Reliability analysis for aircraft systems : a case study

Abstract

Ensuring the reliability of aircraft systems is paramount in aviation for safety, efficiency, and economic viability. This thesis investigates statistical methodologies used for reliability analysis, focusing on the application of advanced statistical models to enhance the assessment of aircraft system reliability. A novel, model-based approach integrating real-time data analytics was developed to predict potential system failures, thereby refining maintenance schedules and improving aircraft uptime. The study employs a comprehensive review of existing reliability models, critiques their limitations, and proposes an innovative approach that uses Extreme Value Theory (EVT) and other statistical tools to address these challenges. Data collected from a fleet of commercial aircraft over a four-year period provided a robust dataset for analysis, enabling the identification of failure patterns and the prediction of future maintenance needs with greater accuracy. A detailed reliability analysis was conducted on critical systems such as air condition, communication, flight control, fuel, landing gear, and light under various operational conditions. The research revealed that traditional models, which often rely on unrealistic assumptions and manual adjustments, fall short of accurately reflecting the complex realities of modern aviation operations. In response, the proposed model incorporates dynamic data inputs and environmental variables, offering a more nuanced understanding of system behaviors. The results indicate that the approach significantly enhances the prediction of failure probabilities and maintenance demands, facilitating proactive maintenance strategies that align with real-world conditions. This leads to reduced downtime and maintenance costs, while improving safety and reliability. This thesis contributes to the advancement of reliability engineering in aviation by providing a scientifically rigorous, data-driven methodology for the assessment and management of aircraft system reliability. By integrating contemporary statistical methods with real-time data analytics, it sets a new standard for predictive maintenance in the aviation industry, promising enhanced operational efficiency and a reduction in unscheduled aircraft maintenance.