数据驱动的热轧过程故障诊断研究现状与展望

doi:10.13228/j.boyuan.issn1006-9356.20230473

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Abstract Hot rolling process is an important cohering link in the whole production process of iron and steel, whose safe production and efficient operation are of great significance to the high-quality developments of iron and steel industry. Due to the complex and variable working conditions, clear system hierarchy of hot rolling process as well as the interconnection and coupling of loops, faults of multiple processes, equipment, subsystems or control loops may occur at the same time or cascade successively, which make the theories and methods of fault diagnosis for hot rolling process become research hotspots in the field of process control. Based on the analysis of characteristics and main fault types of hot rolling process, the research status of data-driven fault diagnosis methods for hot rolling process is emphatically summarized, the advantages and disadvantages of mainstream methods are qualitatively analyzed. Moreover, the existing problems of fault diagnosis for hot rolling process are sorted out, and the development directions in the future are further prospected.

Key words： hot rolling process fault diagnosis data-driven intelligent diagnosis machine learning deep learning

Received: 02 August 2023

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	Articles by authors
	MA Liang
	SHI Fuzhong
	PENG Kaixiang

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MA Liang,SHI Fuzhong,PENG Kaixiang. Status and prospects in data-driven fault diagnosis for hot rolling process[J]. China Metallurgy, 2023, 33(12): 12-17.

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http://www.zgyj.ac.cn/EN/10.13228/j.boyuan.issn1006-9356.20230473 OR http://www.zgyj.ac.cn/EN/Y2023/V33/I12/12

