Expert Experience and Data-Driven Based Hybrid Fault Diagnosis for High-SpeedWire Rod Finishing Mills

作     者：Cunsong Wang Ningze Tang Quanling Zhang Lixin Gao Haichen Yin Hao Peng 

作者机构：Institute of Intelligent ManufacturingNanjing Tech UniversityNanjing210009China Beijing Gaohuahan Intelligent Technology Co.Ltd.Beijing100084China BeijingWavelet Rhythm Technology Co.Ltd.Beijing100020China College of Mechanical and Power EngineeringNanjing Tech UniversityNanjing211816China 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2024年第138卷第2期

页      面：1827-1847页

核心收录：

学科分类：070801[理学-固体地球物理学] 07[理学] 0708[理学-地球物理学] 0701[理学-数学] 

基　　金：the National Key Research and Development Program of China under Grant 2021YFB3301300 the National Natural Science Foundation of China under Grant 62203213 the Natural Science Foundation of Jiangsu Province under Grant BK20220332 the Open Project Program of Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System under Grant 2022A0004 

主　　题：High-speed wire rod finishing mills expert experience data-driven fault diagnosis 

摘      要：The reliable operation of high-speed wire rod finishing mills is crucial in the steel production *** complex system-level equipment,it is difficult for high-speed wire rod finishing mills to realize fault location and real-time *** solve the above problems,an expert experience and data-driven-based hybrid fault diagnosis method for high-speed wire rod finishing mills is proposed in this ***,based on its mechanical structure,time and frequency domain analysis are improved in fault feature *** approach of combining virtual value,peak value with kurtosis value index,is adopted in time domain *** adjustment and side frequency analysis are proposed in frequency domain analysis to obtain accurate component characteristic frequency and its corresponding ***,according to time and frequency domain characteristics,fault location based on expert experience is proposed to get an accurate fault ***,the proposed method is implemented in the equipment intelligent diagnosis *** taking an equipment fault on site,for example,the effectiveness of the proposed method is illustrated in the system.