Improved DBO-VMD and optimized DBN-ELM based fault diagnosis for control valve

Author:

Zhang Dengfeng,Zhang ChiORCID,Han Xiaodong,Wang CunsongORCID

Abstract

Abstract Control valves play a vital role in process production. In practical applications, control valves are prone to blockage and leakage faults. At the small control valve openings, the vibration signals exhibit the drawbacks of significant interference and weak fault characteristics, which causes subpar fault diagnosis performance. To address the issue, a diagnostic model based on optimized variational mode decomposition (VMD) and improved deep belief network-extreme learning machine (DBN-ELM) is proposed. Firstly, good point set population initialization, nonlinear convergence factor, and adaptive Gaussian–Cauchy mutation strategies are applied in the dung beetle optimization algorithm (DBO) to escape local optima. Then, the improved DBO (IDBO) is used to optimize VMD parameters to obtain a series of modal components. Next, the generalized dispersion entropy (GDE) is formed by the combination of generalized Gaussian distribution and refined composite multiscale fluctuation-based dispersion entropy. The maximum correlation coefficient modal components are applied to extract GDE. Finally, the IDBO is applied to optimize the parameters of the DBN-ELM network to improve the classification performance of control valve faults. The comparative experiment results demonstrate that the proposed model can extract effective features and the diagnostic accuracy reaches 99.87%.

Funder

Key R&D Program of China

National Natural Science Foundation of China

Publisher

IOP Publishing

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3