COX’S REGRESSION MODEL FOR RELIABILITY ANALYSIS AND FAILURE TIME PREDICTION IN ENGINEERING SYSTEMS

Reliability engineering is essential for predicting failure times, optimizing maintenance strategies, and minimizing operational risks in engineering systems. Traditional models, such as Weibull and exponential distributions, provide useful insights into failure rates but struggle to incorporate multiple influencing factors. Cox’s Proportional Hazards Model offers a more flexible approach by integrating covariates such as environmental stress, operational loads, and maintenance history, without assuming a predefined failure time distribution. This study explores the application of Cox’s regression model in reliability analysis, focusing on failure time prediction and system longevity assessment under varying operational conditions. A major advantage of Cox’s model is its ability to handle censored data, a common challenge in reliability studies where some systems remain operational beyond the observation period. By employing the partial likelihood estimation method, the model quantifies the impact of various covariates on failure risks without requiring explicit assumptions about baseline hazard functions. This study utilizes a simulated dataset of 20 industrial components observed over five years to demonstrate the model’s effectiveness in predicting system failures and estimating survival probabilities. The study further evaluates parameter estimation techniques, including maximum likelihood estimation (MLE) and Type I and Type II censoring, to validate the model’s predictive power in practical reliability applications. Additionally, statistical computations confirm that the Cox model provides reliable hazard rate estimations, enabling engineers to develop predictive maintenance schedules and enhance system reliability. This research highlights the importance of survival analysis techniques in reliability engineering. Cox’s model proves to be an effective tool for identifying risk factors and optimizing failure time predictions. The methodology is applicable to diverse engineering domains, including mechanical component wear analysis, structural health monitoring, and software reliability assessment.