錫膏檢測 (Solder Paste Inspection, SPI) 為電子設備製造業中重要的檢測流程之一。透過測量印刷電路板 (Printed Circuit Board, PCB) 上錫膏的尺寸與體積，以針對製程進行後續的品質分析。本研究旨在建立一個非監督式異常檢測系統，對從SPI所收集而來的串流資料進行分析。此串流資料分析系統採用一個結合邊緣運算與雲端運算的串流數據分析框架，藉此解決由表面黏著技術 (Surface Mount Technology, SMT) 產線中取得的SPI檢測資料之數據量過於龐大的問題。本研究分別利用具有自動時間窗格設定且基於局部遞歸率的異常搜索方法 (Local Recurrence Rate based Discord Search with Automatic Time Window, LRRDS-ATW)，以及基於遞歸率之具穩健性的K均值分群演算法 (Local Recurrence Rate and Robust K-Means clustering, LRR-RKMeans)，進行此數據框架內邊緣運算模型與雲端運算模型的配置。本研究之實驗以實際場域所收集而來的SPI資料進行分析，並配合上述所建立之框架，利用邊緣運算模型在邊緣端所收集的原始時間序列資料中進行初步偵測，並將檢測出的異常時間序列視為代表性的數據，傳至雲端進行進一步的檢測分析。而為了確保在僅傳送邊緣運算模型所偵測出的異常時間序列是否能夠完整代表原始資料，該實驗中使用混淆矩陣 (confusion matrix) 將邊緣端偵測結果與雲端偵測結果進行比較，並確認其相似程度。研究結果顯示出若使用LRRDS-ATW將能夠達到77%的相似程度，而LRR-RKMeans甚至可達到86%的相似程度。此兩種模型所顯示出的結果，表示於邊緣端所偵測出的異常時間序列資料，具有足夠的特徵能夠代表原始資料。

Solder Paste Inspection (SPI) is one of the important inspection processes in electronic device manufacturing. By measuring the size and volume of the solder paste on the Printed Circuit Board (PCB), the quality analysis can be performed on the manufacturing process. In this research, an unsupervised streaming data analysis system was proposed to detect the SPI data. This stream data analysis system used a stream data analysis framework that combined edge computing and cloud computing, and aimed to solve the problem of analyzing the SPI data. This research used Local Recurrence Rate based Discord Search with Automatic Time Window (LRDS-ATW) and Local Recurrence Rate and Robust K-Means clustering (LRR-RKMeans) to configure the edge computing model and cloud computing model within the streaming data frame. The experiments of this research analyzed the SPI data collected from the real field, and cooperated with the framework established above, used the edge computing model to make preliminary detections from the original time series data at the edge site, and then the detected abnormal patterns as the representative data, the abnormal patterns were transmitted to the cloud for further detection and analysis. In order to ensure that the abnormal time series detected at only the edge site can completely represent the original data. In the experiment, a confusion matrix was used to compare the edge results with the cloud results to confirm the similarity. From the results, it can be seen that if LRRDS-ATW is used, it will be able to achieve a similarity of 77%, and LRR-RKMeans even can reach a similarity of 86%. The results of both two methods showed that the abnormal time series data detected from the edge site were well representative enough to present the characteristic of original data.

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