多元可逆深度概率学习及齿轮退化分析研究

Multivariate invertible deep probabilistic learning and gear degeneration evaluation

齿轮退化状态的准确评估对于设备安全运行具有重要意义。常规的齿轮退化状态评估方法的效果受特征提取、预处理等因素的影响。基于生成模型的状态评估方法利用原始观测进行评估,能够降低人为因素的影响。但传统生成模型如变分自编码器(VAE)存在边缘估计不准确的缺点。本文提出了多元可逆深度概率学习(MIDPL),通过叠加可以被优化的可逆变换实现从既定初始分布到未知观测分布的转换,将分布特性复杂的多观测序列转换至既定初始分布进行边缘概率计算继而实现状态评估。本文通过齿轮退化实验验证了MIDPL的有效性,与VAE相比,MIDPL在点蚀和断齿数据集下的评估误差分别降低了30.92%和69.25%,MIDPL能够实现更为稳定和准确的齿轮退化过程评估。

The gear degeneration evaluation technology plays an important role in maintaining safety of various equipment operation. The traditional gear degeneration evaluation methods are susceptible to feature extraction and data pre-processing tricks. The methods based on the generative model use raw observations to perform evaluation. And the human factors can be effectively reduced. However, traditional generative models, such as variational autoencoder(VAE), are limited by poor performance in marginal probability density evaluation. In this study, multivariate invertible deep probabilistic learning(MIDPL) is proposed, which can establish the connection between a given distribution and an unknown observation distribution by stacking learnable invertible transformation. The marginal probability density evaluation of the multi observation sequence can be realized through the given distribution. The proposed MIDPL model is evaluated by gear degeneration experiments. Compared with VAE, the evaluation errors of MIDPL for gear pitting dataset and gear breaking dataset are reduced by 30.92% and 69.25%, respectively. The proposed MIDPL can achieve more accurate and stable degeneration evaluation.