Cloud-based diagnosis of refrigerant leakage fault of chiller using virtual sensor residuals assisted xgboost algorithm
Soğutma sistemlerinin soğutucu akışkan sızıntısı arızasının sanal sensör farkı destekli xgboost algoritması ile bulut tabanlı tespiti
- Tez No: 770571
- Danışmanlar: DOÇ. DR. ZHIMIN DU
- Tez Türü: Yüksek Lisans
- Konular: Fizik ve Fizik Mühendisliği, Makine Mühendisliği, Physics and Physics Engineering, Mechanical Engineering
- Anahtar Kelimeler: Deep learning, XGBoost, Preprocessing, HVAC system, FDD, Regression
- Yıl: 2022
- Dil: İngilizce
- Üniversite: Shanghai Jlao Tong University
- Enstitü: Yurtdışı Enstitü
- Ana Bilim Dalı: Makine Mühendisliği Ana Bilim Dalı
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 127
Özet
Over the recent decades, improvements in the sensor technology have enabled researchers to access extensive operational data of HVAC chillers. This large amounts of data have facilitated the adaptation of novelsmart fault detection and diagnosis (FDD) methods as a means to increasing the energy efficiency of buildings. However, although the data processing methods are very powerful, the experimental data collected from chillers generally lack the sufficient diversity and standardization that the data driven methods require to obtain a general model that can be applied in various cases. A deficiency in preprocessing or a misconception in the selection evaluation metric may easily result in a misinterpretation of the test results. If there is such misjudgment, the final FDD method would suffer heavily from low generalization capacity and overfitting. This paper highlights the origins of common misconceptions and attempts to achieve a higher generalization performance than the conventional methods by constructing a novel procedure to diagnose the refrigerant leakage fault of chillers. To achieve this target, first the faulty and fault-free operation data of an industrial chiller is collected. Then, after conducting the relevant preprocessing steps, an algorithm that creates virtual sensors based on fault-free conditions is constructed. This algorithm is then used to calculate selected residuals of actual sensor readings and virtual sensor predictions. An optimized XGBoost algorithm is supplied with both normal and faulty conditions as inputs to make the final prediction on the refrigerant charge level. The diagnosis results from the proposed method are compared with other widely-accepted methods such as Support Vector Machines (SVM), Multi-Layer Perceptron (MLP), Random Forest (RF) and pure Extreme Gradient Boosting (XGBoost). The results reveal that, all of the traditional methods have above 99% accuracy in random testing. Yet, when these methods are kfold tested, even the best compared model could only achieve accuracies of 54%, 68% and 72%. The proposed method, on the other hand, achieved an exceptional generalization performance with accuracy results of 68%, 70% and 72%, while the method still retained above 99% accuracy in random testing. As the final step, a cloud based FDD framework is constructed to track the status chiller health from a user friendly website. The framework uses a dedicated server for its FDD module, where the software makes judgements on chiller health by predicting the refrigerant load level from the sensor data obtained by the chiller in real-time. The proposed model is uploaded to the framework and the study is concluded with conducting simulations and tests on the online system.
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