為了更有效的利用製程故障的數據，監督式學習法(Supervised learning techniques)近年來已被廣泛的研究且應用於製程監測(Process monitoring)上，然而，要良好的使用現有的監督式學習法監測製程，在訓練數據庫中需要包含製程正常操作數據以及所有的製程故障數據。對於真實的工業製程，這個條件通常是很嚴苛的，因為有一些故障並不是時常發生，要收集到此類故障數據較為困難，而且隨著產線設備使用時間增加，機器不免會有老化現象，會出現從沒發生過的未知故障，當這些故障發生時，數據庫中沒有這些故障訊息，導致現存方法無法良好的監測出未知故障。另外，現有監督式學習法用於製程監測上大部分都含有一個強烈的假設，其假設每類別需符合高斯分佈(Gaussian distribution)，而在真實工業生產中，製程數據往往無法符合此項假設，導致無法很好的處理非高斯分佈數據。
本文結合混和判別分析法(Mixture discriminant analysis, MDA)及統計製程控制(Statistical process control, SPC)的概念，發展出一套混和判別監測法(Mixture discriminant monitoring, MDM)，對於批式製程獨有的三維數據特性亦發展出自適應批式混和判別監測法(Adaptive batch MDM, ABMDM)，同時解決上述兩個問題，於多模態連續製程及多階段批式製程中的製程監測更凸顯其適性。針對監測出已知故障，藉由工程師或操作員對製程的了解可及時的做出故障診斷(Diagnosis)，若是監測出未知故障，使用重構方法(Reconstruction-based)來進行故障判定(Isolation)的工作，進而找出故障發生的原因最有可能是由哪一個製程變數造成。
最後，以田納西伊士曼製程(Tennessee Eastman process)及塑膠射出成型製程的監測為例，驗證此方法的有效性。

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