基于CDA和MoG-BBN的齿轮磨损状态识别研究

提出了基于混合高斯输出贝叶斯信念网络模型的齿轮磨损状态识别新方法,建立了变量消元算法和期望最大化算法相结合的模型推理算法,通过计算待识别磨损特征向量的概率值来确定齿轮磨损状态。针对期望最大化算法容易局部收敛的问题,对其进行了改进,使其更容易获得全局最优值。根据磨损特征之间的非线性关系这一特性,应用曲线距离分析方法对特征进行降维。最后,利用五种不同工况下的齿轮磨损实验数据对模型进行验证。结果表明,该模型可以有效地识别齿轮磨损状态,识别正确率可以达到99%,为齿轮箱的健康管理提供了科学依据。

Wear degree identification of gear based on CDA and MoG-BBN

A new approach for identifying the wear degree of gear based on Mixture of Gaussians Bayesian Belief Network (MoG-BBN) was proposed. The inference algorithm was established through combining the variable elimination algorithm with expectation maximization algorithm. Then, one can recognize the gearbox wear states through identifying the hidden state of MoG-BBN which best fits the observations. Aiming at the local convergence problem of expectation maximization, a modified algorithm was proposed. According to the non-linear dependencies between features, the curvilinear distance analysis was used for dimension reduction. Finally, the data of gear’s wear experiment were used to demonstrate the proposed methods. The results showed the classification accuracy was 99%.