基於數據驅動之冰機效率及運轉參數建模與設備故障偵測

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謝育祥(Yu-Xiang Hsieh) 查詢紙本館藏

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電機工程學系

論文名稱

基於數據驅動之冰機效率及運轉參數建模與設備故障偵測

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摘要(中)

本論文以冰水主機設備（冰機）節能維護為研究主軸，冰機在暖通空調系統中是非常耗電的設備，此種大型設備的效率提升與參數優化，往往需藉專業人員的經驗以及知識才能達成。本研究嘗試藉由冰機的運轉數據資料來分析與調整適合的冰機特徵參數，若無法調整則找出冰機設備故障因素，期許本研究對於冰機設備的維護貢獻一份心力。
本研究以訓練資料訓練非線性迴歸模型(Non-linear regression model)，模型的參數是感測器收集到的參數資料。在模型設計上不直接結合冰機的物理性質做開發，發展數據驅動(Data driven)模式的研究。由於並不是所有參數都是可控的，可控參數被設定為其中四個，而系統在冰機效率低落診斷上給出的建議調整參數不限於可控參數，會包含到其他非可控參數，因此本研究針對此問題做進一步探討。在系統給出建議調整參數為非可控參數時，則透過訓練資料搭配最小平方法(Least square algorithm)來建立出建議參數(不可控)與可控參數的非線性迴歸模型，藉由此可控參數與建議調整參數(不可控)之關係模型，再依據該建議參數(不可控)調整數值為目標執行基因演算法(Genetic algorithm)找出四個可控參數的調整量；其中需先將訓練資料中各參數做最大-最小正規化(Min-Max normalization)再進行模型訓練，提高模型準確度。
本研究也針對設備的運行狀態進行監測，用以及早發現不正常運作及設備異常的狀況發生，利用專家規則(Expert rules)的判斷來解決此項問題；以t-檢定(t-test)來計算各參數之非可靠度，並將規則內的參數之非可靠度帶入專家故障規則中，分別計算每一條規則的可靠度，當該規則可靠度低於百分之50時，則會給出設備需保養或維運的標的，由此判斷屬於何種設備故障。本研究開發了使用者介面，以利使用者設定相關分析參數及資料庫的連接，將冰機效率分擬合結果、水塔效率曲線、故障可靠度及指定參數當前的狀態視覺化，並顯示分析之可控參數調整的值及故障分析需改善的設備。

摘要(英)

This paper focuses on the energy-saving maintenance of the chiller unit in the HVAC system. The chiller machine is a very power-consuming device. The efficiency improvement and parameter optimization of such large-scale equipment often require the experience and knowledge of professionals to achieve. This study attempts to analyze and adjust the uncontrollable characteristic parameters of the chiller machine based on the operating data of the chiller machine, and find out the failure factors of the chiller machine if it cannot be adjusted. It is hoped that this study will contribute to the maintenance of chiller machine equipment.
This study trains a nonlinear regression model using training data, with the variables being parameter data collected by sensors. In terms of model design, instead of directly combining the physical properties of the chiller, the study uses a black-box method to develop a data-driven research mode. As not all parameters are controllable, four are set as controllable parameters. The recommendations for adjusting parameters given by the system during analysis and diagnosis are not limited to controllable parameters and may include other non-controllable parameters. Therefore, this study further investigates this issue. When the system suggests adjusting uncontrollable parameters, a non-linear regression model for suggested and controllable parameters is established based on training data and the least square algorithm to create a non-linear relationship between controllable and suggested parameters. Genetic Algorithm is then used to find the adjustments for four controllable parameters according to the suggested parameter. Before model training, the training data is normalized to improve the accuracy of the model by min-max normalization.
This study also monitors equipment operating status to detect abnormal operations or equipment malfunction as early as possible, and expert rules are established to address this issue. Fault rules are defined based on past experiences, and t-tests are used to detect whether each specified parameter is abnormal. The selected parameters are then incorporated into the expert fault rules, and the reliability of each rule is calculated. If the reliability of a rule is lower than fifty percent, the target equipment is identified as needing maintenance or repair, and the type of equipment malfunction is determined. This study developed a user interface for users to set relevant analysis parameters and connect to the database. The analysis results are visualized, including the efficiency analysis of the chiller, the efficiency curve of the cooling tower, the fault reliability, and the current status of the specified parameters. The suggested values for controllable parameter adjustments and the equipment that needs improvement for fault analysis are also displayed.

關鍵字(中)

★ 非線性迴歸模型
★ 故障分析
★ 數據驅動
★ 專家規則

關鍵字(英)

★ non-linear regression model
★ fault analysis
★ data-driven
★ expert rule

論文目次

摘要 i
Abstract ii
致謝 iv
目錄 v
圖目錄 vii
表目錄 ix
第一章緒論 1
1.1研究背景與動機 1
1.2文獻回顧 1
1.3研究目標 4
1.4論文架構 4
第二章系統架構與軟硬體介紹 6
2.1冰水主機系統架構介紹 6
2.2系統硬體介紹 8
2.3系統軟體介紹 9
第三章冰機可控參數之值 13
3.1數據驅動 13
3.2迴歸模型之建立 13
3.2.1移除離群值 14
3.2.2數據最大-最小正規化 14
3.2.3非線性迴歸模型 16
3.2.4最小平方法 17
3.2.5冰機效率預測模型建立 19
3.3建立不可控與可控參數之關係 21
3.3.1參數模型建立 21
3.3.2基因演算法與可控參數的尋找 22
第四章冰機系統故障判別 26
4.1成對樣本t-檢定 26
4.2設備參數監測 28
4.3冷卻水塔效率分析 29
4.4專家故障規則 31
4.5本章節重點總結 34
第五章系統功能介紹 36
5.1系統流程與架構 36
5.2使用者參數及系統執行設定 37
5.3使用者介面介紹 39
第六章實驗結果 42
6.1資料參數正規化 42
6.2回歸模型冰機效率預測 43
6.3不可控參數調整結果 45
6.4指定參數監測結果 50
6.5故障規則可靠度計算 52
6.6使用者介面呈現結果 54
第七章結論與未來展望 58
7.1結論 58
7.2未來展望 58
參考文獻 60

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指導教授

王文俊(Wen-June Wang)

審核日期

2023-7-24

推文