A Hybrid Intelligent Fault Diagnosis Strategy for Chemical Processes Based on Penalty Iterative Optimization

Yuman Yao,Wenjia Luo,Jiaxin Zhang,Yiyang Dai

doi:10.3390/pr9081266

Yuman Yao, Wenjia Luo + Show 2 more

Open Access

https://doi.org/10.3390/pr9081266

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Abstract

Process fault is one of the main reasons that a system may appear unreliable, and it affects the safety of a system. The existence of different degrees of noise in the industry also makes it difficult to extract the effective features of the data for the fault diagnosis method based on deep learning. In order to solve the above problems, this paper improves the deep belief network (DBN) and iterates the optimal penalty term by introducing a penalty factor, avoiding the local optimal situation of a DBN and improving the accuracy of fault diagnosis in order to minimize the impact of noise while improving fault diagnosis and process safety. Using the adaptive noise reduction capability of an adaptive lifting wavelet (ALW), a practical chemical process fault diagnosis model (ALW-DBN) is finally proposed. Then, according to the Tennessee–Eastman (TE) benchmark test process, the ALW-DBN model is compared with other methods, showing that the fault diagnosis performance of the enhanced DBN combined with adaptive wavelet denoising has been significantly improved. In addition, the ALW-DBN shows better performance under the influence of different noise levels in the acid gas absorption process, which proves its high adaptability to different noise levels.

Highlights

To test the adaptability and accuracy of the enhanced deep belief network (DBN) and adaptive lifting wavelet (ALW)-DBN in situations with different amplitudes of noise, we used HYSYS to perform a dynamic simulation of the acid gas absorption process
To more accurately extract the fault characteristics and eliminate different noise levels, an ALW-DBN model based on ALW noise reduction and an enhanced deep confidence network were proposed
By using the data after adaptive wavelet denoising as the input data of the enhanced DBN, the two optimization algorithms were combined to form a complete ALW-DBN fault diagnosis model

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. With the advancement of industrial intelligence, modern industry has higher requirements for system reliability and safety, which enables the rapid development of real-time risk management methods for efficiently detecting faults that threaten system reliability and eliminating uncertain noise affecting system safety. The fault detection and diagnosis (FDD) methods in the risk method play a central role [1]. Research on FDD technology began in 1971 [2], and it is under rapid development

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