Chatter Detection and Diagnosis in Hot Strip Mill Process With a Frequency-Based Chatter Index and Modified Independent Component Analysis

In this article, we propose a framework to monitor the chatter phenomenon and to diagnose the cause variables of chatter occurred in the hot strip mill process (HSMP). For monitoring chatter, we develop a chatter index (CI) that quantifies chatter to confirm its occurrence. Based on the data classified as normal by the CI, a multivariate statistical process monitoring model for detecting chatter is constructed using the modified independent component analysis (MICA) method. The monitoring results show that the model based on the MICA outperforms other models based on the principal component analysis and independent component analysis. For the diagnosis of the cause variables of detected chatter, various contribution plots can be used. In this article, we develop a relative contribution plot for a more obvious diagnosis than the existing contribution plot. Using this, we diagnose and analyze the cause variables of the detected chatter in the HSMP.

[1]  Ping Zhang,et al.  A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process , 2012 .

[2]  Mohammad Reza Niroomand,et al.  Theoretical and Experimental Analysis of Chatter in Tandem Cold Rolling Mills Based on Wave Propagation Theory , 2015 .

[3]  Du-Ming Tsai,et al.  Defect Detection in Solar Modules Using ICA Basis Images , 2013, IEEE Transactions on Industrial Informatics.

[4]  Yong Zang,et al.  Rolling process and its influence analysis on hot continuous rolling mill vibration , 2016 .

[5]  Vladimir Panjkovic,et al.  Causes of chatter in a hot strip mill: Observations, qualitative analyses and mathematical modelling , 2012 .

[6]  Plant-Wide Industrial Process Monitoring: A Distributed Modeling Framework , 2016, IEEE Transactions on Industrial Informatics.

[7]  P. Miller,et al.  Contribution plots: a missing link in multivariate quality control , 1998 .

[8]  ChangKyoo Yoo,et al.  Statistical process monitoring with independent component analysis , 2004 .

[9]  Yan Yang,et al.  Image Denoising by Sparse Code Shrinkage , 2009, 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing.

[10]  Huijun Gao,et al.  Data-Based Techniques Focused on Modern Industry: An Overview , 2015, IEEE Transactions on Industrial Electronics.

[11]  Age K. Smilde,et al.  Generalized contribution plots in multivariate statistical process monitoring , 2000 .

[12]  M.-C. Theyssier.  Manufacturing of advanced high-strength steels (AHSS) , 2015 .

[13]  Kaixiang Peng,et al.  A Novel Scheme for Key Performance Indicator Prediction and Diagnosis With Application to an Industrial Hot Strip Mill , 2013, IEEE Transactions on Industrial Informatics.

[14]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.

[15]  S.J. Qin,et al.  Multiblock principal component analysis based on a combined index for semiconductor fault detection and diagnosis , 2006, IEEE Transactions on Semiconductor Manufacturing.

[16]  Chuanhou Gao,et al.  Guest Editorial: Special section on data-driven approaches for complex industrial systems , 2013, IEEE Trans. Ind. Informatics.

[17]  Xiaojie Zhang,et al.  Monitoring of continuous steel casting process based on independent component analysis , 2010, 2010 Chinese Control and Decision Conference.

[18]  In-Beum Lee,et al.  Fault detection and diagnosis based on modified independent component analysis , 2006 .

[19]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[20]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[21]  Zhiqiang Ge,et al.  Distributed Parallel PCA for Modeling and Monitoring of Large-Scale Plant-Wide Processes With Big Data , 2017, IEEE Transactions on Industrial Informatics.

[22]  S. Joe Qin,et al.  Reconstruction-Based Fault Identification Using a Combined Index , 2001 .

[23]  Xiaoqiang Yan,et al.  Dynamic Model of the Hot Strip Rolling Mill Vibration Resulting from Entry Thickness Deviation and Its Dynamic Characteristics , 2019, Mathematical Problems in Engineering.

[24]  Bernard Legube,et al.  Principal component analysis: an appropriate tool for water quality evaluation and management—application to a tropical lake system , 2004 .

[25]  Mohammad Reza Niroomand,et al.  Experimental Investigations and ALE Finite Element Method Analysis of Chatter in Cold Strip Rolling , 2012 .

[26]  Tony L. Schmitz,et al.  Chatter recognition by a statistical evaluation of the synchronously sampled audio signal , 2003 .

[27]  Mohammad Reza Niroomand,et al.  Frequency analysis of chatter vibrations in tandem rolling mills , 2012 .

[28]  Xiang Li,et al.  Multi-modal Sensing for Machine Health Monitoring in High Speed Machining , 2006, 2006 4th IEEE International Conference on Industrial Informatics.

[29]  Wolfgang Rasp,et al.  Application of the theory of rolling to rolling in the case of mill vibrations , 1986 .