International Journal of Multidisciplinary Research and Growth Evaluation

Advances in Predictive Maintenance of Industrial Boilers Using Condition Monitoring and Thermal Efficiency Analytics

Paul Chibuike Obetta¹, Nwakamma Ninduwezuor-Ehiobu², Henry Chukwuemeka Olisakwe³

1. ¹
2. ²
3. ³

Abstract

Industrial boilers are capital-intensive assets whose unplanned outages carry severe consequences for production continuity, energy cost, and personnel safety. Traditional maintenance regimes, based either on running equipment to failure or on fixed calendar intervals, are increasingly unable to balance reliability against cost in modern process industries. This paper reviews recent advances, up to 2022, in predictive maintenance of industrial boilers achieved through the integration of condition monitoring technologies and thermal efficiency analytics. It examines the principal sensing modalities applied to boiler systems, including vibration analysis, infrared thermography, ultrasonic thickness measurement, acoustic emission, flue gas analysis, online water chemistry monitoring, and emerging aerial and geospatial inspection methods, and discusses how efficiency indicators derived from heat loss accounting expose degradation mechanisms such as fouling, slagging, scaling, and heat exchanger deterioration before they manifest as failures. The paper then surveys data-driven frameworks, including machine learning classifiers, statistical prognostic models for remaining useful life estimation, and digital twin architectures, that convert monitored data into actionable maintenance decisions. Because predictive maintenance programs succeed or fail within a wider organizational system, the review also draws on the adjacent literatures of data infrastructure and governance, cybersecurity of operational technology, occupational safety management, maintenance supply chain and spare parts logistics, economic evaluation and decision support, and the energy transition context in which boiler assets now operate. Reported industrial outcomes indicate meaningful reductions in unplanned downtime and fuel consumption, alongside improved inspection targeting. Persistent challenges include sensor reliability in harsh combustion environments, scarcity of labeled failure data, integration with legacy control systems, and organizational readiness.

Keywords: predictive maintenanceindustrial boilerscondition monitoringthermal efficiencymachine learningremaining useful lifedigital twinmaintenance supply chainoperational technology security

How to cite

Licensed under CC BY 4.0.