Reliability with multiple causes of failures: Modeling and practice through a case study on ultrasound probes for medical imaging

\nIn this paper, we deal with statistical modeling and a related case study for reliability when multiple failure causes are present. At first, we present in detail two main approaches for competing risk modeling, e.g. the Cox Proportional Hazards model, and the Fine & Gray model. In both models, we consider the inclusion of random effects, a no-trivial issue in this context, especially from the practical point of view. Following, we deal with advanced statistical models to compare the causes of failure, providing extremely useful information for production managers. To perform a useful study for practitioners, statistical modeling is illustrated through an empirical example related to ultrasound probes for medical imaging. The main theory is briefly presented comprehensively, while particular emphasis is given to data structure for model estimation and interpretation of the results, highlighting methodological comparisons and practical differences. Details related to two statistical software are also provided. Furthermore, reliability modeling could be efficiently applied by practitioners and engineers to solve similar technical problems.\n