Decentralized microsystem-based diagnostics of bearings of an electric motor

Author

Marschner, U. ; Jossa, I. ; Elender, G. ; Hoffmann, T. ; Herrmann, T. ; Heinrich, M. ; Fischer, W.-J. ; Krompas, A. ; Becker, E.

Author_Institution

Semicond. & Microsyst. Technol. Lab., Dresden Univ. of Technol., Dresden, Germany

fYear

1999

fDate

Aug. 31 1999-Sept. 3 1999

Firstpage

1149

Lastpage

1154

Abstract

A new microsystem is presented which is able to diagnose bearing faults completely autonomous using vibration and temperature measurements. The heart of the microsystem is a 16-Bit digital signal processor (DSP) which can operate up to 100 MIPS (million instructions per second). It is able to calculate the diagnosis result including necessary Fast Fourier Transforms (FFT), envelope calculations and the classification by a neural network within a few seconds. The extracted features are stored in a large non-volatile memory and are used for a long term trend analysis. Current or predicted faults are displayed locally and announced to the staff or a host computer via field bus or phone.

Keywords

electric motors; fast Fourier transforms; fault diagnosis; machine bearings; neural nets; power engineering computing; DSP; FFT; bearing faults diagnosis; decentralized microsystem-based diagnostics; digital signal processor; electric motor; fast Fourier transforms; field bus; microsystem; neural network; temperature measurements; vibration measurements; Circuit faults; Digital signal processing; Feature extraction; Neural networks; Substrates; Temperature measurement; Vibrations; Local Bearing fault diagnosis; envelope spectrum; hybrid microsystem; process monitoring; self-organizing neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (ECC), 1999 European

Conference_Location

Karlsruhe

Print_ISBN

978-3-9524173-5-5

Type

conf

Filename

7099464