TY -的A2 - Chen Miaochao AU -李,本盟——亏,Le AU -罗,京东AU -陈,期间PY - 2021 DA - 2021/12/20 TI -再生的机械设备的寿命预测方法基于振动信号特征提取的SP - 1962896六世- 2021 AB -机械设备是机械设备的重要组成部分,和它的工作状态直接关系到机械设备的整体性能。准确的评估和预测机械设备的性能退化趋势具有重要意义,确保机械设备系统的可靠性和安全性。基于典型的故障设备的数据,本文分析了机械设备的能量特征参数在不同类型和程度的失败在时域。利用振幅谱分析、希尔伯特包络解调和小波包分解方法,和其他振动信号处理方法,初步提取多个统计特征参数。其次,针对多个不相关和冗余组件的统计参数,机械设备的新方法提取故障特征提出了基于方差值和主成分分析。该方法可以有效地机械设备的故障状态进行分类。验证了该方法的有效性通过实际设备的信号。之后,从振动信号中提取价值的双排辊设备作为降解特性。为了减少的影响,不规则的振动信号特征,简化复杂的振动信号,小波变换和支持向量机模型相结合,根据分解后的降解。95%置信区间的预测价值。 The SVM model is established based on data characteristics, and single-step and multistep prediction of equipment degradation trends are carried out. The prediction result shows that, according to the mapping position formula, the distribution of equipment degradation prediction points is obtained, and a 95% confidence interval based on the distribution of the prediction points is given. Finally, on the basis of completing feature extraction, this paper applies an unsupervised feature selection method. The sensitive characteristics of life prediction and the prediction results of a single SVM model and a neural network model are compared and analyzed at the same time. SN - 1687-9120 UR - https://doi.org/10.1155/2021/1962896 DO - 10.1155/2021/1962896 JF - Advances in Mathematical Physics PB - Hindawi KW - ER -