基于电流信号稀疏滤波特征融合的齿轮箱故障诊断方法

Current-based Gearbox Fault Diagnosis Based on Sparse Filtering Feature Fusion

风电齿轮箱的故障诊断方法主要以振动信号分析为主,相比于振动信号,电流信号具有非侵入式、监测成本低等优点。因此,提出基于发电机电流信号的风电齿轮箱故障诊断方法。针对电流信号基频分量干扰大、信噪比低而造成的特征提取难的问题,提出基于稀疏滤波网络的电流信号无监督特征学习与融合方法。首先,设计基于稀疏滤波的局部特征学习网络,用于从原始电流信号和包络信号中分别学习不同的故障特征;然后,将通过稀疏滤波网络学习到的原始信号稀疏特征与包络信号稀疏特征进行融合以丰富故障特征空间;最后,将融合的特征输入到支持向量机进行训练,实现不同故障类型的智能识别与诊断。通过风电齿轮箱实验台开展齿轮箱故障模拟实验来验证所提出的方法。实验结果表明,该方法能够从电流信号中自动提取反映齿轮故障的有用特征,相比于传统特征提取方法,取得了较高的诊断精度和效率。

Vibration signal-based analysis is the main fault diagnosis method for wind turbine gearboxes. Compared with the vibration signal, the current signal has the advantages of non-invasive and low monitoring cost. Therefore, a fault diagnosis approach for wind turbine gearbox based on the generator current signal is proposed. The current signal usually presents a large interference of fundamental frequency component and a low signal-to-noise ratio, leading to difficulties in feature extraction. In order to address these issues, an unsupervised feature learning and fusion method using current signals based on a two-layer sparse filtering network is proposed. Firstly, a local feature learning network based on sparse filtering is designed to learn different fault features from the original current signal and the envelope signal respectively. Then, the sparse features of the original signal and the envelope signal learned in the sparse filtering network are fused with the aim to enrich the fault feature space. Finally, the fused features are fed into the support vector machine for training to realize the intelligent recognition and diagnosis of different fault types. The proposed method is validated through the gearbox faults experiments on a wind turbine gearbox test rig. Experimental results show that the proposed method can automatically extract the useful features reflecting gear faults from the current signals. Compared with the traditional feature extraction method, it achieves higher diagnostic accuracy and efficiency.