Reliability analysis of computed tomography equipment using the q‐Weibull distribution

Abstract

Inspired by the successful application of the q‐Weibull distribution in other research fields, we took the lead to use it in the field of medical devices in this work. The parameter estimation of the q‐Weibull distribution was performed using the probability plot method. The CT failure data from Nanfang Hospital in Guangzhou, China, were used to study the reliability of CT equipment at two levels: the CT system and its seven main components. In terms of evaluation accuracy, the mean squared error, Akaike's information criterion, and the determination coefficient were used to compare the accuracy of fitting of different distribution models. The results show that the accuracy of fitting the q‐Weibull distribution is higher than that of the Weibull distribution in terms of determination coefficient and mean squared error. When considering the complexity of the model, the fit accuracy of the Weibull distribution is better. The results were analyzed using reliability and failure rate plots. The q‐Weibull distribution gives a good fit for the failure data of the CT system and components. Though the Weibull distribution fits better in a few cases, the q‐Weibull distribution can describe the entire “bathtub curve” with only a set of parameters. The findings of this study can be extended to other medical devices.

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“…However, above the visual analysis of the failure rate function, the shape parameter, β, is decisive in the analysis and enables the identification of failure type. Depending on the value of β, the failure rate, λ, evolves differently and can be associated with one of the phases of the bathtub curve [ 50 ], typical for engineering systems [ 51 ]: If β < 1, the failure rate is decreasing, typical of the “burn-in” period. This kind of failure rate indicates an “early failure” and is typically associated with systems with manufacturing defects; If β = 1, the failure rate is constant, typical of the “useful life” period.…”

“…However, above the visual analysis of the failure rate function, the shape parameter, β, is decisive in the analysis and enables the identification of failure type. Depending on the value of β, the failure rate, λ, evolves differently and can be associated with one of the phases of the bathtub curve [50], typical for engineering systems [51]:…”