為了達到對化工廠進行整體監控的目的，我們必需要能夠得到各個重要程序變數的準確估測值，因此過去有關感測器網路設計的研究多半著重測量點配置。但除了準確度以外，事實上系統可靠度也是一個須研究的實際課題，在文獻中僅Sanchez與Bagajewicz [2000]曾經探討過流量感測器網路之單純修正型維修策略，但其他維修手段(如備份及重複硬體等)卻仍然付之闕如，而本研究之主要目的即是開發兼具重複與備份硬體之感測器網路維修策略，並以改良式基因演算法搜尋對應最適化問題最佳解。我們發現備份硬體對系統可靠度會有顯著的幫助，而重複硬體的裝設對提升可靠度的效果雖不如備份硬體顯著，但卻可同時增加估測準確度。在成本較充裕的情況下，採用兼具重複及備份之維修策略可以使系統可靠度與準確度同時超越傳統感測器網路所能達到的極限值。

Accurate estimates of the key variables are considered to be essential for assessing the performance of any chemical process. In order to enhance estimation precision, one of the main themes in past researches is concerned with the optimal placement of sensors. However, other than the problem of sensor network design, the issue of maintenance policy should also be addressed to ensure the system reliability.
In the literature, only Sanchez and Bagajewicz (2000) tried to analyze the impact of integrating a simple corrective maintenance program in the design of flow sensor network. Although reasonable results have been obtained in this study, it should be noted that the discussions of other means for enhancing the reliability, e.g. preventive maintenance, redundant and spare hardware, have not been included at all. Consequently, it is the objectives of this study to incorporate the possibilities of redundant and spare hardware in a corrective maintenance model and also develop a mathematical program for generating the best sensor locations and their maintenance strategy simultaneously in a mass-flow network.
The genetic algorithm were used in our application examples to solve the optimization problem. From the results we have obtained so far, it can be concluded that
The spare sensors can indeed be used to significantly raise the system reliability.
Although adopting redundant hardware is a less effective method for improving reliability, it can be applied to reduce the estimation variability as well.
If the life-cycle cost of a sensor network is not a limiting condition, the maximum precision and reliability achieved with the conventional design and maintenance strategies can always be surpassed with the use of proper redundant and spare sensors.

第一章 緒論1
第二章 基因演算法4
2-1 導論4
2-2 基因演算法的演化流程5
2-2-1 編碼與解碼6
2-2-2 起始族群9
2-2-3 適應函數10
2-2-4 複製10
2-2-5 交配13
2-2-6 突變15
2-3 菁英政策18
2-4 基因演算法的特點18
2-5 簡例19

第三章 最適流量感測器網路設計問題25
3-1 可觀測性26
3-2 可靠度及可用度29
3-3 生命週期成本34
3-4 準確度35
3-5 硬體重複性39
第四章 感測器維修策略42
4-1 修正維修42
4-2 具備份之修正維修45
4-3 考慮硬體重複性之具備份修正維修49

第五章 實例測試56
5-1 修正維修模式56
5-2 具備份之修正維修模式69
5-3 兼具重複及備份感測器之修正維修模式77

第六章 結論與展望87
參考文獻89
附錄A92
附錄B98

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