In order to promote the stability of centrifugal pump units and maximize the role of centrifugal pumps, this paper analyzes the composition and basic working principle of centrifugal pumps, presents the main concerns ...In order to promote the stability of centrifugal pump units and maximize the role of centrifugal pumps, this paper analyzes the composition and basic working principle of centrifugal pumps, presents the main concerns of centrifugal pump maintenance, and finally investigates the common faults and maintenance methods of centrifugal pumps for reference.展开更多
The air conditioning systems in electric city buses usually operate in rapidly changing ambient conditions and are more likely to suffer from mechanical faults. Although many fault detection and diagnosis (FDD) method...The air conditioning systems in electric city buses usually operate in rapidly changing ambient conditions and are more likely to suffer from mechanical faults. Although many fault detection and diagnosis (FDD) methods have been developed for building air conditioning systems, they are difficult to be applied to bus air conditioners since its operation is highly dynamic and fault-free data are usually unavailable. Therefore, this paper proposes an FDD method for electric bus air conditioners to tackle the above issues. First, the method identifies faults in an unsupervised manner by comparing selected features among a group of peer systems. Then, considering the features are influenced by the operating conditions, Gaussian process regression (GPR) models are established to find the relationships between each feature and its influential parameters. The probabilistic nature of the GPR is used to differentiate predictions with large uncertainty, which are then excluded from FDD. In this way, robustness of the method is evidently improved. Finally, fault indexes are defined to detect and diagnose mechanical faults. We applied the method to a group of air conditioners in a city bus fleet. Results showed that it can effectively identify refrigerant undercharge and indoor and outdoor fan problems with low false positive/genitive rates. Also, the method is highly robust and not sensitive to the faulty systems in the bus fleet.展开更多
Fault detection and diagnosis(FDD)approaches comprise three main pillars:model-based,knowledge-based,and data-driven strategies.Data-driven approaches prioritise operational data and do not necessitate in-depth unders...Fault detection and diagnosis(FDD)approaches comprise three main pillars:model-based,knowledge-based,and data-driven strategies.Data-driven approaches prioritise operational data and do not necessitate in-depth understanding of the system’s background;yet,significant amounts of data is required,which often poses challenges to researchers.Since simulated data is inexpensive and can run numerous faults types with varying severities and time periods,it has been used in data-driven FDD analysis.However,the majority of FDD approaches are implemented at the system level of buildings.However,most buildings have numerous systems with distinct features.Furthermore,using individualised system-level analysis makes it difficult to see system-to-system relationships.Currently,there is a significant underrepresentation of research that investigate the applications of FDD models under whole-building scenarios,so as to identify a wider range of energy consumption related faults in buildings.Furthermore,since data-driven approaches significantly depend on the quantities of training data,it becomes challenging to diagnose faults that have limited features.As a result,this study diagnoses numerous building systems faults,including single and simultaneous faults with limited features.This is implemented within the context of the whole-building energy performance of religious buildings in hot climatic areas,employing data-driven FDD methodologies.Various multi-class classification approaches were investigated to classify both the normal condition and faulty classes.Furthermore,feature extraction methodologies were compared to quantify their potential for improving the diagnosis.In addition to the classification evaluation metrics,one-way ANOVA and Tukey-Kramer tests were implemented to examine the significance of the reported performance differences.RF classifier obtained highest classification accuracy during validation and testing with about 90%,indicating a promising performance in whole-building faults analysis.The adoption of feature extraction techniques did not improve classification performance,thereby emphasising that some classifiers may perform better with high-dimensional datasets.展开更多
文摘In order to promote the stability of centrifugal pump units and maximize the role of centrifugal pumps, this paper analyzes the composition and basic working principle of centrifugal pumps, presents the main concerns of centrifugal pump maintenance, and finally investigates the common faults and maintenance methods of centrifugal pumps for reference.
基金support of this research by the Research Talent Hub for ITF Project(ITP/002/22LP)sponsored by Hong Kong Innovation and Technology Fund and the Research Grants Council of the Hong Kong SAR(C5018-20GF).
文摘The air conditioning systems in electric city buses usually operate in rapidly changing ambient conditions and are more likely to suffer from mechanical faults. Although many fault detection and diagnosis (FDD) methods have been developed for building air conditioning systems, they are difficult to be applied to bus air conditioners since its operation is highly dynamic and fault-free data are usually unavailable. Therefore, this paper proposes an FDD method for electric bus air conditioners to tackle the above issues. First, the method identifies faults in an unsupervised manner by comparing selected features among a group of peer systems. Then, considering the features are influenced by the operating conditions, Gaussian process regression (GPR) models are established to find the relationships between each feature and its influential parameters. The probabilistic nature of the GPR is used to differentiate predictions with large uncertainty, which are then excluded from FDD. In this way, robustness of the method is evidently improved. Finally, fault indexes are defined to detect and diagnose mechanical faults. We applied the method to a group of air conditioners in a city bus fleet. Results showed that it can effectively identify refrigerant undercharge and indoor and outdoor fan problems with low false positive/genitive rates. Also, the method is highly robust and not sensitive to the faulty systems in the bus fleet.
文摘Fault detection and diagnosis(FDD)approaches comprise three main pillars:model-based,knowledge-based,and data-driven strategies.Data-driven approaches prioritise operational data and do not necessitate in-depth understanding of the system’s background;yet,significant amounts of data is required,which often poses challenges to researchers.Since simulated data is inexpensive and can run numerous faults types with varying severities and time periods,it has been used in data-driven FDD analysis.However,the majority of FDD approaches are implemented at the system level of buildings.However,most buildings have numerous systems with distinct features.Furthermore,using individualised system-level analysis makes it difficult to see system-to-system relationships.Currently,there is a significant underrepresentation of research that investigate the applications of FDD models under whole-building scenarios,so as to identify a wider range of energy consumption related faults in buildings.Furthermore,since data-driven approaches significantly depend on the quantities of training data,it becomes challenging to diagnose faults that have limited features.As a result,this study diagnoses numerous building systems faults,including single and simultaneous faults with limited features.This is implemented within the context of the whole-building energy performance of religious buildings in hot climatic areas,employing data-driven FDD methodologies.Various multi-class classification approaches were investigated to classify both the normal condition and faulty classes.Furthermore,feature extraction methodologies were compared to quantify their potential for improving the diagnosis.In addition to the classification evaluation metrics,one-way ANOVA and Tukey-Kramer tests were implemented to examine the significance of the reported performance differences.RF classifier obtained highest classification accuracy during validation and testing with about 90%,indicating a promising performance in whole-building faults analysis.The adoption of feature extraction techniques did not improve classification performance,thereby emphasising that some classifiers may perform better with high-dimensional datasets.
