2024, Vol. 6, Issue 2, Part A

Author(s): Gbenga Rasheed Ajenifuja and Kayode A Adeparusi

Abstract: High-risk industries such as petrochemical manufacturing, mining, aviation, and nuclear power face escalating reliability and sustainability challenges driven by aging infrastructure, increasing system complexity, and intensifying environmental regulations. Traditional reliability approaches often lack the capacity to integrate failure behaviour, root cause dynamics, probabilistic uncertainties, and environmental interactions within a unified predictive framework. This study proposes a Hybrid Reliability and Sustainability Framework (HRSF) that combines Failure Modes and Effects Analysis (FMEA), Root Cause Analysis (RCA), probabilistic Artificial Intelligence (AI), and environmental factor modelling to optimize decision-making, reduce operational risks, and enhance long-term system resilience. The framework was tested empirically in a cross-industry case study involving 148 critical assets across petrochemical, mineral processing, and energy-generation sectors.
The integrated HRSF uses FMEA for systematic identification of failure modes, RCA for tracing causal pathways, probabilistic AI for dynamic prediction of failure likelihoods under uncertainty, and environmental factor modelling to incorporate temperature, humidity, vibration, corrosion drivers, and emission impacts into reliability equations. Data were collected between 2019 and 2023 from maintenance logs, sensor networks, supervisory systems, environmental monitoring stations, and expert interviews. Results show that the HRSF reduced unplanned downtime by 29.4 percent, improved mean time between failures (MTBF) by 19.7 percent, and achieved a 17.3 percent reduction in sustainability-related non-compliance incidents compared to baseline practices. The probabilistic AI layer improved predictive precision by 24.6 percent relative to conventional statistical models.
This research demonstrates that integrating reliability engineering with sustainability analytics and AI-driven probabilistic reasoning significantly improves operational safety, asset longevity, and environmental compliance. The HRSF provides a replicable and scalable model for high-risk industries seeking to modernize reliability management in alignment with global sustainability and risk-governance standards.

DOI: 10.22271/multi.2024.v6.i2a.861

Pages: 53-62 | Views: 143 | Downloads: 70

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