1Plant Life Assessment, Universität Stuttgart, Germany
2Department of Environmental Engineering, University of Novi Sad, Serbia
*Corresponding author:Daniel Baloš, Plant Life Assessment, Materialprüfungsanstalt (MPA), Universität Stuttgart, Stuttgart, Germany
Submission: December 19, 2024;Published: January 07, 2025
ISSN: 2577-2007Volume5 Issue3
Reliability engineering focuses on ensuring that systems and components perform their intended function under stated conditions for a specified period. Central to this field are statistical methods that transform observed lifetime data into insights about product durability, failure rates, and time-tofailure distributions. This report examines fundamental probability distributions commonly employed in reliability analysis-normal, exponential, rayleigh, and weibull and outlines the basic principles behind parameter estimation. Techniques discussed include linear regression (for specific transformations that facilitate parameter extraction), the maximum likelihood method, and the method of moments. By applying these methods to experimental data from component life tests, engineers can derive statistically sound estimates of distribution parameters, thereby enabling the prediction of failure behavior, the establishment of maintenance schedules, and improvements in product design. In addition to theoretical descriptions, this report emphasizes the importance of practical, applicable formulas that can be directly implemented in programming code or spreadsheet programs. These formulas provide a direct path for engineers to perform reliability and maintenance analysis efficiently. By leveraging such tools, engineers can streamline the process of parameter estimation, making it accessible and actionable even for those with limited statistical backgrounds. The focus is placed on the practical application of these methods to enhance maintenance schedules and system reliability. Parameter estimation techniques enable the precise prediction of failure behaviors, allowing for optimized maintenance interventions and prolonged system uptime. This approach improves operational reliability and contributes to costeffective maintenance strategies by minimizing unexpected downtimes and extending the useful life of components. Through detailed examples and step-by-step guides, this report demonstrates how to derive and apply these formulas, ensuring that the methodologies are understood and readily implementable. Ultimately, this integration of statistical rigor with practical application supports the goal of translating experimental data into actionable insights for reliability engineering.
Keywords:Reliability engineering; Parameter estimation; Normal distribution; Weibull distribution; Rayleigh distribution; Exponential distribution; Linear regression; Maximum likelihood; Method of moments
Abbreviations:MLE: Maximum Likelihood Estimation; PDF: Probability Density Function; CDF: Cumulative Distribution Function; OLS: Ordinary Least Squares; CI: Confidence Interval; MTBF: Mean Time Between Failures; MTTF: Mean Time To Failure