scholarly journals Short-Frequency Fourier Transform for Fault Diagnosis of Induction Machines Working in Transient Regime

2017 ◽  
Vol 66 (3) ◽  
pp. 432-440 ◽  
Author(s):  
Jordi Burriel-Valencia ◽  
Ruben Puche-Panadero ◽  
Javier Martinez-Roman ◽  
Angel Sapena-Bano ◽  
Manuel Pineda-Sanchez
Keyword(s):  
Fourier Transform ◽  
Fault Diagnosis ◽  
Induction Machines ◽  
Transient Regime

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

2017 ◽  
Author(s):  
Jordi Burriel-Valencia ◽  
Ruben Puche-Panadero ◽  
Javier Martinez-Roman ◽  
Angel Sapena-Baño ◽  
Manuel Pineda-Sanchez
Keyword(s):  
Fault Diagnosis ◽  
Induction Machines ◽  
Transient Regime ◽  
Sequence Length ◽  
Time Frequency ◽  
Stator Current ◽  
Start Up ◽  
Analysis Window ◽  
Computational Resources ◽  
Short Time

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.

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

2017 ◽  
Author(s):  
Jordi Burriel-Valencia ◽  
Ruben Puche-Panadero ◽  
Javier Martinez-Roman ◽  
Angel Sapena-Bañó ◽  
Manuel Pineda-Sanchez
Keyword(s):  
Fault Diagnosis ◽  
Induction Machines ◽  
Transient Regime ◽  
Sequence Length ◽  
Time Frequency ◽  
Stator Current ◽  
Start Up ◽  
Analysis Window ◽  
Computational Resources ◽  
Short Time

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.

scholarly journals Fault Diagnosis in the Slip–Frequency Plane of Induction Machines Working in Time-Varying Conditions

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3398 ◽  
Author(s):  
Ruben Puche-Panadero ◽  
Javier Martinez-Roman ◽  
Angel Sapena-Bano ◽  
Jordi Burriel-Valencia ◽  
Martin Riera-Guasp
Keyword(s):  
Fault Diagnosis ◽  
Single Point ◽  
General Scheme ◽  
Induction Machine ◽  
Induction Machines ◽  
Transient Conditions ◽  
Time Frequency ◽  
Bearing Defects ◽  
Motor Current Signature Analysis ◽  
The Hilbert Transform

Motor current signature analysis (MCSA) is a fault diagnosis method for induction machines (IMs) that has attracted wide industrial interest in recent years. It is based on the detection of the characteristic fault signatures that arise in the current spectrum of a faulty induction machine. Unfortunately, the MCSA method in its basic formulation can only be applied in steady state functioning. Nevertheless, every day increases the importance of inductions machines in applications such as wind generation, electric vehicles, or automated processes in which the machine works most of time under transient conditions. For these cases, new diagnostic methodologies have been proposed, based on the use of advanced time-frequency transforms—as, for example, the continuous wavelet transform, the Wigner Ville distribution, or the analytic function based on the Hilbert transform—which enables to track the fault components evolution along time. All these transforms have high computational costs and, furthermore, generate as results complex spectrograms, which require to be interpreted for qualified technical staff. This paper introduces a new methodology for the diagnosis of faults of IM working in transient conditions, which, unlike the methods developed up to today, analyzes the current signal in the slip-instantaneous frequency plane (s-IF), instead of the time-frequency (t-f) plane. It is shown that, in the s-IF plane, the fault components follow patterns that that are simple and unique for each type of fault, and thus does not depend on the way in which load and speed vary during the transient functioning; this characteristic makes the diagnostic task easier and more reliable. This work introduces a general scheme for the IMs diagnostic under transient conditions, through the analysis of the stator current in the s-IF plane. Another contribution of this paper is the introduction of the specific s-IF patterns associated with three different types of faults (rotor asymmetry fault, mixed eccentricity fault, and single-point bearing defects) that are theoretically justified and experimentally tested. As the calculation of the IF of the fault component is a key issue of the proposed diagnostic method, this paper also includes a comparative analysis of three different mathematical tools for calculating the IF, which are compared not only theoretically but also experimentally, comparing their performance when are applied to the tested diagnostic signals.

scholarly journals FEM approach for diagnosis of induction machines' non-adjacent broken rotor bars by short-time Fourier transform spectrogram

2019 ◽  
Vol 2019 (17) ◽  
pp. 4566-4570 ◽  
Author(s):  
Panagiotis A. Panagiotou ◽  
Ioannis Arvanitakis ◽  
Neophytos Lophitis ◽  
Jose A. Antonino-Daviu ◽  
Konstantinos N. Gyftakis
Keyword(s):  
Fourier Transform ◽  
Induction Machines ◽  
Short Time Fourier Transform ◽  
Broken Rotor Bars ◽  
Short Time

A Study on Fault Diagnosis of Gears Based on Local Mean Decomposition Method

2013 ◽  
Vol 683 ◽  
pp. 899-902
Author(s):  
Qiang Pan ◽  
Deng Hong Xiao ◽  
Tian He
Keyword(s):  
Fourier Transform ◽  
Fault Diagnosis ◽  
Hilbert Transform ◽  
Decomposition Method ◽  
Local Mean Decomposition ◽  
Local Mean ◽  
Component Amplitude

In present paper, the effectiveness of local mean decomposition (LMD) method to signals of fault gears, which are multi-component amplitude modulated and frequency modulated, is demonstrated. A series of tests on wearing and broken tooth of gears are conducted. And the fault characteristics extracted by Fourier transform, Hilbert transform and LMD are compared. The results validate that LMD method is an effective way to extract the characteristics of fault gears and improve the accuracy of fault diagnosis of gears since it is able to reduce effect of false components.

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