Issue |
Int. J. Metrol. Qual. Eng.
Volume 15, 2024
|
|
---|---|---|
Article Number | 19 | |
Number of page(s) | 14 | |
DOI | https://doi.org/10.1051/ijmqe/2024015 | |
Published online | 04 October 2024 |
Research Article
Reliability with multiple causes of failures: Modeling and practice through a case study on ultrasound probes for medical imaging
1
Department of Statistics Computer Science Applications “G.Parenti”, University of Florence, Viale Morgagni, 59, 50134, Florence, Italy
2
R&D Global Transducer Technology, Esaote Spa, Via di Caciolle,
15, 50127 Florence, Italy
3
Department of Statistics Computer Science Applications “G. Parenti”, University of Florence, Italy
* Corresponding author: rossella.berni@unifi.it
Received:
30
January
2024
Accepted:
14
July
2024
In 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.
Key words: Competing risks / random effects / Weibull models / ultrasound transducers
© R. Berni et al., Published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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