摘要
在現今的數位世代，面對不斷增長的的消費性電子產品需求，造就了半導體行業的激烈競爭。半導體製造商為確保市場競爭力，除開發新技術外，提高生產效率以及降低製造成本也是競爭策略之一。其中，製造設備的可用性與性能將直接影響生產效率，若發生計畫外的設備故障將導致大量的產能損失，甚至影響產品品質。預防性保養是當前產業用以避免非預期性設備異常的常見做法，但此方法未能有效的考量設備狀況，存在設備利用率不足的可能，因此狀態性維修(CBM)的概念逐漸萌芽。
狀態性維修策略是藉由製造設備上的感應器，大量蒐集生產過程的生產數據，在考量設備狀況下進行維護策略的製訂，以確保設備利用率。本研究提出以數據驅動的狀態性維修架構，利用設備感應器所蒐集的資料，先以偏最小平方判別分析(PLS-DA)進行數據降維，再基於變量投影重要性(VIP)選取特徵子集。經提取後的特徵一方面可經由轉換，進一步作為設備健康度指標(HI)，用以監測設備的當前狀況；另一方面則做為基於人工神經網路(ANN)模型的輸入，透過模型的構建來檢測設備未來的健康狀況。倘若設備的健康狀況開始出現下降狀態，則建立一個長短期記憶(LSTM)網路來預測設備的剩餘使用壽命(RUL)。
本研究以台灣某半導體公司提供的設備生產數據進行實證研究，並驗證其模型效度與可行性。
關鍵字：狀態性維修、機台狀態監控、預測性維修、人工神經網絡、長短期記憶、偏最小平方法。

Abstract
The technology revolution has created an ever-increasing demand for consumer electronics, which has consequently resulted in an intense competition in the semiconductor industry. Manufacturers focus on enhancing production efficiency and lowering their manufacturing costs. This is heavily dependent on the availability and performance of the manufacturing equipment. Unscheduled breakdown of fabrication equipment may cause significant production losses, and affect the product quality. Current industry practices are predominantly preventive or reactive maintenance. Regular maintenance performed without considering the equipment condition, which causes disruption in the production process and may lead to the underutilization of the equipment. The advancement in manufacturing technology allows the collection of sensor data from the equipment during production process. Condition Based Maintenance (CBM) strategy effectively integrates this data for making maintenance decisions. The proposed framework is a data driven approach that uses features extracted from the sensor data to develop a method for the condition monitoring of the equipment health. Partial least square discriminant analysis (PLS-DA) is used for dimensionality reduction of the data, and a subset of feature is selected based on variable importance projection (VIP). These features are further transformed into a health indicator (HI) that provides information about the current state of the equipment. Then an artificial neural network (ANN) based model is constructed for detecting the future health state. Finally, a long short-term memory (LSTM) network is built to predict the remaining useful life (RUL) of the equipment, once it enters the degraded state. An empirical study is conducted at a semiconductor company in Taiwan to validate this research.
Keywords: condition based maintenance, predictive maintenance, remaining useful life, artificial neural networks, long short-term memory, partial least square.

Table of Contents
Table of Contents i
List of Tables iii
List of Figures iv
Chapter 1 Introduction 1
1.1 Research Background and Motivation 1
1.2 Research Objective 3
1.3 Thesis Organization 4
Chapter 2 Literature Review 5
2.1 Maintenance and Maintenance Strategies 5
2.1.1 Reactive Maintenance 5
2.1.2 Preventive Maintenance 6
2.1.3 Condition Based Maintenance 7
2.2 Condition Monitoring 8
2.3 Remaining Useful Life Prediction 9
2.3.1 Model-based Approach 10
2.3.2 Data-driven Approach 11
2.3.3 Hybrid Approach 11
2.4 Artificial Neural Network (ANN) 11
2.5 Long-Short Term Memory (LSTM) 12
Chapter 3 Research Framework 14
3.1 Problem Definition 16
3.2 Data Preprocessing 17
3.2.1 Feature Extraction 18
3.2.3 Feature Selection 19
3.3 Model Construction 21
3.3.1 Health Indicator (HI) 21
3.3.2 Condition Monitoring 22
3.3.3 Remaining Useful Life Prediction 22
3.4 Evaluation and Interpretation 25
Chapter 4 Empirical Study 27
4.1 Problem Definition 27
4.2 Data Acquisition and Preprocessing 28
4.2.1 Feature Extraction 29
4.2.2 Feature Selection 30
4.3 CBM Model Construction 31
4.3.1 Health Indicator (HI) 31
4.3.2 Condition Monitoring - Classification Model 34
4.3.3 RUL Estimation - Regression Model 36
4.4 Results Interpretation 38
4.4.1 Comparison of Classification Model 38
4.4.2 Comparison of Regression Models 39
Chapter 5 Conclusion 41
5.1 Conclusion 41
5.2 Future Direction 42
Reference 43

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