Method for diagnosing bearing breakdown of wind generating set

Abstract

The invention discloses a method for diagnosing bearing breakdown of a wind generating set. The method comprises the steps that vibration signals of a bearing are acquired; a wavelet packet analysis method is used for conducting three-layer decomposition on the vibration signals, soft threshold quantitative processing is conducted on high-frequency coefficients under all decomposition scales, and one-dimensional wavelet reconstruction is conducted according to the low-frequency coefficients and the high-frequency coefficients of a bottommost layer of wavelet decomposition; wavelet packet decomposition is conducted on the reconstructed vibration signals, the energy of all frequency bands on a third layer is extracted, the energy of all the frequency bands constitutes a breakdown diagnosis input vector with a breakdown feature input vector used as a BP neural network, and a three-layer BP neural network is established; a feature input vector sample of historical breakdown data is acquired and input to the three-layer BP neural network for training; a breakdown diagnosis feature vector of real-time operation data of the bearing is acquired and input to the trained BP neural network; intelligent diagnosis of bearing breakdown types is achieved. The method for diagnosing bearing breakdown of the wind generating set can be used for precisely diagnosing the bearing breakdown types and precisely positioning breakdown positions.

Description

technical field [0001] The invention relates to a method for diagnosing a bearing fault of a wind power generating set, in particular to a method for diagnosing a bearing fault of a wind power generating set based on a wavelet packet-BP neural network. Background technique [0002] With the development of wind power generation technology, the installed capacity of wind power generators is increasing continuously, and it is constantly developing in the direction of large-scale. However, the rapid growth of the wind power industry has also led to a continuous increase in the operation and maintenance costs of wind turbines. Bearings are important components in both double-fed wind turbines and direct-drive wind turbines. Bearing damage will directly affect the performance of the whole machine. Operating conditions. In order to reduce the downtime of wind turbines and reduce the maintenance costs of the turbines, it is necessary to perform precise fault diagnosis on important ...

AI Technical Summary

Problems solved by technology

However, the traditional method based on spectrum analysis can not only accurately diagnose the type of bearing fault, but also cannot accurately determine the fault location, which is difficult to meet the needs

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