Timely and accurate diagnosis of gearbox vibration signals is crucial for reducing the operational costs of wind turbines.An algorithm based on particle swarm optimization-neutrosophic K-nearest neighbor(PSO-NKNN)for this purpose is proposed in this paper.First,in the initial stage,wavelet packet decomposition and reconstruction techniques are used to extract features from the raw signal in order to capture its energy characteristics.Then,the particle swarm optimization(PSO)algorithm is introduced to optimize the nearest neighbor algorithm(NKNN).Finally,a PSO-NKNN fault diagnosis model is constructed and experimentally validated using real data collected from the QPZZ-Ⅱ platform.The experimental results show that this method compensates for the uncertainty in the weight distribution of the"false"membership degree in the NKNN,effectively improving classification accuracy while enhancing the model's noise resistance.
维普
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