在進行產品生命週期測試期間，某些產品具有相當高的可靠度，在此種情形之下，想要取得完整的實驗資料需要耗費相當多的測試時間以及人力，而且所得的資料有可能會不太完整。故設限資料會經常出現在壽命實驗當中，然而實驗有時並無法完全的控制，此時樣本資料則是會符合多重設限資料的型態。
本研究將對於符合韋伯分配之多重設限之部分加速測試資料下，本研究採取最大概似估計法對其參數進行估計。而解聯立方程式則是使用牛頓法，另外本研究也採用EM 演算法，EM演算法則是在完全資料的概似函數架構下進行參數的估計、並且求得參數的信賴區間。最後在模擬測試下，驗證了在最大概似估計法的架構下使用EM演算法會比採用牛頓法來得到更好的結果。

EM algorithm is used to determine maximum likelihood estimates with the data that are multiple censored data. We present maximum likelihood estimation via the expectation-maximization (EM) algorithm to estimate the Weibull parameters and accelerate factor in step-stress partially accelerated life tests under multiple censored data. EM algorithm is used to determine maximum likelihood estimates when the data are multiple censored data. In addition, asymptotic variance and covariance matrix of the estimators are derived by using the complete and missing information matrices, and confidence intervals of the parameters are obtained. A simulation study is conducted to compare the preference of the maximum likelihood estimation via the EM algorithm verses maximum likelihood estimation via Netwom-Raphson (NR) algorithm.

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