Condition monitoring and reliability assessment, an essential tool for Boiler Plant Maintenance - A review

Condition monitoring is an essential technique which is usually deployed to monitor the health parameters and conditions of a boiler plant in such a way that variations or significant changes can like failure can be identified and proffer adequate solutions to it. It is an important aspect of condition-based maintenance which is used in maintaining an equipment according to its conditions. Thus, this study focused on the fundamental principles of condition monitoring which involve the identification and selection of a physical measurement that will show the deterioration stage and the importance of taking the readings at intervals. More so, the study established that monitoring and measurement should focus more at critical components that have high frequency of failure. Thus, condition-based maintenance of boilers will help in improving the ,availability of the boiler.

[1]  Madhurima Ghosh Effect of flue gas constituents on boiler tube failure of a captive power plant , 2023, Engineering Failure Analysis.

[2]  M. A. El‐Bindary,et al.  Spectrophotometric and fluorometric methods for effective monitoring of rust state in boiler systems and Fe2+ ion in pharmaceutical samples , 2023, Journal of Molecular Liquids.

[3]  Jide Niu,et al.  Experimental study on performance assessments of HVAC cross-domain fault diagnosis methods oriented to incomplete data problems , 2023, Building and Environment.

[4]  Michael E. Cholette,et al.  Degradation Modelling and Lifetime Assessment for Boiler Waterwall with Incomplete Inspection Data , 2023, Process Safety and Environmental Protection.

[5]  Dilbagh Panchal,et al.  Fuzzy decision support system for sustainable operational performance optimization for boiler unit in milk process industry , 2023, Appl. Soft Comput..

[6]  M. Yulianto,et al.  Thermal Characteristics of Coconut Shells as Boiler Fuel , 2022, International Journal of Renewable Energy Development.

[7]  F. Meng,et al.  Discriminability Analysis of Characterization Parameters in Micro-Leakage of Turbocharged Boiler’s Evaporation Tube , 2022, Energies.

[8]  Zhiwu Liang,et al.  Self-optimizing control and safety assessment to achieve economic and safe operation for oxy-fuel combustion boiler island systems , 2022, Applied Energy.

[9]  G. Gong,et al.  Cross temporal-spatial transferability investigation of deep reinforcement learning control strategy in the building HVAC system level , 2022, Energy.

[10]  O. Ighodaro,et al.  Failure Investigation of the Tube of a Dual Fired Steam Boiler in a Western Nigerian Food and Beverage Manufacturing Plant , 2022, Engineering Failure Analysis.

[11]  S. Grądziel,et al.  Mathematical model of a power boiler operation under rapid thermal load changes , 2022, Energy.

[12]  Q. Zhang,et al.  A study on transfer learning in enhancing performance of building energy system fault diagnosis with extremely limited labeled data , 2022, Building and Environment.

[13]  Aaron S. Yeardley,et al.  Integrating machine learning techniques into optimal maintenance scheduling , 2022, Comput. Chem. Eng..

[14]  Yupeng Wu,et al.  Assessment of HVAC system operational fault impacts and multiple faults interactions under climate change , 2022, Energy.

[15]  S. Ali,et al.  Modelling the causes of boiler accidents: implications for economic and social sustainability at the workplace , 2022, Heliyon.

[16]  S. Bathrinath,et al.  Smart maintenance management approach: Critical review of present practices and future trends in SMEs 4.0 , 2022, Materials Today: Proceedings.

[17]  Michael E. Cholette,et al.  Optimal inspections and maintenance planning for anti-corrosion coating failure on ships using non-homogeneous Poisson Processes , 2021, Ocean Engineering.

[18]  Ronald W. Breault,et al.  Data analytics for leak detection in a subcritical boiler , 2021 .

[19]  Shuilong He,et al.  Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis. , 2021, ISA transactions.

[20]  Shuilong He,et al.  Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions. , 2021, ISA transactions.

[21]  Georgios Boustras,et al.  Safety and risk analysis in digitalized process operations warning of possible deviating conditions in the process environment , 2021 .

