###### Abstract  
This study focuses on the reliability assessment of a series system composed of Weibull-distributed components. Because high-reliability components rarely fail during life testing or actual operation, conventional system reliability analysis methods based on failure time data do not work well. This paper presents a practical approach to address this issue, with a major interest in inferring the lower confidence limits of system reliability and reliable life. The proposed system reliability assessment method utilizes the minimum lifetime distribution theory to derive the closed-form confidence limits for system reliability indexes from Weibull zero-failure data. Furthermore, a system reliability update procedure is introduced, integrating life data at both the component and system levels. Monte Carlo simulations demonstrate that the proposed approach is more accurate than conventional methods. Finally, an engineering example of reliability assessment and life prediction for a satellite infrared Earth sensor is presented to illustrate the advantages and applications of the proposed method. Weibull distribution; series system; zero-failure; reliability assessment; confidence limit 22 January 202527 February 202527 March 20 the assessment accuracy [8; 9]. Moreover, the MML method fails to consider the impact of zero-failure components and often gives overly optimistic estimates [10]. The MCS method is particularly suitable when directly constructing an analytical model of system reliability is difficult [11]. [PERSON] et al. [12] investigated a system reliability evaluation algorithm based on MCS and applied it to assess the reliability of grinding machines. Similarly, [PERSON] et al. [13] used MCS to determine the confidence limit of reliability for a power distribution system, assuming that component life follows an exponential or Weibull distribution. The key to the MCS method is determining the fiducial distributions of component reliability, achieved through the generalized pivot quantity method [14]. However, it is almost impossible to accurately construct the fiducial distributions when dealing with zero-failure data. In other words, the MCS method cannot handle situations with zero-failure components in a series system. The Bayesian approach provides an effective means of combining prior information on component lifetime distribution parameters with sample data for system reliability analysis. [PERSON] et al. [15] developed a hybrid model that combines the Bayesian method with the variance propagation method to estimate the confidence intervals of zero-failure systems, but this approach is limited to binomial data. The Bayesian model proposed by [PERSON] et al. [16] can naturally integrate life data from different layers of the system, including various types of censored life data. Nevertheless, in the Bayesian framework, the selection of prior distribution and its hyperparameters is subjective. The additional uncertainty introduced by component priors may significantly impact the stability of system reliability estimates [17; 18]. Existing studies typically assume that sufficient failure time data can be obtained from component-level testing. However, with advancements in industrial manufacturing, many products have become increasingly reliable. Failures of high-quality products are rare during limited testing time, even under accelerated test scenarios [19]. Moreover, to minimize test time and save test costs, reliability engineers often opt for zero-failure test programs. Consequently, zero-failure data are frequently observed or collected [20]. Without failure time information, conventional reliability analysis methods tend to be inadequate, even ineffective. In the industry, the exponential and 2-parameter Weibull distributions are the two most widely and successfully applied lifetime distribution models. Notably, the 2-parameter Weibull distribution simplifies to the exponential distribution when its shape parameter is set to one.