极端条件下基于改进深度森林的行星齿轮箱故障诊断方法

齿轮箱是风电机组重要且易出现故障的设备，早期故障威胁系统运行安全。在极端条件中高效、准确的齿轮箱故障诊断对风电机组的安全稳定运行至关重要，因此提出了基于改进深度森林的行星齿轮箱故障诊断方法。为了实现不平衡小样本与强噪声的极端条件下行星齿轮箱故障的高效诊断，首先针对旋转机械振动数据样本较少与不平衡的情况，在Wasserstein生成对抗网络中引入梯度惩罚，生成样本补充原始数据集。然后利用多粒度扫描处理振动信号数据点之间的联系，增强数据中的故障特征。最后在级联森林内部引入新的基学习器并运用量子粒子群算法优化参数，获得具有高诊断精度的模型结构进行故障分类，输出诊断结果。经与其他智能诊断方法在多场景下进行的对比实验，证实了所提方法在极端条件下的分类效果较好，能有效提高齿轮箱故障诊断的准确性。

Fault diagnosis technology of a planetary gearbox based on an improved deep forest algorithmunder extreme conditions

A gearbox is an important but fault-prone part of wind turbine. Early faults threaten the operational safety of the system. Efficient and accurate gearbox fault diagnosis in extreme conditions is very important for safe and stable operation, thus, a planetary gearbox fault diagnosis algorithm based on an improved deep forest is proposed. To realize the efficient diagnosis of planetary gear box faults in extreme conditions of unbalanced small samples and strong noise, this paper first introduces gradient punishment in a Wasserstein generation countermeasure network to generate samples to supplement the original data set in view of the small and unbalanced vibration data samples of rotating machinery. Second, multi granularity scanning is used to deal with the relationship between vibration signal data points to enhance the fault characteristics in the data. Finally, a new base learner is introduced into the cascade forest and the quantum particle swarm optimization algorithm is used to optimize the key parameters to obtain a model structure with high diagnostic accuracy for fault classification and to output diagnostic results. Compared with other intelligent diagnosis methods in multiple scenarios, it is proved that the proposed method has good classification effect in extreme conditions, and can effectively improve the accuracy of gearbox fault diagnosis.