Data-Driven Modeling, Fault Diagnosis and Optimal Sensor Selection for HVAC Chillers

2007 ◽  
Vol 4 (3) ◽  
pp. 469-473 ◽  
Author(s):  
Setu Madhavi Namburu ◽  
Mohammad S. Azam ◽  
Jianhui Luo ◽  
Kihoon Choi ◽  
Krishna R. Pattipati
Keyword(s):  
Fault Diagnosis ◽  
Sensor Selection ◽  
Data Driven ◽  
Selection For ◽  
Data Driven Modeling

scholarly journals Sensor selection for fault diagnosis in uncertain systems

2018 ◽  
Vol 93 (3) ◽  
pp. 629-639 ◽  
Author(s):  
Daniel Jung ◽  
Yi Dong ◽  
Erik Frisk ◽  
Mattias Krysander ◽  
Gautam Biswas
Keyword(s):  
Fault Diagnosis ◽  
Uncertain Systems ◽  
Sensor Selection ◽  
Selection For

Discrete Fault Diagnosis of Structurally Reconfigurable Systems

2021 ◽  
Author(s):  
Eeshan Deosthale ◽  
Daniel Jung ◽  
Qadeer Ahmed
Keyword(s):  
Fault Diagnosis ◽  
Structural Analysis ◽  
Fault Detection ◽  
Hybrid Systems ◽  
Fault Tolerant ◽  
Automatic Transmission ◽  
Sensor Selection ◽  
Reconfigurable Systems ◽  
Mode Identification ◽  
Selection For

Abstract Fault diagnosis of a certain class of hybrid systems referred to as Structurally Reconfigurable (SR) systems is complicated. This is because SR systems tend to switch their configuration which may or may not be faulty. It is important to identify the mode of the SR system along with the corresponding fault if any, in order to facilitate a fault tolerant action. This paper combines discrete fault diagnosis with mode identification for SR systems to achieve two main objectives: Sensor selection for fault detection, isolation and mode identification, and residual selection for mode identification. The framework is built using a structural analysis based approach to meet these objectives. This framework is demonstrated for a 10-Speed Automatic Transmission, which is an illustrative example of SR systems.

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