Application of Machine Learning in Industrial Boilers: Fault Detection, Diagnosis, and Prognosis

Yang Meng, Xinyun Wu, Jumoke Oladejo, Xinyue Dong, Zhiqian Zhang, Jie Deng, Yuxin Yan, Haitao Zhao, Edward Lester, Tao Wu, Cheng Heng Pang

Research output: Journal PublicationArticlepeer-review

8 Citations (Scopus)


Enhancement in boiler efficiency via controlled operation could lead to energy savings and reduction in pollutant emission. Activities such as scheduled maintenance could be improved by increasing boiler availability and reducing running costs. However, the time interval between recommended maintenance is varied depending on boilers. The application of fault detection, diagnosis and prognosis (FDDP) in industrial boilers plays an important role in optimizing operation, early-warning of faults, and identification of root causes. This review discusses the application of machine learning (ML)-based algorithms (knowledge-driven and data-driven) for FDDP, thus allowing the identification of fit-for-purpose techniques for specific applications leading to improved efficiency, operability, and safety.

Original languageEnglish
Pages (from-to)535-544
Number of pages10
JournalChemBioEng Reviews
Issue number5
Publication statusPublished - Oct 2021


  • Diagnosis system
  • Fault detection
  • Industrial boiler
  • Machine learning
  • Prognostics

ASJC Scopus subject areas

  • Bioengineering
  • Chemical Engineering (miscellaneous)
  • Biochemistry
  • Process Chemistry and Technology
  • Filtration and Separation
  • Industrial and Manufacturing Engineering


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