Coal-fired boiler fault prediction using artificial neural networks

Nong Nurnie Mohd Nistah, King Hann Lim, Lenin Gopal, Firas Basim Ismail Alnaimi

Research output: Contribution to journalArticle

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2486-2493
Number of pages8
JournalInternational Journal of Electrical and Computer Engineering
Issue number4
Publication statusPublished - Aug 2018


All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Electrical and Electronic Engineering

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