預防性維護為工業4.0時代中重要的發展項目之一，而剩餘使用壽命預測即為預防性維護方法的一種，可以有效降低設備故障所造成之損失。在電子代工廠中常見的波焊錫爐需要定時清除錫渣，以防止錫渣過多造成焊接品質下降或機台停止運轉的狀況發生。若固定時間定期清理，可能增加人力資源消耗外，或無法在焊接品質已下降的情況下，提前進行錫渣之清理。因此，若能透過預測波焊錫爐錫渣清理之剩餘使用壽命，將可降低人力資源與提升焊接品質。
本論文使用溫度、電流與高度感測器配合資料擷取器做為感測與量測裝置，進行波焊錫爐從開始運轉到泵浦馬達停止轉動時的資料收集。在感測器經過濾波去雜訊與變化量計算後，本論文使用健康指標化與感測器資料融合兩種方法將三種感測器的資料融合成一可做為評估波焊錫爐健康狀況之實際健康指標，並用實際健康指標套入自動迴歸移動平均模型進行剩餘使用壽命預測，在平均使用時間長度六小時的測試資料中，預估的平均誤差為20分鐘。
本論文已開發一套基於剩餘使用壽命預測之波焊錫爐清理預警系統，希望可以降低人力資源使用與提升波焊錫爐焊接品質。

Predictive maintenance is one of the most significant developing projects in industry 4.0. Remaining useful life prediction is one of the predictive maintenance methods, which can effectively reduce the losses caused by equipment failure. Wave soldering tin stoves commonly found in the electronic factories require regular cleaning of tin dross in prevention of motor clogging, which will cause quality decrease in soldering or cause the tin stoves to stop running. However, regular cleaning may increase the consumption of human resources, and the tin dross may not be cleaned on time while the soldering quality had become low. Therefore, if we can predict the tin dross cleaning time by using remaining useful life prediction, then we can decrease the consumption of human resources and increase the soldering quality.
We use temperature, current, and height sensors for data measuring with the data acquisition box in this thesis. Data is collected as the tin stove starts running till the motor stops running. After processing the data, we construct a health indicator and use data fusion to mix three different kinds of sensor’s data into one real health indicator, which can evaluate tin stove’s real health condition. After that, we use the real health indicator’s data and autoregressive moving average model to make the remaining useful life prediction. In the average testing time of 360 minutes, the average error of the tin stove’s cleaning prediction is 20 minutes.
In this thesis we have developed a pre-warning system of the wave soldering cleaning based on the prediction of remaining useful life to decrease the consumption of human resources and increase the soldering quality.

摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VII
表目錄 XII
第一章、 緒論 1
1.1 研究動機與目的 1
1.2 文獻探討 2
1.2.1 設備維護方法 2
1.2.2 剩餘使用壽命預測相關研究 3
1.3 論文架構 6
第二章、 研究背景 7
2.1 雙列直插元件焊接 7
2.2 渦電流距離感測器 8
2.3 熱電偶溫度感測器 10
2.4 剩餘使用壽命 13
2.4.1 剩餘使用壽命預測方法 14
2.4.2 剩餘使用壽命預測技術 14
2.5 資料融合 16
2.5.1 資料融合概述 16
2.5.2 多元一次線性迴歸分析 16
2.6 自動迴歸移動平均模型 18
第三章、 研究方法 20
3.1 系統架構 20
3.2 實驗裝置與環境參數 22
3.3 感測器與資料擷取系統 24
3.3.1 熱電偶溫度感測器與硬體轉換電路 24
3.3.2 渦電流距離感測器 27
3.3.3 電壓與電流感測模組 29
3.3.4 資料擷取器 30
3.4 系統流程架構 31
3.4.1 感測器資料 32
3.4.2 資料前處理 34
3.4.3 理想健康指標 40
3.4.4 資料融合與實際健康指標 42
3.4.5 預測模型建立與測試資料預測 45
3.5 實驗設計 47
3.5.1 實驗流程 48
3.5.2 實驗驗證 49
第四章、 實驗結果與討論 51
4.1 單一感測器資料預測 51
4.1.1 單一溫度感測器預測結果 51
4.1.2 單一高度感測器預測結果 54
4.1.3 單一電流感測器預測結果 56
4.1.4 單一感測器預測結果 58
4.2 單一感測器健康指標化預測 59
4.2.1 溫度感測器健康指標化剩餘使用壽命預測 59
4.2.2 高度感測器健康指標化剩餘使用壽命預測 62
4.2.3 電流感測器健康指標化剩餘使用壽命預測 64
4.2.4 感測器健康指標化預測結果 66
4.3 多感測器資料融合預測 67
4.3.1 一次迴歸資料融合預測 67
4.3.2 二次迴歸資料融合預測 70
4.3.3 無移動平均資料融合預測 73
4.4 實驗結果討論 77
第五章、 結論與未來展望 78
附錄一 79
附錄二 98
附錄三 117
參考文獻 136

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