Preprint Article Version 2 This version is not peer-reviewed

Fault Diagnosis of Induction Machines in Transient Regime Using Current Sensors with an Optimized Slepian Window

Version 1 : Received: 4 December 2017 / Approved: 4 December 2017 / Online: 4 December 2017 (15:53:29 CET)
Version 2 : Received: 5 December 2017 / Approved: 6 December 2017 / Online: 6 December 2017 (05:34:29 CET)

A peer-reviewed article of this Preprint also exists.

Burriel-Valencia, J.; Puche-Panadero, R.; Martinez-Roman, J.; Sapena-Bano, A.; Pineda-Sanchez, M. Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window. Sensors 2018, 18, 146. Burriel-Valencia, J.; Puche-Panadero, R.; Martinez-Roman, J.; Sapena-Bano, A.; Pineda-Sanchez, M. Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window. Sensors 2018, 18, 146.

Journal reference: Sensors 2018, 18, 146
DOI: 10.3390/s18010146

Abstract

The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as analysis window. In this paper the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15 MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current's spectrogram with a significant reduction of the required computational resources.

Subject Areas

fault diagnosis; condition monitoring; short time Fourier transform; slepian window; prolate spheroidal wave functions; discrete prolate spheroidal sequences; time-frequency distributions

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