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Proceedings Paper

Electromechanical impedance-based fault detection in a rotating machine by using an operating condition compensation approach
Author(s): K. M. Tsuruta; D. S. Rabelo; C. G. Guimarães; A. A. Cavalini; R. M. Finzi Neto; V. Steffen
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Paper Abstract

The electromechanical impedance is a condition-based maintenance (CBM) methodology that uses sensors network to evaluate health condition of mechanical systems. Piezoelectric transducers are used as sensors and actuators to damage detection. Such approach monitors changes in the electric impedance of piezoelectric transducers that are bonded to the host structure. Normally the evaluation of the impedance responses is performed by using damage metrics, which permit to quantify the influence of damage. This is possible since the sensor electrical impedance is directly related to the mechanical impedance of the structure. However, the frequency response functions (FRFs) resulting from this method are susceptible to environmental and operational conditions that must be accounted for in order to avoid false diagnostics. Thus, the aim of this paper relies on the correct detection of incipient faults in rotating shafts under operating condition by using a real-time Impedance-based Structural Health Monitoring (ISHM) method. For this purpose, a data normalization procedure for compensation of changes in environmental and operating conditions is used to minimize changes in impedance signatures resulting from these external influences. Changes on dynamic load result from altering the rotation speed and unbalance level, while temperature changes stem from daily room temperature variations. The compensation technique is based on a hybrid optimization method associated with a given damage metrics. Additionally, a statistical model is used for threshold determination based on the Statistical Process Control (SPC) method. Experimental results show that an incipient damage associated with temperature and dynamic loads effects could be successfully detected with a probability of detection above 95 % confidence for the majority of the sensors used.

Paper Details

Date Published: 10 April 2017
PDF: 17 pages
Proc. SPIE 10172, A Tribute Conference Honoring Daniel Inman, 1017206 (10 April 2017); doi: 10.1117/12.2258227
Show Author Affiliations
K. M. Tsuruta, Univ. Federal de Uberlândia (Brazil)
D. S. Rabelo, Univ. Federal de Uberlândia (Brazil)
C. G. Guimarães, Univ. Federal de Uberlândia (Brazil)
A. A. Cavalini, Univ. Federal de Uberlândia (Brazil)
R. M. Finzi Neto, Univ. Federal de Uberlândia (Brazil)
V. Steffen, Univ. Federal de Uberlândia (Brazil)


Published in SPIE Proceedings Vol. 10172:
A Tribute Conference Honoring Daniel Inman
Donald J. Leo; Pablo A. Tarazaga, Editor(s)

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