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

被引：8
|
作者：
Meng, Yang [1 ,2 ]
Wu, Xinyun [3 ]
Oladejo, Jumoke [4 ]
Dong, Xinyue [4 ]
Zhang, Zhiqian [4 ]
Deng, Jie [4 ]
Yan, Yuxin [5 ]
Zhao, Haitao [6 ]
Lester, Edward [7 ]
Wu, Tao [1 ,5 ]
Pang, Cheng Heng [3 ,4 ]
机构：
[1] Univ Nottingham Ningbo China, Ningbo New Mat Inst, Ningbo 315042, Peoples R China
[2] Chinese Acad Sci, Ctr Excellence Reg Atmospher Environm, Inst Urban Environm, Xiamen 361021, Peoples R China
[3] Univ Nottingham Ningbo China, Municipal Key Lab Clean Energy Convers Technol, Ningbo 315100, Peoples R China
[4] Univ Nottingham Ningbo China, Dept Chem & Environm Engn, Ningbo 315100, Peoples R China
[5] Univ Nottingham Ningbo China, Key Lab Carbonaceous Wastes Proc & Proc Intensifi, Ningbo 315100, Peoples R China
[6] MIT, Dept Mech Engn, Cambridge, MA 02139 USA
[7] Univ Nottingham, Dept Chem & Environm Engn, Nottingham NG7 2RD, England
来源：
CHEMBIOENG REVIEWS | 2021年 / 8卷 / 05期
关键词：
Diagnosis system; Fault detection; Industrial boiler; Machine learning; Prognostics; PRINCIPAL COMPONENT ANALYSIS; ARTIFICIAL NEURAL-NETWORK; SUPPORT VECTOR MACHINE; EXPERT-SYSTEM; ROTATING MACHINERY; DECISION TREE; METAL-OXIDES; DATA-DRIVEN; ASH FUSION; CHALLENGES;
D O I：
10.1002/cben.202100008
中图分类号：
TQ [化学工业];
学科分类号：
0817 ;
摘要：
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.
引用
收藏
页码：535 / 544
页数：10
相关论文
共 50 条
  • [31] Fault Diagnosis of Induction Machine for Rotor Cage Damage Using MCSA for Industrial Application
    Prasad, Kapu V Sri Ram
    Singh, Varsha
    Electric Power Components and Systems, 2024,
  • [32] Fault detection using machine learning based dynamic ICA-distributed CCA: Application to industrial chemical process
    Ali, Husnain
    Zhang, Zheng
    Safdar, Rizwan
    Rasool, Muhammad Hammad
    Yao, Yuan
    Yao, Le
    Gao, Furong
    DIGITAL CHEMICAL ENGINEERING, 2024, 11
  • [33] A study on the fault diagnosis of rotating machine by machine learning
    Jeon, Hang-Kyu
    Kim, Ji-Sun
    Kim, Bong-Ju
    Kim, Won-Jin
    JOURNAL OF THE ACOUSTICAL SOCIETY OF KOREA, 2020, 39 (04): : 263 - 269
  • [34] Fault diagnosis and prognosis of steer-by-wire system based on finite state machine and extreme learning machine
    Dun Lan
    Ming Yu
    Yunzhi Huang
    Zhaowu Ping
    Jie Zhang
    Neural Computing and Applications, 2022, 34 : 5081 - 5095
  • [35] Fault diagnosis and prognosis of steer-by-wire system based on finite state machine and extreme learning machine
    Lan, Dun
    Yu, Ming
    Huang, Yunzhi
    Ping, Zhaowu
    Zhang, Jie
    NEURAL COMPUTING & APPLICATIONS, 2022, 34 (07): : 5081 - 5095
  • [36] Two-step vibration-based machine learning model for the fault detection and diagnosis in rotating machine and its blind application
    Espinoza-Sepulveda, Natalia
    Sinha, Jyoti
    STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2025, 24 (02): : 1029 - 1042
  • [37] An Automated Machine Learning Approach for Real-Time Fault Detection and Diagnosis
    Leite, Denis
    Martins, Aldonso, Jr.
    Rativa, Diego
    De Oliveira, Joao F. L.
    Maciel, Alexandre M. A.
    SENSORS, 2022, 22 (16)
  • [38] Machine learning based mechanical fault diagnosis and detection methods: a systematic review
    Xin, Yuechuan
    Zhu, Jianuo
    Cai, Mingyang
    Zhao, Pengyan
    Zuo, Quanzhi
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2025, 36 (01)
  • [39] A distributed sensor-fault detection and diagnosis framework using machine learning
    Jan, Sana Ullah
    Lee, Young Doo
    Koo, In Soo
    INFORMATION SCIENCES, 2021, 547 : 777 - 796
  • [40] Semi-Supervised Machine Learning for Fault Detection and Diagnosis of a Rooftop Unit
    Albayati, Mohammed G. G.
    Faraj, Jalal
    Thompson, Amy
    Patil, Prathamesh
    Gorthala, Ravi
    Rajasekaran, Sanguthevar
    BIG DATA MINING AND ANALYTICS, 2023, 6 (02) : 170 - 184
12345