A Wavelet Transform-Assisted Convolutional Neural Network Multi-Model Framework for Monitoring Large-Scale Fluorochemical Engineering Processes

Processes

Feng Xue¹  Kai Song¹  Kun Zhou¹  Xu Chen¹  Xintong Li [M]¹  Zhiqiang Ge²  Zhibing Chen³ 

[1] School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China;State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, Zhejiang, China;Zhejiang Juhua Co., Ltd. Fluor-polymeric Plant, Quzhou 324004 Zhejiang, China;

关键词: fluorochemical engineering processes;    convolutional neural network (CNN);    wavelet transform;    fault detection and diagnosis (FDD);    deep learning;   

DOI  :  10.3390/pr8111480

来源: DOAJ

【 摘 要 】

The barely satisfactory monitoring situation of the hypertoxic fluorochemical engineering processes requires the application of advanced strategies. In order to deal with the non-linear mechanism of the processes and the highly complicated correlation among variables, a wavelet transform-assisted convolutional neural network (CNN) based multi-model dynamic monitoring method was proposed. A preliminary CNN model was first trained to detect faults and to diagnose part of them with minimum computational burden and time delay. Then, a wavelet assisted secondary CNN model was trained to diagnose the remaining faults with the highest possible accuracy. In this step, benefitting from the scale decomposition capabilities of the wavelet transform function, the inherent noise and redundant information could be filtered out and the useful signal was transformed into a higher compact space. In this space, a well-designed secondary CNN model was trained to further improve the fault diagnosis performance. The application on a refrigerant-producing process located in East China showed that not only regular faults but also hard to diagnose faults were successfully detected and diagnosed. More importantly, the unique online queue assembly updating strategy proposed remarkably reduced the inherent time delay of the deep-learning methods. Additionally, the application of it on the widely used Tennessee Eastman process benchmark strongly proved the superiority of it in fault detection and diagnosis over other deep-learning methods.

【 授权许可】

Unknown