[22]  Natarianto Indrawan,et al.  Leak detection in a subcritical boiler , 2020 .

[23]  Giulio Lorenzini,et al.  Markov-based performance evaluation and availability optimization of the boiler–furnace system in coal-fired thermal power plant using PSO , 2020, Energy Reports.

[24]  Xiaoyan Li Design of energy-conservation and emission-reduction plans of China’s industry: Evidence from three typical industries , 2020 .

[25]  Christos T. Maravelias,et al.  Predictive maintenance scheduling optimization of building heating, ventilation, and air conditioning systems , 2020 .

[26]  V. Vijay,et al.  Advances in biogas valorization and utilization systems: A comprehensive review , 2020 .

[27]  Ruqiang Yan,et al.  Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study , 2020, ISA transactions.

[28]  John D. Lee,et al.  Improving process safety: What roles for Digitalization and Industry 4.0? , 2019 .

[29]  Richard Clark,et al.  The Future of Coal-Fired Power Generation in Southeast Asia , 2019 .

[30]  Marcin Trojan,et al.  Modeling of a steam boiler operation using the boiler nonlinear mathematical model , 2019, Energy.

[31]  Lin Ma,et al.  Degradation modeling and condition-based maintenance of boiler heat exchangers using gamma processes , 2019, Reliab. Eng. Syst. Saf..

[32]  Mohd Azlan Hussain,et al.  A review of data-driven fault detection and diagnosis methods: applications in chemical process systems , 2019 .

[33]  Mehdi Behzad,et al.  Improving sustainability performance of heating facilities in a central boiler room by condition-based maintenance , 2019, Journal of Cleaner Production.

[34]  Veysi Başhan,et al.  Application of Fuzzy Dematel Technique to Assess Most Common Critical Operational Faults of Marine Boilers , 2018 .

[35]  Karel Macek,et al.  Model−based predictive maintenance in building automation systems with user discomfort , 2017 .

[36]  Karel Macek,et al.  Long-term predictive maintenance: A study of optimal cleaning of biomass boilers , 2017 .

[37]  Prabir Basu,et al.  A review of some operation and maintenance issues of CFBC boilers , 2016 .

[38]  S. Mekhilef,et al.  A review on biomass as a fuel for boilers , 2011 .

[39]  A. A. Ali,et al.  Iterative technique and finite element simulation for supplemental condition monitoring of water-tube boiler , 2009, Simul. Model. Pract. Theory.

[40]  E. Ikonen,et al.  Fouling monitoring in a circulating fluidized bed boiler using direct and indirect model-based analytics , 2023, Fuel.

[41]  J. Jia,et al.  A hybrid prediction approach for enhancing heat transfer efficiency of coal-fired power plant boiler , 2023, Energy Reports.

[42]  A. Shokri,et al.  Principles, operational challenges, and perspectives in boiler feedwater treatment process , 2023, Environmental Advances.

[43]  Mei Yang,et al.  Performance analysis of an efficient waste heat utilization system in an ultra-supercritical coal-fired power plant , 2022, Energy Reports.

[44]  P. Knutsson,et al.  Monitoring of Bed Material in a Biomass Fluidized Bed Boiler , 2022, SSRN Electronic Journal.

[45]  Xiaomin Xu,et al.  Research on Factors Affecting Boiler Feedwater Quality and Its Improvement , 2022, Open Journal of Applied Sciences.

[46]  Y. Vladov,et al.  Statistical analysis of the steam boiler elements’ maintenance results , 2022, IFAC-PapersOnLine.

[47]  Draguna Vrabie,et al.  Modelica-based system modeling for studying control-related faults in chiller plants and boiler plants serving large office buildings , 2021 .

[48]  S. Djayanti Energy Efficiency Improvement Strategies for Boilers: A Case Study in Pharmacy Industry , 2019, E3S Web of Conferences.

[49]  Armand Baboli,et al.  Dynamic Predictive Maintenance in industry 4.0 based on real time information: Case study in automotive industries , 2019, IFAC-PapersOnLine.

[50]  Giles R. Scuderi,et al.  A Comprehensive Review , 2017 .