冰水主機自我故障診斷之研究

A Study of Chiller Self-Fault-Diagnosis

呂金翰(Jin-Han Lyu)

指導教授 ： 李魁鵬

國立臺北科技大學/機電學院/能源與冷凍空調工程系碩士班/碩士(2009年)

https://doi.org/10.6841/NTUT.2009.00388

摘要

由於國內經濟發展快速，為了維持空間理想舒適條件，冰水主機設備成為建築物中不可或缺的設備。然而冰水主機會受到運轉時數增加、維修不良等因素，造成主機設備老舊退化及故障頻率增加，因此浪費建築物15~30%能源消耗。有鑑於此，如何開發出一套自動即時監控系統，達到故障預警協助管理保養與即時故障排除，避免系統運轉損壞，維持高效率運轉周期，就成為一門重要的學問。
本研究集合各種不同技術文獻，整理出空調系統中常見的故障分類，針對兩種故障群組進行探討，第一類是系統感測器故障分析，第二類是製冷系統故障分析。由於空調系統運轉條件必須要有正確的儀表與資訊，才能精確捕捉系統運轉點，因此維修人員必須定期對現場感測器進行校正與保養，所以本文提出兩種統計學理論的感測器故障診斷手法：主成分分析(Principal component analysis, 簡稱PCA)與聯合角度法(Joint angle method, 簡稱JAM)。主成分分析利用Q-statistic plot偵測故障與Q-contribution plot診斷故障原因；同時再運用聯合角度法建立感測器故障庫，進行感測器故障交叉比對，分離出隔離故障原因。利用兩種手法能夠增加感測器故障診斷準確性，提供現場操作人員正確資訊。
另外製冷系統故障分析是利用現有冰水主機故障點，推斷出系統物理性能指標。利用這些性能指標描述冰水主機的健康情況，套入性能回歸參考模式中，分隔出系統故障原因，驗證其迴歸模式準確性。最後將此兩種故障診斷策略利用VB軟體撰寫即時自動化故障診斷程式。

關鍵字

FDD ； PCA主成分分析 ； JAM聯合角度法 ； 迴歸模式 ； VB

並列摘要

Due to the rapid development of the domestic economy, in order to maintain a comfortable room for the ideal conditions the chiller has became as essential to the building. However, when the chiller running hours increase and there is improper maintenance, it will waste about 15 to 30 percent energy consumption. Therefore, it is important to develop a automatic real-time monitoring system to assist in the management of maintenance and failure prediction, and real-time troubleshooting in order to avoid damage to system operation and to maintain the efficient operation.
In this study, two kinds of fault diagnosis usually found in a chiller system were sorted out and discussed after a variety of technical literature review. The first category is the sensor fault analysis system and the second category is the analysis of cooling system failure. Because operating conditions of air-conditioning system must have the correct instrumentation and information systems to accurately capture operation, thus maintenance personnel must be regularly carried out on-site sensor calibration and maintenance. This study proposed two statistical methods of sensor fault diagnosis: Principal component analysis and Joint angle method. Principal component analysis use Q-statistic plot to dectect the fault and Q-contribution plot to diagnose the cause of the malfunction. At the same time, the joint angle sensor fault database was established to verify cross-sensor failure to isolate the cause of the malfunction isolation. The combination of this two methods increases the accuracy of sensor fault diagnosis and provide the operator the right information at the scene.
Another analysis of refrigeration system failure was the use of existing chiller’s failure to infer the performance indicators of the system. Performance indicators were used to describe the health of the chiller and then are input to regression model to indicate and isolate the failure causes. Finally, fault diagnosis strategy for this two fault diagnosis method were written in VB using real-time fault diagnosis of automated programs.

並列關鍵字

FDD ； Principal component analysis ； Joint angle method ； Regression model ； VB

參考文獻

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