一直以來工程師於製造過程分析時，透過統計製程管制(Statistical Process Control, SPC)進行監控，在量測結果分析前所得到的數據來源必須是可信的，隨著量測設備越來越精密的情況下，其設備之適用性及功能性亦成為企業選購時所參考之要件；本研究將以自行車鏈條製造廠之一量測機台為例，透過量測系統分析(Measurement System Analysis, MSA)與SPC之歷史資料帶入貝氏分析(Bayesian Analysis)方法，估算量測系統造成異常的機率，以有效達到確保量測品質之監控，係成為一值得探討之議題。首先，藉由SPC 及MSA 之歷史資料探討當SPC 發生異常通知時， ̅ 管制圖中製程發生異常平均數移動之變化量，以及量測設備異常所移動之變化量進而求得檢定力，接著使用貝氏分析方法列出製程出現異常後，對應量測機台機率分佈情況，並估算由量測系統所引起異常的機率，當某一機率分佈可能性相對較高時，則量測設備評估應進行校正作業；期望能盡早發現於量測設備校正週期期間，由量測系統所引起異常的機率。最後，量測系統應進行校正時，則以量測系統分析(Measurement System Analysis, MSA)中的穩定性、偏性、線性、重複性與再現性(Gauge Repeatability & Reproducibility, GRR)以及區別分類數(Number of Distinct Categories, NDC)以評估量測設備之適用性與其精密度，並進一步確定量測設備是否發生異常；期望提供相關人員一評估方法，以降低其異常所造成企業成本的損失。

SPC (statistical process control) has been the main monitoring platform for manufacturing industry during their control processes, therefore the data source
obtained must be very reliable before used for analysis. The applicability and functionality of measurement equipment have also become a key consideration when making a purchasing decision. Therefore, predicts the abnormal of
measurement equipment will be worth exploring and discussing. This research applies with measurement equipment in a bicycle chain manufacturing industry,
according to Bayesian analysis, we used the existing data of measurement system analysis (MSA) and SPC to forecast the risk of measurement abnormal in order to ensure the quality of manufacturing monitoring. First，we used the average of ̅control charts abnormal changed to investigate abnormal alarm of SPC combined with the abnormal change of measurement equipment to calculate the Power, next enumerated manufacturing process abnormal data with Bayesian analysis and distributed into each measurement equipment to calculate the abnormal frequency. Suggest doing a calibration when one of the measurement
equipment with higher abnormal frequency. The result of the calibration should work in accordance with MSA, including stability、bias、linearity、repeatability、
reproducibility and number of distinct categories to determine with suitable precision and accuracy. We provide a reference to aware of measurement abnormal which can help lower the cost.

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