A fault detection method for heat loss in a tyre vulcanization workshop using a dynamic energy consumption model and predictive baselines

被引:12
作者
Guo, Jianhua [1 ,2 ]
Yang, Haidong [2 ]
机构
[1] Guangdong Polytech Normal Univ, Dept Comp Sci, Guangzhou, Guangdong, Peoples R China
[2] Guangdong Univ Technol, Dept Mechatron Engn, Guangzhou, Guangdong, Peoples R China
基金
中国国家自然科学基金;
关键词
Fault detection; Heat loss; Tyre vulcanization; Energy consumption model; Thermodynamics; Prediction; LEAKAGE DETECTION; ANOMALY DETECTION; EFFICIENCY; FRAMEWORK; TIRE;
D O I
10.1016/j.applthermaleng.2015.07.064
中图分类号
O414.1 [热力学];
学科分类号
摘要
In a tyre vulcanization workshop (TVWS), the faults of steam traps and insulating layers usually lead to great heat loss and significantly lower energy efficiency. These faults tend to be difficult to detect in practice, and hence often got ignored. This paper presents a fault detection method for heat loss at a workshop level. A dynamic and hierarchical energy consumption model (DHECM) for a TVWS is proposed to establish the expected energy consumption of the vulcanizing process from thermodynamic theory. This model allows the separation of the heat loss and the technical energy consumption from the actual energy usage. The LMBP algorithm and energy consumption factors are adopted to estimate the baselines for the heat loss and help detect the faults. This method is validated in a large TVWS in Guangzhou China. Test results show that the heat loss of the TVWS was reasonably evaluated and it accounted for as much as 44.78% of the energy consumption under its regular operations. The heat loss estimation facilitated better detection performance, and helped identify the faults at a low level. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:711 / 721
页数:11
相关论文
共 36 条
[1]  
Beale M.H., 2013, MATLAB NEURAL NETWOR
[2]   Optimization of machining processes from the perspective of energy consumption: A case study [J].
Bi, Z. M. ;
Wang, Lihui .
JOURNAL OF MANUFACTURING SYSTEMS, 2012, 31 (04) :420-428
[3]  
ChenHuali, 2005, Journal of Tsinghua University (Science and Technology), V45, P119
[4]   Towards energy and resource efficient manufacturing: A processes and systems approach [J].
Duflou, Joost R. ;
Sutherland, John W. ;
Dornfeld, David ;
Herrmann, Christoph ;
Jeswiet, Jack ;
Kara, Sami ;
Hauschild, Michael ;
Kellens, Karel .
CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2012, 61 (02) :587-609
[5]  
Han IS, 1999, J APPL POLYM SCI, V74, P2063, DOI 10.1002/(SICI)1097-4628(19991121)74:8<2063::AID-APP22>3.3.CO
[6]  
2-M
[7]   Internal leakage detection for feedwater heaters in power plants using neural networks [J].
Heo, Gyunyoung ;
Lee, Song Kyu .
EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (05) :5078-5086
[8]  
Hongwei Liu, 2010, Proceedings 2010 Sixth International Conference on Natural Computation (ICNC 2010), P456, DOI 10.1109/ICNC.2010.5583151
[9]   Integrated leakage detection and localization model for gas pipelines based on the acoustic wave method [J].
Jin, Hao ;
Zhang, Laibin ;
Liang, Wei ;
Ding, Qikun .
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES, 2014, 27 :74-88
[10]   Unit process energy consumption models for material removal processes [J].
Kara, S. ;
Li, W. .
CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2011, 60 (01) :37-40