摘要
滚动轴承作为机械设备的关键部件,其健康状态直接影响设备性能与寿命,滚动轴承故障诊断问题已成为机械设备维护领域的研究热点。文中聚焦多源信号融合在滚动轴承故障诊断中的研究进展,系统梳理了该领域关键技术。首先阐述多源信号融合的背景内涵及其典型类型特征,其次从数据级、特征级、决策级三个层次解析融合框架,进而通过不确定性推理、模型驱动以及数据驱动分类方法对常见信号融合故障诊断方法进行梳理。最后展望了信号融合故障诊断方法的未来发展趋势。
As a key component of mechanical equipment,the health status of rolling bearings directly affects the performance and service life of equipment.Therefore,rolling bearing fault diagnosis has become a research hotspot in the field of mechanical equipment maintenance.This paper focuses on the research progress of multisource signal fusion in rolling bearing fault diagnosis and systematically reviews the key technologies in this field.First,the background,connotation,and typical type characteristics of multisource signal fusion are expounded.Second,the fusion framework is analyzed from three levels:data-level,feature-level,and decision-level.Furthermore,common signal fusion fault diagnosis methods are sorted out from the aspects of uncertainty reasoning,model-driven,and data-driven classification methods.Finally,the future development trends of signal fusion fault diagnosis methods are prospected.
作者
李军宁
温金鹏
白梦莎
王宇安
任举
LI Junning;WEN Jinpeng;BAI Mengsha;WANG Yu’an;REN Ju(School of Mechatronic Engineering,Xi’an Technological University,Xi’an 710021,China)
出处
《西安工业大学学报》
2025年第4期516-529,共14页
Journal of Xi’an Technological University
基金
国家自然科学基金项目(51505361)
陕西省教育厅服务地方专项项目(24JC045)
西安工业大学研究生教育教学研究生教学改革项目(XAGDYJ240804)。
关键词
滚动轴承
多源信号融合
特征提取
故障诊断
rolling bearings
multi-source information fusion
feature extraction
fault diagnosis
