建築設備會因營運的時間越長，使設備故障的機率越高，設備可靠度則相對越低，而由於設備的老化是無可避免的，故需要經由維護的作業，使得設備可維持在一定的功能性下，降低設備的老化速率，進而延長設備的更新置換時間，然而，維護的作業則會對應維護費用支出，因此，需建立一套有效益的維護策略模型，在能達到性能要求與預算考量下，降低維護成本的目標。
根據近年文獻指出，空調系統之維護費用在年度總維護預算，佔有相當大的比例，且在實務上維護之進行，通常於設備損壞造成整體空調系統無法正常運作時，才進行報修及後續維護等過程，以此維護方式不僅增加設備維護成本，更降低使用者之舒適度。而在於設備性能的考量上，常常無法被準確衡量，且在性能的劣化速率上，也會依不同的階段，有著相當大的差異。
在此，本研究將目標著重於大樓空調系統設備，利用浴缸曲線（Bathtub curve）之預測模式進行設備性能之評估，並應用基因演算法（Genetic Algorithms, GA）運算，最佳預防性維護作業之時間間隔與最佳維護方式，建立一空調系統維護策略最佳化模式，進而求解出符合最低可接受性能與成本限制之於空調維修最低成本費用，同時利用Visual Basic撰寫電腦程式，輔助使用者求得最佳維護區間與維護方式。
最後，經由模式驗證之結果所示，本研究提出之模式與應用程式，能提供設備管理者，方便達到維護管理的需求，且可由設備性能狀態，得知何時設備會處於較不穩定的狀態，可針對此階段加以監控，使得整體維護效益更能提昇。

Building facilities would get higher failure rate and lower reliability with time in its operation stage. Because it is hard to prevent the deterioration, maintenance is required to keep the function normal and extend the replacement period. However, maintenance can be costly. An efficient maintenance strategy model is therefore useful and will benefit from cost control as well as facilities performance.
According to recent literature, the maintenance cost of HVAC systems has a great amount in the total annual maintenance budget. But on one hand, HVAC systems are often maintained and repaired practically after broken down and wouldn’t function properly. Users and owners would face not only the increasing maintenance costs, but also the decreasing thermal comfort in this way. The other hand, the performance of the HVAC systems is difficult to evaluate while the deterioration rate varies from stages of the systems.
As a result, this research develops the optimal maintenance strategy for HVAC systems by using a prediction model of the bathtub curve to evaluate the performance and using genetic algorithms to optimize the maintenance interval and type. In addition, the research provides a computer program written in Visual Basic for minimizing the maintenance cost with constraints on both performance and cost, and therefore assists users with the information of the best maintenance interval and type.
Finally, as the results of the validation, this research proposes the model and applications for equipment manager to maintenance and management problems. Moreover, the overall facility performance will increase by predicting and monitoring the unstable stages.

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