A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Comprehensive study on sensitive parameters for chiller fault diagnosis
Highlights Sensitive parameters to chiller faults are screened out using sensitivity analysis. Cascade feature cleaning and supplement (CFCS) is proposed for refinement. Nine out of 64 parameters have been screened out with an accuracy of 99.79%. The feature set has an excellent practicality and generalization performance. Parameters of lubricating oil are excellent features for fault diagnosis.
Abstract For the fault diagnosis of a building chiller, the selection of suitable features/parameters is potentially more critical than the selection of diagnosis methods. This study explores the sensitive parameters for the seven typical faults in chillers by performing global sensitivity analysis (GSA) based on a Random Forest (RF) meta-model, and proposes a novel hybrid feature screening strategy of cascade feature cleaning and supplement (CFCS) based on correlation analysis and experience. Compared with the traditional experience-based selection, the proposed methods can enable an insight from all dimensions; hence reduce the number of sensors significantly while retaining as much useful information as possible. The results show that the fourteen (SPCS-2 set) or nine parameters (SPDM-set) screened out from the original 64 parameters have an excellent indication to the seven faults, with the diagnosis accuracy reaching 99.67% and 99.79%, respectively. The generalization performance and diagnostic reliability of the feature sets has also been verified under different diagnosis methods. Besides, it is demonstrated that the parameters of lubricating oil exhibit an immediate and salient indication to the system status, more significant for fault diagnosis than those of refrigerant. Therefore, sensors for oil parameters are recommended to be installed for an early recognition of faults.
Comprehensive study on sensitive parameters for chiller fault diagnosis
Highlights Sensitive parameters to chiller faults are screened out using sensitivity analysis. Cascade feature cleaning and supplement (CFCS) is proposed for refinement. Nine out of 64 parameters have been screened out with an accuracy of 99.79%. The feature set has an excellent practicality and generalization performance. Parameters of lubricating oil are excellent features for fault diagnosis.
Abstract For the fault diagnosis of a building chiller, the selection of suitable features/parameters is potentially more critical than the selection of diagnosis methods. This study explores the sensitive parameters for the seven typical faults in chillers by performing global sensitivity analysis (GSA) based on a Random Forest (RF) meta-model, and proposes a novel hybrid feature screening strategy of cascade feature cleaning and supplement (CFCS) based on correlation analysis and experience. Compared with the traditional experience-based selection, the proposed methods can enable an insight from all dimensions; hence reduce the number of sensors significantly while retaining as much useful information as possible. The results show that the fourteen (SPCS-2 set) or nine parameters (SPDM-set) screened out from the original 64 parameters have an excellent indication to the seven faults, with the diagnosis accuracy reaching 99.67% and 99.79%, respectively. The generalization performance and diagnostic reliability of the feature sets has also been verified under different diagnosis methods. Besides, it is demonstrated that the parameters of lubricating oil exhibit an immediate and salient indication to the system status, more significant for fault diagnosis than those of refrigerant. Therefore, sensors for oil parameters are recommended to be installed for an early recognition of faults.
Comprehensive study on sensitive parameters for chiller fault diagnosis
Energy and Buildings ; 251
2021-07-27
Article (Journal)
Electronic Resource
English
Application of Classification Functions to Chiller Fault Detection and Diagnosis
| British Library Conference Proceedings | 1997
Application of Classification Functions to Chiller Fault Detection and Diagnosis
| British Library Online Contents | 1997