上海交通大学学报 ›› 2021, Vol. 55 ›› Issue (10): 1272-1280.doi: 10.16183/j.cnki.jsjtu.2019.169
所属专题: 《上海交通大学学报》2021年12期专题汇总专辑; 《上海交通大学学报》2021年“机械工程”专题
收稿日期:
2019-06-18
出版日期:
2021-10-28
发布日期:
2021-11-01
通讯作者:
胡雄
E-mail:huxiong@shmtu.edu.cn
作者简介:
王微(1983-),女,浙江省金华市人,讲师,主要从事港口机械状态监测与故障预测研究.
基金资助:
WANG Wei, WANG Bing, HU Xiong(), SUN Dejian
Received:
2019-06-18
Online:
2021-10-28
Published:
2021-11-01
Contact:
HU Xiong
E-mail:huxiong@shmtu.edu.cn
摘要:
针对岸桥起升减速箱的退化状态评估问题,提出一种基于在线改进符号序列熵(O_ISSE)与逻辑回归的退化状态在线评估方法.首先引入阈值因子,保留信号变化方向与幅值的“粗粒化”信息,降低原算法对于冲击成分的“敏感性”,提出改进后的符号序列熵(ISSE).采用滑动窗Weibull拟合的方法有效滤除ISSE特征序列中的波动影响,形成O_ISSE.最后,训练并建立逻辑回归模型,在线计算得到未知样本的健康因子H值,实现未知样本的状态识别.采用上港某码头在线监测的起升机构减速箱全寿命数据进行实例分析.研究结果表明,所提ISSE和逻辑回归模型方法能够挖掘信号中的复杂度变化规律,准确地跟踪并识别性能退化状态.
中图分类号:
王微, 王冰, 胡雄, 孙德建. 基于在线改进符号序列熵与逻辑回归模型的岸桥起升减速箱在线退化评估[J]. 上海交通大学学报, 2021, 55(10): 1272-1280.
WANG Wei, WANG Bing, HU Xiong, SUN Dejian. Online Degradation Assessment of Shore Bridge Hoisting Gearbox Based on Improved Symbolic Sequence Entropy and Logistic Regression Model[J]. Journal of Shanghai Jiao Tong University, 2021, 55(10): 1272-1280.
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