Fault detection and diagnosis of electric bus air conditioning systems incorporating domain knowledge and probabilistic artificial intelligence
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-05-01
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| Series: | Energy and AI |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546824000302 |
| _version_ | 1797224124178759680 |
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| author | Fangzhou Guo Zhijie Chen Fu Xiao |
| author_facet | Fangzhou Guo Zhijie Chen Fu Xiao |
| author_sort | Fangzhou Guo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T13:48:08Z |
| format | Article |
| id | doaj.art-4d9308774e3d4d1fb558bd7db7cd2c5b |
| institution | Directory Open Access Journal |
| issn | 2666-5468 |
| language | English |
| last_indexed | 2024-04-24T13:48:08Z |
| publishDate | 2024-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj.art-4d9308774e3d4d1fb558bd7db7cd2c5b2024-04-04T05:07:20ZengElsevierEnergy and AI2666-54682024-05-0116100364Fault detection and diagnosis of electric bus air conditioning systems incorporating domain knowledge and probabilistic artificial intelligenceFangzhou Guo0Zhijie Chen1Fu Xiao2Department of Building Environment and Energy Engineering, Hong Kong Polytechnic University, Hong Kong; Lawrence Berkeley National Laboratory, Berkeley, CA, USADepartment of Building Environment and Energy Engineering, Hong Kong Polytechnic University, Hong KongDepartment of Building Environment and Energy Engineering, Hong Kong Polytechnic University, Hong Kong; Research Institute for Smart Energy, Hong Kong Polytechnic University, Hong Kong; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2666546824000302Fault detection and diagnosisPredictive maintenanceAir conditionerElectric vehicleData-driven modelGaussian process |
| spellingShingle | Fangzhou Guo Zhijie Chen Fu Xiao Fault detection and diagnosis of electric bus air conditioning systems incorporating domain knowledge and probabilistic artificial intelligence Energy and AI Fault detection and diagnosis Predictive maintenance Air conditioner Electric vehicle Data-driven model Gaussian process |
| title | Fault detection and diagnosis of electric bus air conditioning systems incorporating domain knowledge and probabilistic artificial intelligence |
| title_full | Fault detection and diagnosis of electric bus air conditioning systems incorporating domain knowledge and probabilistic artificial intelligence |
| title_fullStr | Fault detection and diagnosis of electric bus air conditioning systems incorporating domain knowledge and probabilistic artificial intelligence |
| title_full_unstemmed | Fault detection and diagnosis of electric bus air conditioning systems incorporating domain knowledge and probabilistic artificial intelligence |
| title_short | Fault detection and diagnosis of electric bus air conditioning systems incorporating domain knowledge and probabilistic artificial intelligence |
| title_sort | fault detection and diagnosis of electric bus air conditioning systems incorporating domain knowledge and probabilistic artificial intelligence |
| topic | Fault detection and diagnosis Predictive maintenance Air conditioner Electric vehicle Data-driven model Gaussian process |
| url | http://www.sciencedirect.com/science/article/pii/S2666546824000302 |
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