Abstract
In manufacturing industry, internal leakage of steam trap usually results in great steam waste. In particular, internal steam leakage in tyre vulcanization workshop has significant influences on its production safety and energy efficiency. In practice, internal steam leakage problem often is ignored and it tends to be difficult to detect this leakage due to lack of comprehensive flow meter or method. This paper presents a collaborative detection method for this problem in tyre vulcanization workshop with artificial immune algorithm. In the method, internal leakage of steam trap is defined as nonself antigens, and steam pressure differentials between steam pipe and steam rooms (or bladders) are extracted as epitopes of antigens. Furthermore, periodic energy efficiency and steam pressure of vulcanizer as the danger signals are simultaneously detected. Energy efficiency is represented by damaged cells which will be identified firstly for locating the leaking steam straps through antibodies. Furthermore, the self-adaptive danger thresholds for energy efficiency are evaluated through a steam consumption model and the Levenberg–Marquardt back propagation algorithm. An immune-based clustering algorithm aiNet is then adopted to generate antibodies (detectors) for detection on the steam pressure of vulcanizer. Finally, a case study is implemented to validate this method, which shows that collaborative detection method allows locating the specific leaking steam trap and is a feasible tool to reduce steam waste and ensure the safety of steam supply.
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References
Aickelin U, Cayzer S (2002) The danger theory and its application to artificial immune systems. In: the 1st international conference on artificial immune Systems, Canterbury, UK, pp 141–148
Beale M, Hagan MT, Demuth HB (2013) MATLAB neural network \({\text{Toolbox}}^{{\rm TM}}\) user’s guide. MathWorks, Natick
Bi ZM, Wang L (2012) Optimization of machining processes from the perspective of energy consumption: a case study. J Manuf Syst 31:420–428. https://doi.org/10.1016/j.jmsy.2012.07.002
Costa Silva G, Palhares RM, Caminhas WM (2012) Immune inspired fault detection and diagnosis: a fuzzy-based approach of the negative selection algorithm and participatory clustering. Expert Syst Appl 39:12474–12485. https://doi.org/10.1016/j.eswa.2012.04.066
Duflou JR, Sutherland JW, Dornfeld D, Herrmann C, Jeswiet J, Kara S, Hauschild M, Kellens K (2012) Towards energy and resource efficient manufacturing: a processes and systems approach. CIRP Ann Manuf Technol 61:587–609. https://doi.org/10.1016/j.cirp.2012.05.002
Escobet A, Nebot À, Cellier FE (2011) Fault diagnosis system based on fuzzy logic: application to a valve actuator benchmark. J Intell Fuzzy Syst 22:155–171
Heo G, Lee SK (2012) Internal leakage detection for feedwater heaters in power plants using neural networks. Expert Syst Appl 39:5078–5086. https://doi.org/10.1016/j.eswa.2011.11.031
Hofmeyr SA, Forrest S (2000) Architecture for an artificial immune system. Evolut Comput 8:443–473. https://doi.org/10.1162/106365600568257
Jin H, Zhang L, Liang W, Ding Q (2014) Integrated leakage detection and localization model for gas pipelines based on the acoustic wave method. J Loss Prev Process Ind 27:74–88. https://doi.org/10.1016/j.jlp.2013.11.006
Li Y, He Y, Wang Y, Yan P, Liu X (2014) A framework for characterising energy consumption of machining manufacturing systems. Int J Prod Res 52:314–325. https://doi.org/10.1080/00207543.2013.813983
Liang W, Zhang L, Xu Q, Yan C (2013) Gas pipeline leakage detection based on acoustic technology. Eng Failure Anal 31:1–7. https://doi.org/10.1016/j.engfailanal.2012.10.020
Ll Jie L, Gao Xinbo G, Jiao Licheng J (2004) A novel clustering method with network structure based on clonal algorithm. In: 2004 IEEE international conference on acoustics, speech, and signal processing. IEEE, pp V–793–6. https://doi.org/10.1109/ICASSP.2004.1327230
Matzinger P (1994) Tolerance, danger, and the extended family. Annu Rev Immunol 12:991–1045. https://doi.org/10.1146/Annurev.Iy.12.040194.005015
