A neural network prototype for fault detection and diagnosis of heating systems
- French Scientific and Technical Building Center, Marne-la-Vallee (France). HVAC Dept.
An artificial neural network (ANN) prototype for fault detection and diagnosis (FDD) in complex heating systems is presented in this paper. The six operating modes with faults used to develop this prototype stemmed from a detailed investigation in cooperation with heating systems maintenance experts, and are among the most important operating faults for this type of system. The prototype has been developed by using the daily values obtained by a preprocessing procedure of the simulation data of one reference heating system, and then generalizing to four heating systems not used during the training phase. This paper demonstrates the feasibility of using ANNs for detecting and diagnosing faults in heating systems provided that training data representative of the behavior of the systems with and without faults are available.
- OSTI ID:
- 345266
- Report Number(s):
- CONF-9702141-; TRN: IM9922%%199
- Resource Relation:
- Conference: American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) winter meeting, Philadelphia, PA (United States), 24-28 Feb 1997; Other Information: PBD: 1997; Related Information: Is Part Of ASHRAE transactions: Technical and symposium papers, 1997. Volume 103, Part 1; PB: 1136 p.
- Country of Publication:
- United States
- Language:
- English
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