关键词: 健康状态评估, 多尺度分析, 改进l1趋势滤波, 模糊C均值聚类, 评估准则, 滚动轴承

Abstract: To solve the problem that real-time online quantitative evaluation of rolling bearing health status of rotating equipment in industrial enterprises,based on the monotonicity principle,the mean value of multi-scale correlation number and multi-scale spectral distance index were selected as the health index.By using multi-scale analysis,improved l1   trend filtering and fuzzy C-means clustering analysis,a data-driven real-time health evaluation model and the evaluation criteria knowledge base of rolling bearing were constructed.Referring to the basic principle of transfer learning,a mapping method of real-time data to the coordinate system corresponding to the constructed knowledge base of health assessment criteria was proposed.The health assessment model trained by the experimental data of "run to failure" can be applied to engineering practice.Taking the second group of bearing experimental data of the University of Cincinnati Intelligent Maintenance System (IMS) center as the model training data,the knowledge base of bearing health assessment criteria was established.The first group of experimental data of IMS center and the vibration acceleration data of P3409A centrifugal pump bearing in hydrocracking unit of a petrochemical company in China were used as the model validation data to verify the health assessment model of rolling bearing.The results showed that the model had generalization and could effectively represent the health state of rolling bearing.The evaluation process did not rely on the prior knowledge of external experts and did not need the historical fault data of the bearing to be evaluated,which had important engineering application value for realizing the "unmanned" intelligent operation and maintenance of rotating equipment.

Key words: health assessment, multi-scale analysis, improved l1 trend filtering, fuzzy C-means clustering, evaluation criteria, rolling bearings