Abstract
A strategy based on rough set (RS) and artificial neural network (ANN) is developed to detect and diagnose sensor drifting faults. The reduced information is used to develop classification rules and train the neural network to infer appropriate parameters. The differences between measured thermodynamic states and predicted states obtained from models for normal performance (residuals) are used as performance indices for sensor fault detection and diagnosis. Simultaneous temperature sensor drifting faults of the supply chilled water (SCW) and return chilled water (RCW) can be diagnosis successfully.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.