在傳統的可靠度研究中常見的資料為測試物件的壽命或設限時 間。然而大部份的可靠度實驗並沒有考慮到現實生活中的某些變數， 例如系統使用率、系統負荷、使用環境溫度、溼度等條件。隨著科技 的進步，現今我們已可以在許多的產品上安裝感應元件或晶片，如此 我們將可輕易的收集到上述的變數資料。我們在這篇論文中主要考慮 串聯系統的實驗，若系統是有安裝感應元件或晶片則我們可以收集到 這些系統的使用次數和使用率等資訊；反之，若系統沒有安裝感應元 件或晶片，則我們只能觀測到系統的壽命。模型中的最大概似估計量 主要是透過最大期望演算法求得，再透過遺失資訊法則算出觀測資訊 矩陣進一步求得估計量其對應的標準差。透過上述的方法證明了我們 不僅可以得到較準確的估計結果而且比起一般的方法還節省了大量的 運算時間。 In traditional reliability life test, usually the data are the failure (censored) times of the test units. However, most of the analyses do not consider the variabilities in the real life, such as the use-rate, load, temperature, humidity, etc. With new technology, we can install sensors or smart chips in many products so that the variabilities of the above characteristics can be collected. In this thesis, we consider the life test of multi-components series systems, when the cycles-to-failure and use-rate information are available for those systems with censors (chips). On the other hand, only the times-to-failure can be collected from the systems without censors (chips). EM-algorithm is employed to obtain the MLEs along with their estimated standard errors computed based on the observed information matrix via the missing information principle. It turns out that the proposed method not only provides accurate results but also saves much computing time than the existing method.
