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

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Application of Machine Learning in Industrial Boilers: Fault Detection, Diagnosis, and Prognosis
ChemBioEng Reviews ( IF 6.2 ) Pub Date : 2021-08-14 , DOI: 10.1002/cben.202100008
Yang Meng _{1,

2} , Xinyun Wu ₃ , Jumoke Oladejo ₄ , Xinyue Dong ₄ , Zhiqian Zhang ₄ , Jie Deng ₄ , Yuxin Yan ₅ , Haitao Zhao ₆ , Edward Lester ₇ , Tao Wu _{1,

5} , Cheng Heng Pang _{3,

4}

Affiliation

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.

中文翻译：

机器学习在工业锅炉中的应用：故障检测、诊断和预测

通过控制运行提高锅炉效率可以节省能源并减少污染物排放。可以通过提高锅炉可用性和降低运行成本来改进诸如定期维护之类的活动。但是，建议维护之间的时间间隔因锅炉而异。故障检测、诊断和预测（FDDP）在工业锅炉中的应用，对于优化运行、故障预警和根本原因识别具有重要作用。本综述讨论了基于机器学习 (ML) 的算法（知识驱动和数据驱动）在 FDDP 中的应用，从而允许识别适合特定应用的技术，从而提高效率、可操作性和安全性。

更新日期：2021-10-08

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