基于Kriging模型的冷水机组故障检测与诊断方法

提出应用Kriging模型对冷水机组进行故障检测与诊断（FDD），采用ASHRAE RP-1043项目中无故障运行数据建立并验证冷水机组Kriging模型.利用参数敏感性原理对比T-统计方法和指数加权移动平均（EWMA）方法，对比结果表明，EWMA方法提高了参数敏感性.结合Kriging模型、EWMA方法和故障诊断规则表，用实测故障数据对冷水机组故障进行检测与诊断，检测和诊断的故障包括冷凝器结垢、制冷剂充注过多、制冷剂泄漏、不凝性气体、冷冻水流量减少和冷却水流量减少6个故障．诊断结果表明，应用Kriging模型能够准确有效地检测与诊断冷水机组不同水平的故障.
The Kriging model was introduced to detect and diagnose the faults in the chillers of building air-conditioning systems. This model was built and validated by using the normal data from ASHRAE RP-1043. The methods of T-statistic and exponentially-weighted moving average (EWMA) were compared by the sensitivity of performance indexes. The results show that the EWMA can achieve better performance sensitivity. Combined with the EWMA, Kriging model and the rules of fault diagnosis, the chiller faults like condenser fouling, refrigerant overcharge, refrigerant leakage, non-condenser gas, reduced evaporator water flow rate, and reduced condenser water flow rate were diagnosed using the measured data from ASHRAE RP-1043. The diagnosis results show that the chiller faults at different levels can be accurately and efficiently detected and diagnosed by using the Kriging model.