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

Abstract 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.

[1]  Shengwei Wang,et al.  A statistical fault detection and diagnosis method for centrifugal chillers based on exponentially-weighted moving average control charts and support vector regression , 2013 .

[2]  R. L. Earle.  CHAPTER 5 – HEAT-TRANSFER THEORY , 1983 .

[3]  Sami Kara,et al.  Unit process energy consumption models for material removal processes , 2011 .

[4]  Sukhan Lee,et al.  Support vector machine based optimal control for minimizing energy consumption of biped walking motions , 2012 .

[5]  Sung-Ju Kang,et al.  Optimal cure steps for product quality in a tire curing process , 1999 .

[6]  Evgueniy Entchev,et al.  Performance prediction of a solar thermal energy system using artificial neural networks , 2014 .

[7]  Qi Wang,et al.  Design of a Vibration-Based Pipeline Leak Detection System , 2014 .

[8]  Gyunyoung Heo,et al.  Internal leakage detection for feedwater heaters in power plants using neural networks , 2012, Expert Syst. Appl..

[9]  John R. Reisel,et al.  Development and validation of artificial neural network models of the energy demand in the industrial sector of the United States , 2014 .

[10]  Jeremy William Moyer.  Energy Efficiency Improvements for a Large Tire Manufacturing Plant , 2011 .

[11]  Jörg Franke,et al.  E|Benchmark - Approaches and Methods for Assessing the Energy Efficiency of the Industrial Automated Product Manufacturing , 2014 .

[12]  Corinne Reich-Weiser,et al.  Appropriate use of Green Manufacturing Frameworks , 2010 .

[13]  Yufeng Li,et al.  A framework for characterising energy consumption of machining manufacturing systems , 2014 .

[14]  Haidong Yang,et al.  Energy anomaly detection in tire curing by using data integration and forecasting techniques , 2012 .

[15]  Sami Kara,et al.  Towards Energy and Resource Efficient Manufacturing: A Processes and Systems Approach , 2012 .

[16]  Erik Dotzauer,et al.  Bayesian network-based early-warning for leakage in recovery boilers , 2008 .

[17]  Frank E. Pfefferkorn,et al.  A metric for defining the energy efficiency of thermally assisted machining , 2009 .

[18]  Wei Liang,et al.  Gas pipeline leakage detection based on acoustic technology , 2013 .

[19]  Congbo Li,et al.  An integrated method for assessing the energy efficiency of machining workshop , 2013 .

[20]  Shiliang Sun,et al.  A review of optimization methodologies in support vector machines , 2011, Neurocomputing.

[21]  Hongwei Liu,et al.  On the Levenberg-Marquardt training method for feed-forward neural networks , 2010, 2010 Sixth International Conference on Natural Computation.

[22]  Lihui Wang,et al.  Optimization of machining processes from the perspective of energy consumption: A case study , 2012 .

[23]  Liang Zhu,et al.  Research on Gate Valve Internal Leakage Detection Based on Acoustic Emission Signal Processing , 2014 .

[24]  Xin Chen,et al.  Statistical Modeling for Energy Consumption and Anomaly Detection in Rubber Vulcanization Process , 2013 .

[25]  Shahin Rahimifard,et al.  A framework for modelling energy consumption within manufacturing systems , 2011 .

[26]  Shahin Rahimifard,et al.  Minimising Embodied Product Energy to support energy efficient manufacturing , 2010 .

[27]  Wei Liang,et al.  Integrated leakage detection and localization model for gas pipelines based on the acoustic wave method , 2014 .

[28]  Hao Feng,et al.  A SVM-based pipeline leakage detection and pre-warning system , 2010 .

[29]  Ye Hao.  Oil pipeline leak detection and location based on image processing , 2005 .

[30]  Xiangqiao Yan.  A Numerical Modeling of Dynamic Curing Process of Tire by Finite Element , 2007 .

[31]  Rajesh N. Patel,et al.  Determination of Time Delay and Rate of Temperature Change during Tyre Curing (Vulcanizing) Cycle , 2013 .