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Sensors 2018, 18(1), 146; doi:10.3390/s18010146

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

Institute for Energy Engineering, Universitat Politècnica de València, Cmno. de Vera s/n, 46022 Valencia, Spain
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Received: 4 December 2017 / Revised: 31 December 2017 / Accepted: 2 January 2018 / Published: 6 January 2018
(This article belongs to the Section Physical Sensors)
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Abstract

The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in the 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 the 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. View Full-Text
Keywords: fault diagnosis; condition monitoring; short time Fourier transform; Slepian window; prolate spheroidal wave functions; discrete prolate spheroidal sequences; time-frequency distributions 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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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

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.

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