[1]	刘文仲. 中国钢铁工业互联网发展现状及思考[J]. 中国冶金,2022,32(11):1.
[2]	赵会平,陆宁云,姜斌. 轧钢过程故障诊断研究现状及发展趋势[J]. 轧钢,2011,28(1):48.
[3]	马亮,彭开香,董洁. 工业过程故障根源诊断与传播路径识别技术综述[J]. 自动化学报,2022,48(7):1650.
[4]	邵健,何安瑞,陈雨来,等. 热轧智能工厂构架设计与实践:有形与无形的统一[J]. 中国冶金,2022,32(1):1.
[5]	王国栋,吴迪,刘振宇,等. 中国轧钢技术的发展现状和展望[J]. 中国冶金,2009,19(12):1.
[6]	彭艳. 冶金轧制设备技术数字化智能化发展综述[J]. 燕山大学学报,2020,44(3):218.
[7]	孙杰,汪龙军,任辉,等. 轧制过程多工序指标建模及优化的研究现状与发展趋势[J]. 冶金自动化,2022,46(2):57.
[8]	马亮,彭开香. 带钢热轧全流程质量建模与异常溯源的研究现状与展望[J]. 冶金自动化,2022,46(6):16.
[9]	石怀涛,宋文丽,袁哲,等. 轧钢机状态监测与故障诊断技术[J]. 控制工程,2015,22(增刊1):82.
[10]	DONG M,LIU C. Design and fault diagnosis observer for HAGC system on strip rolling mill[J]. Journal of Iron and Steel Research,International,2006,13(4):27.
[11]	GU D W,POON F W. A robust fault-detection approach with application in a rolling-mill process[J]. IEEE Transactions on Control Systems Technology,2003,11(3):408.
[12]	张义方,何成,崔立,等. 传动-压下-带钢耦合激励CSP轧机振动分析[J]. 中国冶金,2022,32(9):79.
[13]	刘坤,王晓晨,张天明. 基于有限元仿真的热连轧精轧带钢楔形控制分析[J]. 中国冶金,2023,33(9):104.
[14]	李亚新,刘元铭,王振华,等. 不锈钢/碳钢复合板纵波轧制搓轧变形机理[J]. 钢铁,2023,58(8):138.
[15]	李维刚,邓肯,刘翱,等. 基于案例学习的PC轧机板形智能设定方法[J]. 钢铁研究学报,2017,29(8):654.
[16]	殷瑞钰. 流程型制造流程的本质与共性规律[J]. 钢铁,2023,58(2):1.
[17]	王贵成,贺振,高俊丽,等. 基于灰色系统理论的我国钢铁产业与GDP关系研究[J]. 矿冶工程,2019,39(2):128.
[18]	张芮. 热连轧主传动设备状态监测与故障诊断系统的设计及应用[J]. 冶金自动化,2008,32(3):65.
[19]	刘佳伟,金鹏,陈亮,等. 基于自适应数字滤波的旋转机械动平衡边缘计算[J]. 中国冶金,2022,32(2):102.
[20]	王明,徐慧东,和东平,等. 颗粒阻尼吸振器对轧机辊系减振特性的研究[J]. 振动与冲击,2023,42(2):23.
[21]	崔金星,邓烁,彭艳,等. 工业数据驱动的轧机振动预测和工艺优化[J]. 振动·测试与诊断,2022,42(1):110.
[22]	LIU Q,ZHU Q Q,QIN S J,et al. Dynamic concurrent kernel CCA for strip-thickness relevant fault diagnosis of continuous annealing processes[J]. Journal of Process Control,2018,67:12.
[23]	张飞,郭强,申屠南凯. 基于PLS的热连轧过程故障检测与诊断[J]. 轧钢,2008,25(6):45.
[24]	ZHANG K,DONG J,PENG K X. A novel dynamic non-Gaussian approach for quality-related fault diagnosis with application to the hot strip mill process[J]. Journal of the Franklin Institute,2017,354(2):702.
[25]	MA L,DONG J,PENG K X,et al. Hierarchical monitoring and root-cause diagnosis framework for key performance indicator-related multiple faults in process industries[J]. IEEE Transactions on Industrial Informatics,2019,15(4):2091.
[26]	PENG K X,ZHANG K,DONG J,et al. Quality-relevant fault detection and diagnosis for hot strip mill process with multi-specification and multi-batch measurements[J]. Journal of the Franklin Institute,2015,352(3):987.
[27]	赵春晖,余万科,高福荣. 非平稳间歇过程数据解析与状态监控-回顾与展望[J]. 自动化学报,2019,45:1.
[28]	谢丰,付文鹏,李阳,等. 基于贝叶斯决策模型的热轧卷筒电机故障诊断[J]. 中国冶金,2021,31(4):68.
[29]	章昕,张飞,肖雄,等. 基于量子粒子群算法-支持向量机的冷连轧断带故障诊断[J]. 冶金自动化,2020,44(6):17.
[30]	王国柱,刘建昌,李元,等. 加权k最近邻重构分析的工业过程故障诊断[J]. 控制理论与应用,2015,32(7):873.
[31]	ELSHENAWY L M,CHAKOUR C,MAHMOUD T A. Fault detection and diagnosis strategy based on k-nearest neighbors and fuzzy C-means clustering algorithm for industrial processes[J]. Journal of the Franklin Institute,2022,359(13):711.
[32]	YANG W T,REIS M S,BORODIN V. An interpretable unsupervised Bayesian network model for fault detection and diagnosis[J]. Control Engineering Practice,2022,127:105304.
[33]	时培明,刘奥运,张逸伦,等. 基于动态贝叶斯网络与DS证据理论的轧机颤振监测方法[J]. 计量学报,2022,43(9):1178.
[34]	袁杰,王福利,王姝,等. 基于D-S融合的混合专家知识系统故障诊断方法[J]. 自动化学报,2017,43(9):1580.
[35]	王晓雯,张勇军,郭强,等. 基于轧制机理和混合神经网络的热轧精轧带宽预测[J]. 中国冶金,2023,33(2):114.
[36]	刘小杰,张玉洁,李欣,等. 基于大数据的高炉炉温监测预警系统[J]. 中国冶金,2023,33(2):98.
[37]	王帅,李强. 联合大数据和神经网络的高炉透气性预报模型[J]. 钢铁,2023,58(7):46.
[38]	柴天佑,丁进良. 流程工业智能优化制造[J]. 中国工程科学,2018,20(4):51.
[39]	KATZEF M,CULLEN A C,ALPCAN T,et al. Generative adversarial networks for anomaly detection on decentralised data[J]. Annual Reviews in Control,2022,53:329.
[40]	WELLSANDT S,KLEIN K,HRIBERNIK K,et al. Hybrid-augmented intelligence in predictive maintenance with digital intelligent assistants[J]. Annual Reviews in Control,2022,53:382.
[41]	ALINEZHAD H S,ROOHI M H,CHEN T W. A review of alarm root cause analysis in process industries:Common methods,recent research status and challenges[J]. Chemical Engineering Research and Design,2022,188:846.
[42]	杨朋澄,张凯,彭开香. 基于深度置信网络的热连轧精轧过程故障诊断方法[J]. 冶金自动化,2022,46(6):78.
[43]	孙建亮,孙孟乾,郭贺松,等. 热轧带材多缺陷和单缺陷表面质量综合预报[J]. 钢铁,2021,56(1):51.
[44]	张瑞成,刘力菲,梁卫征. 基于SSAE-LSTM模型的冷连轧机扭振预测[J]. 锻压技术,2023,48(4):193.
[45]	CACCIARELLI D,KULAHCI M. A novel fault detection and diagnosis approach based on orthogonal autoencoders[J]. Computers and Chemical Engineering,2022,163:107853.
[46]	葛超,杨奇睿,刘佳伟,等. 基于空洞卷积神经网络与注意力机制GRU的滚动轴承故障诊断[J]. 中国冶金,2022,32(4):99.
[47]	刘艳菊,王秋霁,赵开峰,等. 基于卷积神经网络的热轧钢条表面实时缺陷检测[J]. 仪器仪表学报,2021,42(12):211.
[48]	代岩,黄瑞,方田,等. 基于深度学习的热轧过钢检测追踪系统[J]. 冶金自动化,2022,46(5):76.
[49]	邓能辉,侯睿,叶俊明. 基于深度学习的圆钢表面缺陷检测系统[J]. 中国冶金,2022,32(12):113.
[50]	ZHU Z,SHI H B,SONG B,et al. Convolutional neural network based feature learning for large-scale quality-related process monitoring[J]. IEEE Transactions on Industrial Informatics,2022,18(7):4555.