Coal-Fired Boiler Fault Prediction using Artificial Neural Networks

Boiler fault is a critical issue in a coal-fired power plant due to its high temperature and high pressure characteristics. The complexity of boiler design increases the difficulty of fault investigation in a quick moment to avoid long duration shut-down. In this paper, a boiler fault prediction model is proposed using artificial neural network. The key influential parameters analysis is carried out to identify its correlation with the performance of the boiler. The prediction model is developed to achieve the least misclassification rate and mean squared error. Artificial neural network is trained using a set of boiler operational parameters. Subsequenlty, the trained model is used to validate its prediction accuracy against actual fault value from a collected real plant data. With reference to the study and test results, two set of initial weights have been tested to verify the repeatability of the correct prediction. The results show that the artificial neural network implemented is able to provide an average of above 92% prediction rate of accuracy.

[1]  Firas Basim Ismail,et al.  Development and implementation of intelligent condition monitoring system for steam turbine trips , 2016 .

[2]  V. Selladurai,et al.  Predictive modelling and optimization of nitrogen oxides emission in coal power plant using Artificial Neural Network and Simulated Annealing , 2012 .

[3]  Girijesh Prasad,et al.  A neural net model-based multivariable long-range predictive control strategy applied in thermal power plant control , 1997 .

[4]  Raj D. Patel,et al.  A Review Paper on Erosion and Corrosion Behavior of Coal Combustion Chamber , 2014 .

[5]  Mohsen Assadi,et al.  Development of artificial neural network model for a coal-fired boiler using real plant data , 2009 .

[6]  M. Addel-Geliel,et al.  Application of model based fault detection for an industrial boiler , 2012, 2012 20th Mediterranean Conference on Control & Automation (MED).

[7]  Martin A. Riedmiller,et al.  Advanced supervised learning in multi-layer perceptrons — From backpropagation to adaptive learning algorithms , 1994 .

[8]  Jingcheng Wang,et al.  Ash fouling monitoring and key variables analysis for coal fired power plant boiler , 2015 .

[9]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[10]  Inmaculada Arauzo,et al.  Monitoring and prediction of fouling in coal-fired utility boilers using neural networks , 2005 .

[11]  O. Kisi,et al.  Comparison of three back-propagation training algorithms for two case studies , 2005 .

[12]  M. E. Dame,et al.  Backpropagation Neural Network Modeling for Fault Location in Transmission Line 150 kV , 2014 .

[13]  Ping Yang,et al.  Fault diagnosis for boilers in thermal power plant by data mining , 2004, ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004..

[14]  Thomas Palmé,et al.  Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant , 2010 .

[15]  Anna Jankowska,et al.  Early detection and prediction of leaks in fluidized-bed boilers using artificial neural networks , 2015 .

[16]  R. Stephenson.  A and V , 1962, The British journal of ophthalmology.

[17]  Mohsen Assadi,et al.  Fault prediction/diagnosis and sensor validation technique for a steam power plant , 2005 .

[18]  Stelios Timotheou,et al.  A novel weight initialization method for the random neural network , 2009, Neurocomputing.

[19]  Luis M. Romeo,et al.  Neural network for evaluating boiler behaviour , 2006 .

[20]  Akash Saxena,et al.  Fault Detection and Classification in Transmission Line using Wavelet Transform and ANN , 2016 .

[21]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[22]  Wan-Chi Siu,et al.  An independent component analysis based weight initialization method for multilayer perceptrons , 2002, Neurocomputing.

[23]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.