Fault Diagnosis of Wind Turbine Based on Convolution Neural Network Algorithm

Abstract

Relying on expert diagnosis, it solves the problem of fan failure efficiency and meets the needs of automatic inspection and intelligent operation monitoring of fans. In order to make up for the deficiency of intelligent diagnosis of bearing fault based on vibration signal detection, signal transformation, and convolution neural network identification and improve the ability of intelligent diagnosis, this study designs a deep convolution neural network model and diagnosis algorithm with three pairs of convolution pooling layers and two full connection layers. The experimental verification of the proposed method is carried out based on the public data set, and the effects of three different signal transformation methods based on vibration signal through vibration gray map, short-time Fourier transform time-frequency map, and continuous wavelet transform time-frequency map on the accuracy of diagnosis model are compared and analyzed. A very accurate guarantee is received, close to 100%. The final experimental results demonstrate the effectiveness of the information on the accuracy of diagnostic testing and provide new ideas for the verification and testing of wind turbine wind energy. Compared with other machine learning algorithms, the real-time recognition of machine learning based on time-domain statistical features is lower than that of convolutional neural network methods. The effect of the scale of the trained model on the accuracy of the algorithm is discussed. A sample ratio of 50% and 70% was found to be appropriate.

Image Feature Enhancement and Its Application in Fault Diagnosis of Rotating Machinery: A Review

“…For instance, in bearing diagnosis, Kapla et al 51 utilize grayscale image conversion to extract features. Similarly, Xiao et al 52 utilize gray image conversion to diagnose fan faults. The accuracy of image processing using a convolutional neural network has been observed to be close to 100%.…”

Dynamic Condition Adversarial Adaptation for Fault Diagnosis of Wind Turbine Gearbox

“…With the advancement of artificial intelligence technology, rotating machinery fault diagnosis methods based on deep learning such as NN, LSTM, and DBN have experienced rapid development [3]. These methods are also applied to the fault diagnosis of the WT gearbox [4][5][6][7][8][9]. Huang et al utilized wavelet packet decomposition to process the vibration signals of the gearbox.…”