基于变分自编码器的轴承健康状态评估

针对现有数据驱动型轴承健康状态评估方法普遍存在的特征信息损失大、泛化能力弱和数据依赖强等问题，提出了一种面向高熵特征数据的变分自编码器(variational auto--encoder, 简称VAE)轴承健康状态评估模型。该模型通过学习健康状态下轴承振动信号频谱在特征空间中的高维潜在概率分布，实现对轴承运行健康状态的定量评估。首先，对基于VAE的健康状态评估模型进行理论阐述；其次，建立基于变分证据下界的状态评估指标；最后，通过对比实验证明：变分自编码器在处理轴承运行状态评估方面具有良好的准确度，对异常状态更为敏感；无需人为提取特征和复杂的参数设置，不需对特定的系统进行针对性的参数设置和调校；在小容量训练数据集上仍具备良好的鲁棒性，在工程应用上具有一定的推广价值。

Evaluation Method of Bearing Health State Based on Variational Auto-Encoder

Aiming at the problems such as high loss of characteristic information, weak generalization ability, and strong data dependence commonly existed in existing data-driven based methods for bearings health status evaluation, a new evaluation model based on variational auto-encoder (VAE) which allowing high-entropy characteristic input is proposed. By learning the high-dimensional potential probability distribution of the bearing vibration signal spectrum point in characteristic space, our model can quantitatively evaluate the bearing operating health state. First, the health status evaluation model based on VAE is theoretically elaborated. Afterward, a state assessment index based on the lower bound of variational evidence is established. As a consequence, it is proved that the variational auto-encoder has good accuracy in dealing with the evaluation of bearing running state and is more sensitive to the abnormal state through comparative experiments. Additionally, there is no need to extract features and set complex parameters artificially, also without setting and adjusting specific parameters for a specific system. Furthermore, it retains good robustness even with small training data sets as well as showcases certain promotion value of engineering application.