US 12,360,008 B2
Method and device for fault diagnosis of wind turbine pitch bearing based on neural network
Weifei Hu, Hangzhou (CN); Feng Tang, Hangzhou (CN); Yaxuan Zhang, Hangzhou (CN); Zhenyu Liu, Hangzhou (CN); and Jianrong Tan, Hangzhou (CN)
Assigned to ZHEJIANG UNIVERSITY, Hangzhou (CN)
Filed by ZHEJIANG UNIVERSITY, Zhejiang (CN)
Filed on Dec. 1, 2022, as Appl. No. 18/073,534.
Claims priority of application No. 202210183631.6 (CN), filed on Feb. 28, 2022.
Prior Publication US 2023/0273093 A1, Aug. 31, 2023
Int. Cl. G01M 13/045 (2019.01); F03D 17/00 (2016.01); G06N 3/08 (2023.01)
CPC G01M 13/045 (2013.01) [F03D 17/00 (2016.05); G06N 3/08 (2013.01); F05B 2260/80 (2013.01)] 9 Claims
OG exemplary drawing
 
1. A method for fault diagnosis of wind turbine pitch bearing based on neural network, comprising:
S1: setting, by a low-frequency vibration acceleration sensor and a general-purpose vibration acceleration sensor, sample rolling angles to collect vibration signals from a fixed point of a blade, determining an optimal measurement rolling angle, and adjusting the blade correspondingly,
wherein said determining the optimal measurement rolling angle comprises:
calculating root mean square (RMS) values of the vibration signals at each test rolling angle, respectively, comparing the RMS values between the same measurement points, and taking a rolling angle with the maximum RMS value as the optimal measurement rolling angle;
S2: setting vibration measurement points on the pitch bearing;
S3: blocking the blade and adjusting a pitch speed, setting the range of pitch angle, and obtaining, by a vibration sensor and a vibration data acquisition card, the vibration signals of the pitch bearing under the different pitch speeds;
S4: sampling, by the vibration data acquisition card, collected vibration signals at intervals, and normalizing and performing wavelet threshold noise reduction on the sampled signals by a programmable logic controller, dividing the collected vibration signals into fault data and normal data according to health status of the pitch bearing, labelling the fault data and the normal data, and constructing a training dataset;
S5: inputting the training dataset into a Long Short-Term Memory (LSTM) neural network for training, wherein the LSTM neural network includes a set of LSTM cells, segmenting the training dataset into N parts, and feeding into each LSTM cell in sequence, transmitting the output of the last LSTM cell to a fully connected layer, activating the output of the fully connected layer by an activation function, and then calculating the loss of the network by comparing a predicted result with a real label; deploy trained LSTM neural network in a programmable logic controller of the wind turbine, collecting, by the vibration sensor and the vibration data acquisition card, the vibration signals of the wind turbine pitch bearing in real time, inputting the collected signals into the LSTM neural network in the programmable logic controller, and outputting a health status diagnosis result of the wind turbine pitch bearing, maintaining the wind turbine pitch bearing and adjusting variable pitch parameters according to the health status diagnosis result, to obtain the wind turbine pitch bearing without faults.