Simulating application of operational modal analysis to a test rig

Author(s)

Storti, Gustavo Chaves ^[1] ; Carrer, Lais ^[1] ; da Silva Tuckmantel, Felipe Wenzel ^[1] ; Machado, Tiago Henrique ^[1] ; Cavalca, Katia Lucchesi ^[1] ; Bachschmid, Nicolo ^[2]

Affiliation

^[1] Univ Estadual Campinas, Sch Mech Engn, 200 Mendeleyev St, BR-13083860 Campinas, SP - Brazil

^[2] Politecn Milan, Dept Mech, Via Masa 1, I-20158 Milan - Italy

• Document type: Journal article
• Source: MECHANICAL SYSTEMS AND SIGNAL PROCESSING; v. 153, MAY 15 2021.
• Web of Science Citations: 1

Abstract

Industrial machinery is often equipped with proximity probes that measure continuously position and vibrations of the shaft, generally relative to the bearings. These measurements are used for surveillance of the machine, and furnish valuable information to be used in a diagnostic process, especially when run-up or run-down transients are recorded and processed. However, a question arises, during normal operating conditions at constant rotating speed can some additional information about the dynamical behavior of the rotating shaft be extracted from these measurements? Considering that in an industrial environment the shaft vibrations are generally affected by some noise transmitted through the supporting structure by other machines operating in the considered industrial plant, where also fluids are moving in pipes and vessels, all generating vibrations, operational modal analysis (OMA) could be used for identifying natural frequencies and modal damping. And which are the conditions of the vibration measurements required to get reliable results from processing the recorded signals? In this context, the present paper aims to analyze, with the aid of a test rig, the conditions in which OMA will result successful. These conditions include both the operating condition of the shaft and the amount and quality of the noise. The test rig, where also noise has been applied to the rotating shaft by means of a magnetic actuator, furnishes some test runs that are analyzed and used for tuning a model that can then be used to explore the different conditions. (C) 2020 Elsevier Ltd. All rights reserved. (AU)

• FAPESP's process: 19/25906-9 - Model based identification for Multi-Fault detection in rotating machinery
• Grantee: Katia Lucchesi Cavalca Dedini
• Support Opportunities: Research Grants - Visiting Researcher Grant - International
• FAPESP's process: 15/20363-6 - Fault tolerant identification and control of rotating systems
• Grantee: Katia Lucchesi Cavalca Dedini
• Support Opportunities: Research Projects - Thematic Grants