May G, Barletta I, Stahl B, Taisch M (2015) Energy management in production: a novel method to develop key performance indicators for improving energy efficiency. Appl Energy 149:46–61. https://doi.org/10.1016/j.apenergy.2015.03.065
Nunes de Casto L, Von Zuben FJ (2000) An evolutionary immune network for data clustering. In: Proceedings of the sixth Brazilian symposium on neural networks, vol 1. IEEE Computer Society, pp 84–89. https://doi.org/10.1109/SBRN.2000.889718
Pandya M, Patel RN, Amarnath SKP (2013) Determination of time delay and rate of temperature change during tyre curing (vulcanizing) cycle. Procedia Eng 51:828–833. https://doi.org/10.1016/j.proeng.2013.01.118
Pfefferkorn FE, Lei S, Jeon Y, Haddad G (2009) A metric for defining the energy efficiency of thermally assisted machining. Int J Mach Tools Manuf 49:357–365. https://doi.org/10.1016/j.ijmachtools.2008.12.009
Qu Z, Feng H, Zeng Z, Zhuge J, Jin S (2010) A SVM-based pipeline leakage detection and pre-warning system. Measurement 43:513–519. https://doi.org/10.1016/j.measurement.2009.12.022
Rahimifard S, Seow Y, Childs T (2010) Minimising embodied product energy to support energy efficient manufacturing. CIRP Ann Manuf Technol 59:25–28. https://doi.org/10.1016/j.cirp.2010.03.048
UE. Steam leakage-cost, detection, estimating and correction [WWW Document], n.d. http://www.uesystems.com/conferences/past-ultrasound-presentations/ultrasonic-leak-detection-of-steam-and-compressed-air-at-industrial-plants
Wang L, Liu Z, Chen CLP, Zhang Y, Lee S (2012) Support vector machine based optimal control for minimizing energy consumption of biped walking motions. Int J Precis Eng Manuf 13:1975–1981. https://doi.org/10.1007/s12541-012-0260-7
Wang Q, Liu F, Li C (2013) An integrated method for assessing the energy efficiency of machining workshop. J Clean Prod 52:122–133. https://doi.org/10.1016/j.jclepro.2013.03.020
Wang Q, Hong KX, Chen XX, Huang H (2014) Design of a vibration-based pipeline leak detection system. Appl Mech Mater 530–531:266–272. https://doi.org/10.4028/www.scientific.net/AMM.530-531.266
Widarsson B, Dotzauer E (2008) Bayesian network-based early-warning for leakage in recovery boilers. Appl Therm Eng 28:754–760. https://doi.org/10.1016/j.applthermaleng.2007.06.016
Yaïci W, Entchev E (2014) Performance prediction of a solar thermal energy system using artificial neural networks. Appl Therm Eng 73(1):1348–1359. https://doi.org/10.1016/j.applthermaleng.2014.07.040
Yan X (2007) A numerical modeling of dynamic curing process of tire by finite element. https://doi.org/10.1295/polymj.PJ2006182
Zhao Y, Wang S, Xiao F (2013) A statistical fault detection and diagnosis method for centrifugal chillers based on exponentially-weighted moving average control charts and support vector regression. Appl Therm Eng 51:560–572. https://doi.org/10.1016/j.applthermaleng.2012.09.030
Zhu L, Zou B, Gao SH, Jiang M, Li ZP (2014) Research on gate valve internal leakage detection based on acoustic emission signal processing. Adv Mater Res 1037:169–173. https://doi.org/10.4028/www.scientific.net/AMR.1037.169
Acknowledgements
The authors would like to thank the information management group of the studied tyre vulcanization workshop to provide the experimental data and convenience of accessing the database system. This work was supported by the National Natural Science Foundation of China (No. 51475096 and No. U1501248), and the Special Funds for Science and Technology of Guangdong Province (Grant No. 2013A011403006).
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Communicated by Cristina Turner.
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Guo, J., Li, H., Yang, H. et al. A collaborative detection approach for internal steam leakage of tyre vulcanization workshop with artificial immune algorithm. Comp. Appl. Math. 37, 4219–4236 (2018). https://doi.org/10.1007/s40314-017-0569-z
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DOI: https://doi.org/10.1007/s40314-017-0569-z