摘要
针对传统的HMM模型状态数必须预先设定的不足,提出了一种基于DPMM-CHMM的机械设备性能退化评估方法。该方法利用DPMM模型的自动聚类功能,实现了模型结构根据观测数据的自适应变化和动态调整,获得设备运行过程中的最优退化状态数,并结合CHMM良好的分析和建模能力,得到设备退化状态转移路径,实现机械设备运行过程中的退化状态识别和性能评估,并利用滚动轴承全寿命数据进行了应用研究。结果表明,该方法可以有效地识别轴承运行中的不同退化状态,为基于状态的设备维修提供了理论指导。
Aiming at the deficiency of the traditional HMM model,the performance degradation evaluation method for mechanical equipment based on DPMM-CHMM was proposed. With this new method,the automatic clustering function of DPMM model was adopted to realize adaptive changes and dynamic adjustment of a structure model according to the observed data to get the optimal degradation state number in the operation process of mechanical equipment. With good analysis and modeling capabilities of CHMM,the equipment degradation state transition path was obtained to realize the degradation state recognition and performance assessment of mechanical equipment in its operation process. Rolling bearing whole life data were studied,the results showed that the proposed method is feasible,it provides a theoretical guidance for the maintenance of mechanical equipment based on its state.
出处
《振动与冲击》
EI
CSCD
北大核心
2017年第23期170-174,共5页
Journal of Vibration and Shock
基金
国家自然科学基金(51405246)
江苏省自然科学基金面上项目(BK20151271)
江苏省"六大人才高峰"高层次人才资助项目(2017-GDZB-048)
南通市应用基础研究项目(GY12016010)
关键词
狄利克雷混合模型
连续隐马尔可夫模型
性能退化评估
滚动轴承
Dirichlet process mixture model （ DPMM ）
continuous Hidden Markov model （ C H M M ）
Hidden Markov model （ HMM ）
performance degradation assessment
rolling bearing
