of the failure is identified, a serious failure has already occurred

[0004] In addition, the existing fault prediction technology is still unable to early identify and predict the lubrication state of the main bearing, and cannot combine the main bearing fault prediction technology to adjust the lubrication amount of the main bearing in real time to improve the lubrication state of the main bearing and avoid early failure of the bearing

[0005] It can be seen that the above-mentioned existing wind power main bearing failure prediction and life evaluation system obviously still has inconvenience and defects, and needs to be further improved.

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