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Mechanical rotor unbalance monitoring based on system identification and signal processing approaches.

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Title: Mechanical rotor unbalance monitoring based on system identification and signal processing approaches.
Authors: Puerto-Santana, Cristian1,2 (AUTHOR) cpuerto@ainguraiiot.com, Ocampo-Martinez, Carlos2 (AUTHOR) carlos.ocampo@upc.edu, Diaz-Rozo, Javier1 (AUTHOR) jdiaz@ainguraiiot.com
Source: Journal of Sound & Vibration. Dec2022, Vol. 541, pN.PAG-N.PAG. 1p.
Subjects: System identification, Signal processing, Finite element method, Monitoring of machinery, Magnetic bearings, Mass production, Rotors
Abstract: Mechanical unbalance is an important source of vibrations that can cause malfunctions in rotodynamic machinery. In industrial applications, unbalance is a critical issue for mass production machines. Previous studies for detecting and monitoring unbalance are based on balancing machines, trial weights, and intrusive actuators, while other studies rely on signal processing techniques, finite element analysis and physical modeling. These methodologies have some critical drawbacks, especially when non-intrusive monitoring is required, such as having to trial weights or determine constructive parameters such as mass and stiffness. The proposed approach is based on detecting and monitoring the unbalance condition in rotatory machines using data extracted from vibration sensors and a rotation sensor fitted to the system supports. The methodology comprises two main steps: identifying the appropriate speed range for unbalance monitoring and the modal parameters of the rotor, and determining and continuously monitoring the unbalance condition. Signal processing and system identification techniques are used to estimate unbalance in the rotatory machine. Experimental results for two rotodynamic systems demonstrate satisfactory performance in identifying and monitoring different unbalance conditions. • A novel methodology for non intrusive mechanical unbalance monitoring is proposed. • The method selects the proper rotation speed for unbalance monitoring. • Unbalance is detected without using finite element analysis or trial weights. • Campbell diagrams modal parameters are estimated to compute unbalance. • The method is experimentally validated on two different rotodynamic systems. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Mechanical unbalance is an important source of vibrations that can cause malfunctions in rotodynamic machinery. In industrial applications, unbalance is a critical issue for mass production machines. Previous studies for detecting and monitoring unbalance are based on balancing machines, trial weights, and intrusive actuators, while other studies rely on signal processing techniques, finite element analysis and physical modeling. These methodologies have some critical drawbacks, especially when non-intrusive monitoring is required, such as having to trial weights or determine constructive parameters such as mass and stiffness. The proposed approach is based on detecting and monitoring the unbalance condition in rotatory machines using data extracted from vibration sensors and a rotation sensor fitted to the system supports. The methodology comprises two main steps: identifying the appropriate speed range for unbalance monitoring and the modal parameters of the rotor, and determining and continuously monitoring the unbalance condition. Signal processing and system identification techniques are used to estimate unbalance in the rotatory machine. Experimental results for two rotodynamic systems demonstrate satisfactory performance in identifying and monitoring different unbalance conditions. • A novel methodology for non intrusive mechanical unbalance monitoring is proposed. • The method selects the proper rotation speed for unbalance monitoring. • Unbalance is detected without using finite element analysis or trial weights. • Campbell diagrams modal parameters are estimated to compute unbalance. • The method is experimentally validated on two different rotodynamic systems. [ABSTRACT FROM AUTHOR]
ISSN:0022460X
DOI:10.1016/j.jsv.2022.117313