Methods for Analyzing Lifetime Data

The further development of statistical methods for service life and reliability analysis has historically been an integral part of the research work at the Institute for Machine Elements. The focus of the work extends to the optimization of quantitative estimation methods, the precise integration of prior knowledge, probabilistic approaches, machine learning (ML) using artificial intelligence (AI), and prognostics and health management (PHM) with online analysis of service life data. The overarching goal is always to accurately predict the failure behavior of technical systems in order to improve product reliability.Service life is a random variable—influenced by numerous factors, often with considerable variation. Statistical methods are therefore essential for enabling reliable and quantitative statements about reliability. The central task is to identify descriptive statistics for modeling failure behavior, predicting service life, and comparing products.
Since complete service life measurements of all components are sometimes impractical or not available at all times, the analysis is usually carried out on a sample basis, incompletely or successively. Confidence intervals are therefore crucial for quantifying uncertainties and drawing reliable conclusions about the population. Graphical and analytical methods are used to determine distribution parameters and develop new algorithms to enable automated, data-based online analyses.
The combination of classical statistics, modern algorithms, and data-driven methods results in efficient tools for more precise service life analyses, reliability predictions for optimized product development processes, and ultimately increased reliability of technical systems.