| Autor: |
Ya-chao Zhang, Jiaojiao Xin, Pu Wang, Xuejin Gao |
| Rok vydání: |
2015 |
| Předmět: |
|
| Zdroj: |
The 27th Chinese Control and Decision Conference (2015 CCDC). |
| Popis: |
Aiming at the chiller process variables cannot be strictly obey the Gauss distribution, and the large number of variables between the serious correlation, this paper describes a fault detection method to detect the faults of chiller. Independent Component Analysis(ICA) approach is used to extract the correlation of variables of chiller and reduce the dimension of measured data. A ICA-based method model is built to determine the thresholds of statistics and calculate statistics I2 and SPE, which are used to check if a fault occurs in chiller. The method is validated using the laboratory data from ASHRAE RP-1043 and compared with Principle Component Analysis (PCA). Results show that the ICA-based method has better fault detection performance of chiller. It has very good sensitivity for early fault and can effectively reduce the false alarm rate. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
