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This paper presents the operation status and results of ground thermal cycling test of pulse tube refrigerators (PTRs) for space application. Firstly, a thermal cycling degradation model was proposed by considering two physical mechanisms: contamination and fatigue damage. Then, a thermal cycling test scheme of two types of PTRs was designed and performed to demonstrate their long lifetime and high thermal stability. Two type A PTRs with cooling capacity of 1W@60 K and two type B PTRs with cooling capacity of 5W@80 K were continuously operated for about two years in a simulated vacuum thermal cycling environment. Effects of heat rejection temperature variation on thermal stability and dynamic performance of the PTRs were investigated. Furthermore, the thermal cycling degradation model was validated with the actual thermal cycling test data. Finally, the predicted pseudo-failure lifetime was acquired via experimental data and degradation model. Moreover, the estimated reliability of PTRs was obtained through using the Weibull distribution. The proposed thermal cycling test scheme and innovative lifetime prediction and reliability estimation method provide a quick and accurate approach for the cooler manufacturer to assess the lifetime and reliability of the space PTRs.

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According to the present works, it is more efficient by carrying out accelerated degradation test (ADT) with step-down stress than constant and step-up stress. However, maximum likelihood estimation (MLE) is currently the main analysis method for step-down stress accelerated degradation test (SDSADT), which is difficult to be applied into small sample size occasion. To solve this problem, the paper proposes a Bayesian analysis method for SDSADT based on Wiener process. Firstly, degradation path of product performance is described by Wiener process, and then data analysis of SDSADT is carried out by a segmented Bayesian method which can update the evaluation values of model parameters under different accelerated stresses. Lastly, recursive algorithm for parameters estimation is established for reliability assessment of SDSADT. Simulation studies show that the method is more accuracy by comparing with MLE in case of small sample size.