Loose Belt Fault Detection and Virtual Flow Meter Development Using Identified Data-driven Energy Model for Fan Systems

Gang Wang (), Junke Wang, Nurayn Tiamiyu, Zufen Wang and Li Song
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Gang Wang: Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL 33146, USA
Junke Wang: School of Aerospace and Mechanical Engineering, University of Oklahoma, Norman, OK 73019, USA
Nurayn Tiamiyu: School of Aerospace and Mechanical Engineering, University of Oklahoma, Norman, OK 73019, USA
Zufen Wang: Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL 33146, USA
Li Song: School of Aerospace and Mechanical Engineering, University of Oklahoma, Norman, OK 73019, USA

Sustainability, 2023, vol. 15, issue 16, 1-27

Abstract: An energy model that correlates fan airflow, head, speed, and system power input is essential to detect device faults and optimize control strategies in fan systems. Since the application of variable-frequency drives (VFDs) makes the motor-efficiency data published by manufacturers inapplicable for VFD–motor–fan systems, the fan efficiency and drive (belt–motor–VFD) efficiency must be identified for each individual system to obtain accurate energy models. The objectives of this paper are to identify an energy model of existing VFD–motor–fan systems using available experimental data and demonstrate its applications in loose belt fault detection and virtual airflow meter development for optimal control. First, an approach is developed to identify the fan head, fan efficiency, and drive-efficiency curves using available fan head, speed, and system power input as well as temporarily measured airflow rate without measuring shaft power. Then, the energy model is identified for an existing VFD–motor–fan system. Finally, the identified model is applied to detect the slipped belt faults and develop the virtual airflow meter. The experiment results reveal that the developed approach can effectively obtain the energy model of VFD–motor–fan systems and the model can be applied to effectively detect slipped belt faults and accurately calculate the fan airflow rate.

Keywords: fans; drive systems; energy models; loose belt detection; virtual airflow meters (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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