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- W4313648258 abstract "Data-driven Automated Fault Detection and Diagnosis (AFDD) methods represent one of the most promising options for improving energy, environmental and economic performance of Air-Handling Units (AHUs). In this paper, a curated experimental faulted and unfaulted dataset associated to the field operation of a typical real AHU is firstly presented; a new rule-based data-driven AFDD method for fault detection and diagnosis of coils, fans and sensors is developed and its accuracy has been assessed in contrast with measured data." @default.
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- W4313648258 date "2023-01-01" @default.
- W4313648258 modified "2023-09-26" @default.
- W4313648258 title "Experimental Assessment of a Preliminary Rule-Based Data-Driven Method for Fault Detection and Diagnosis of Coils, Fans and Sensors in Air-Handling Units" @default.
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- W4313648258 doi "https://doi.org/10.1007/978-981-19-8769-4_34" @default.
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