A Novel Deep Belief Network Model Constructed by Improved Conditional RBMs and its Application in RUL Prediction for Hydraulic Pumps

IF 0.8 4区 工程技术 Q4 ACOUSTICS International Journal of Acoustics and Vibration Pub Date : 2020-09-30 DOI:10.20855/ijav.2020.25.31669
He Yu, Zai-ke Tian, Hong-ru Li, Baohua Xu, An Guoqing
{"title":"A Novel Deep Belief Network Model Constructed by Improved Conditional RBMs and its Application in RUL Prediction for Hydraulic Pumps","authors":"He Yu, Zai-ke Tian, Hong-ru Li, Baohua Xu, An Guoqing","doi":"10.20855/ijav.2020.25.31669","DOIUrl":null,"url":null,"abstract":"Residual Useful Life (RUL) prediction is a key step of Condition-Based Maintenance (CBM). Deep learning-based techniques have shown wonderful prospects on RUL prediction, although their performances depends on heavy structures and parameter tuning strategies of these deep-learning models. In this paper, we propose a novel Deep Belief Network (DBN) model constructed by improved conditional Restrict Boltzmann Machines (RBMs) and apply it in RUL prediction for hydraulic pumps. DBN is a deep probabilistic digraph neural network that consists of multiple layers of RBMs. Since RBM is an undirected graph model and there is no communication among the nodes of the same layer, the deep feature extraction capability of the original DBN model can hardly ensure the accuracy of modeling continuous data. To address this issue, the DBN model is improved by replacing RBM with the Improved Conditional RBM (ICRBM) that adds timing linkage factors and constraint variables among the nodes of the same layers on the basis of RBM. The proposed model is applied to RUL prediction of hydraulic pumps, and the results show that the prediction model proposed in this paper has higher prediction accuracy compared with traditional DBNs, BP networks, support vector machines and modified DBNs such as DEBN and GC-DBN.","PeriodicalId":49185,"journal":{"name":"International Journal of Acoustics and Vibration","volume":null,"pages":null},"PeriodicalIF":0.8000,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Acoustics and Vibration","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.20855/ijav.2020.25.31669","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ACOUSTICS","Score":null,"Total":0}
引用次数: 5

Abstract

Residual Useful Life (RUL) prediction is a key step of Condition-Based Maintenance (CBM). Deep learning-based techniques have shown wonderful prospects on RUL prediction, although their performances depends on heavy structures and parameter tuning strategies of these deep-learning models. In this paper, we propose a novel Deep Belief Network (DBN) model constructed by improved conditional Restrict Boltzmann Machines (RBMs) and apply it in RUL prediction for hydraulic pumps. DBN is a deep probabilistic digraph neural network that consists of multiple layers of RBMs. Since RBM is an undirected graph model and there is no communication among the nodes of the same layer, the deep feature extraction capability of the original DBN model can hardly ensure the accuracy of modeling continuous data. To address this issue, the DBN model is improved by replacing RBM with the Improved Conditional RBM (ICRBM) that adds timing linkage factors and constraint variables among the nodes of the same layers on the basis of RBM. The proposed model is applied to RUL prediction of hydraulic pumps, and the results show that the prediction model proposed in this paper has higher prediction accuracy compared with traditional DBNs, BP networks, support vector machines and modified DBNs such as DEBN and GC-DBN.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
一种新的基于改进条件RBM的深度置信网络模型及其在液压泵RUL预测中的应用
剩余使用寿命(RUL)预测是基于状态维修(CBM)的关键步骤。尽管基于深度学习的技术的性能依赖于深度学习模型的重结构和参数调优策略,但它们在RUL预测方面显示出了良好的前景。本文提出了一种基于改进条件约束玻尔兹曼机(rbm)的深度信念网络(DBN)模型,并将其应用于液压泵RUL预测。DBN是一种由多层rbm组成的深度概率有向图神经网络。由于RBM是一种无向图模型,同一层的节点之间没有通信,原始DBN模型的深度特征提取能力很难保证连续数据建模的准确性。为了解决这个问题,对DBN模型进行了改进,将RBM替换为改进的条件RBM (improved Conditional RBM, ICRBM),在RBM的基础上在同一层的节点之间增加了时序联动因素和约束变量。将该模型应用于液压泵RUL预测,结果表明,与传统dbn、BP网络、支持向量机以及改进dbn (DEBN、GC-DBN)相比,本文提出的预测模型具有更高的预测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
International Journal of Acoustics and Vibration
International Journal of Acoustics and Vibration ACOUSTICS-ENGINEERING, MECHANICAL
CiteScore
1.60
自引率
10.00%
发文量
0
审稿时长
12 months
期刊介绍: The International Journal of Acoustics and Vibration (IJAV) is the refereed open-access journal of the International Institute of Acoustics and Vibration (IIAV). The IIAV is a non-profit international scientific society founded in 1995. The primary objective of the Institute is to advance the science of acoustics and vibration by creating an international organization that is responsive to the needs of scientists and engineers concerned with acoustics and vibration problems all around the world. Manuscripts of articles, technical notes and letters-to-the-editor should be submitted to the Editor-in-Chief via the on-line submission system. Authors wishing to submit an article need to log in on the IJAV website first. Users logged into the website are able to submit new articles, track the status of their articles already submitted, upload revised articles, responses and/or rebuttals to reviewers, figures, biographies, photographs, copyright transfer agreements, and send comments to the editor. Each time the status of an article submitted changes, the author will also be notified automatically by email. IIAV members (in good standing for at least six months) can publish in IJAV free of charge and their papers will be displayed on-line immediately after they have been edited and laid-out. Non-IIAV members will be required to pay a mandatory Article Processing Charge (APC) of $200 USD if the manuscript is accepted for publication after review. The APC fee allows IIAV to make your research freely available to all readers using the Open Access model. In addition, Non-IIAV members who pay an extra voluntary publication fee (EVPF) of $500 USD will be granted expedited publication in the IJAV Journal and their papers can be displayed on the Internet after acceptance. If the $200 USD (APC) publication fee is not honored, papers will not be published. Authors who do not pay the voluntary fixed fee of $500 USD will have their papers published but there may be a considerable delay. The English text of the papers must be of high quality. If the text submitted is of low quality the manuscript will be more than likely rejected. For authors whose first language is not English, we recommend having their manuscripts reviewed and edited prior to submission by a native English speaker with scientific expertise. There are many commercial editing services which can provide this service at a cost to the authors.
期刊最新文献
Surge Motion Passive Control of TLP with Double Horizontal Tuned Mass Dampers Numerical and Experimental Evaluation of Hydrodynamic Bearings Applied to a Jeffcott Test Bench Experimental and Numerical Investigation on the Flow-Induced Interior Noise Based on Pellicular Analysis Application of Statistical Energy Analysis (SEA) in Estimating Acoustic Response of Panels With Non-Uniform Mass Distribution Railways: An Acoustical Point of View
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1