This study focuses on a critical evaluation of fatigue and reliability in used equipment, highlighting their significance as separate yet interconnected factors influencing industrial performance and quality assurance. Reliability is traditionally defined as the ability of equipment to perform consistently over time, whereas fatigue refers to the progressive structural degradation due to repeated operational stresses. Understanding the interplay between these two concepts is crucial for industries that rely on equipment with extended service periods. This research aims to bridge the gap between fatigue and reliability by defining each concept independently, identifying points of connection, and exploring their combined impact on equipment performance through statistical inference. To achieve this, the study develops a methodological framework using statistical inference techniques, including survival analysis and probabilistic modeling, to assess and predict the reliability and fatigue conditions of used machinery. The research collected extensive data on equipment performance and degradation, formulated hypotheses, and applied these statistical methods to validate the relationship between fatigue and reliability. A case study was conducted to test these hypotheses, simulating real-world conditions where equipment undergoes recurrent stress, demonstrating how statistical inference can accurately predict the onset of fatigue and its effects on reliability. The findings of this study confirm that statistical inference provides a powerful approach to predicting reliability and fatigue conditions, enabling industries to transition from reactive to proactive maintenance strategies. By incorporating statistical models, the research illustrates how industries can anticipate equipment failures, optimize maintenance schedules, and extend the lifespan of machinery, ultimately enhancing operational efficiency and reducing costs. The study emphasizes that reliability should be viewed not merely as a current performance indicator but as a predictive tool influenced significantly by fatigue. This approach provides a deeper understanding of the reliability life cycle, promoting better decision-making and strategic planning in equipment management.
Published Date: 2025-02-19; Received Date: 2024-10-24
