Transactions of Materials and Heat Treatment

Title：Fault detection of finish rolling process for hot strip rolling based on improved HMM

Authors： Zhang Ruicheng Cui Kaixin Liang Weizheng

Unit： College of Electrical Engineering North China University of Science and Technology

KeyWords： strip steel hot rolling fault detection wavelet transform principal component analysis hidden Markov model

ClassificationCode：TP273

year,vol(issue):pagenumber：2023,48(3):126-131

Abstract：

A new method of fault detection was proposed to improve the HMM based on WT and PCA to solve the problems of low accuracy of the traditional HMM method and the nonlinearity and mixed Gaussianity of the hot strip rolling process data. Firstly, wavelet transform was used to denoise the rolling data, and PCA was used to reduce the dimensionality and correlation of the data, which can effectively reduce the number of iterations for model training and improve the accuracy of fault detection. Then, the WT-PCA-HMM fault detection model was obtained by using the expectation maximization algorithm combined with the training of observed sequence training. Finally, the logarithmic likelihood values of the finishing process data was derived from the model to achieve the fault detection. The results show that the WT-PCA-HMM fault detection method can not only reduce the false alarm rate by 8.1% compared with the traditional HMM method, but also reduce the number of model training iterations by 50%, which provides a new method for the fault detection.

Funds：

河北省自然科学基金资助项目（F2018209201）；唐山市科技局科技计划项目（22130213G）；河北省省属高校基本科研业务费资助项目（JQN2021021）

作者简介：张瑞成（1975-），男，博士，教授 E-mail：rchzhang@126.com

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