外文翻译原文

Managing Incipient Faults in Rotating Machines based on Vibration Analysis and Fuzzy Logic

Abstract: This work provides a fault detection method for rotating machines based

on a change of system vibration pattern and in the operation status diagnosis by fuzzy logic. These changes are analyzed and used as parameters for predicting incipient faults, as well as their evolution in operation condition allowing predictive maintenance tasks. A mechanic structure (developed as an experimental prototype where faults can be inserted) called Rotating System has been used. The data acquisition of mechanical vibration has been made by a biaxial solid-state accelerometer in a low-power chip. The accelerometer outputs (axis x and y) provide digital signals whose acceleration information is the duty cycle variation. The outputs are directly measured by a microprocessor-based system without needing an A/D converter. This system, based on a TMS320C25 microprocessor (the Psi25), has been used for acquisition of vibration signals of the Rotating System. The acquired data (stored in *.dat files) are computed and analyzed in frequency domain with a signal processing tool (called SPTOOL) of the Matlab package. The vibration standard of the Rotating System, called the spectral signature, has been obtained based on the mean of ten *.dat files. The faults analyzed in this work are due to the unbalancing of axle_wheel by insertion of asymmetric masses. The relation of mass between the wheel and the smallest unbalanced element is 1 to 10,000. Based on the knowledge of specialists about the operation of a generic machine, elements of different masses have been used to simulate the faults and to diagnose the operation status. The fuzzy system was calibrated to detect and diagnose the normal, incipient fault, maintenance, and danger conditions of the Rotating System using linguistic variables. The rotation frequency and vibration amplitudes of the axle_wheel are considered in each situation as parameters for analysis, diagnostic, and decision by the specialist system (fuzzy logic).

Rui Francisco Martins Marçal

Federal Technological University of Paraná UTFPR

Engineering Management Program - PPGEP

km 04, Monteiro Lobato Avenue,

Ponta Grossa, PR.

84016-210 BRAZIL

E-mail: marcal@pg.cefetpr.br

Kazuo Hatakeyama

Federal Technological University of Paraná

UTFPR

Engineering Management Program - PPGEP

km 04, Monteiro Lobato Avenue,

Ponta Grossa, PR. 84016-210 BRAZIL

E-mail: hatakeyama@pg.cefetpr.br

Altamiro Amadeu Susin Federal University of Rio Grande do Sul

UFRGS

Electrical Engineering Department, Instrumentation Laboratory - LaPsi

103, Oswaldo Aranha Avenue

Porto Alegre, RS. 90035-190 BRAZIL

E-mail: altamiro.susin@ufrgs.br