RESEARCH PAPER
Risk and Reliability Analysis of Critical Boiler Components
1
Environment Laboratory, Institute of Mines, Echahid Cheikh Larbi Tebessi University, Algeria
2
Environment Laboratory, Institute of Mines, Echahid Cheikh Larbi Tebessi University, Tebessa 12002, Algeria, Algeria
Submission date: 2024-12-14
Final revision date: 2025-07-28
Acceptance date: 2025-09-29
Online publication date: 2025-10-19
Publication date: 2025-10-19
Corresponding author
Farid Kechroud
Environment Laboratory, Institute of Mines, Echahid Cheikh Larbi Tebessi University, 12000, TEBESSA, Algeria
Environment Laboratory, Institute of Mines, Echahid Cheikh Larbi Tebessi University, 12000, TEBESSA, Algeria
Eksploatacja i Niezawodność – Maintenance and Reliability 2026;28(2):211506
HIGHLIGHTS
- Critical components are identified using the FMEA.
- The high RPN components reflect the action priority.
- Monte Carlo simulation is used to estimate MTTF.
- The MCS predicts the probability of failure.
- MCS optimizes the time of maintenance.
KEYWORDS
reliabilityrisk priority number (RPN)mean time to failure (MTTF)Boilersfailure mode and effects analysis (FMEA)Monte Carlo simulation (MCS)
TOPICS
ABSTRACT
Boilers are of high importance, providing the necessary steam for many industrial processes. Due to their exposure to harsh conditions, they often lead to frequent technical issues, frequent failures, and unplanned downtime, which significantly affects productivity.
This study combines Failure Mode and Effects Analysis(FMEA) and Monte Carlo simulation to enhance reliability and mitigate the risk of failure in critical boiler components. FMEA is used to identify high-risk components, assess failure modes, and prioritize maintenance actions based on the Risk Priority Number (RPN). Monte Carlo simulation complements this approach by modeling failure scenarios and estimating reliability under changing conditions. The analysis identified five critical components, with reliability simulations revealing an average reliability of less than 50%.
Recommendations from this analysis include the implementation of advanced diagnostics, simulation-based inspections, preventive maintenance measures, and comprehensive technician training.
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