The Application of High-Resolution Spectral Analysis for Identifying Multiple Combined Faults in Induction Motors

García-Pérez, Arturo; Romero-Troncoso, René de Jesús; Cabal-Yépez, Eduardo; Osornio-Ríos, Roque Alfredo

doi:10.1109/tie.2010.2051398

Cited by 208 publications

(102 citation statements)

References 26 publications

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“…It is less frequent to find investigations focused on determining the presence of combined faults through signal analysis, García-Perez [25]. The author indicates that a safe and reliable alternative to determine single or multiple combined failures is through a FIR filter bank combined with high-resolution spectral-analysis (MUSIC).…”

Section: Combined Failuresmentioning

confidence: 99%

State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors

2017

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Despite the complex mathematical models and physical phenomena on which it is based, the simplicity of its construction, its affordability, the versatility of its applications and the relative ease of its control have made the electric induction motor an essential element in a considerable number of processes at the industrial and domestic levels, in which it converts electrical energy into mechanical energy. The importance of this type of machine for the continuity of operation, mainly in industry, is such that, in addition to being an important part of the study programs of careers related to this branch of electrical engineering, a large number of investigations into monitoring, detecting and quickly diagnosing its incipient faults due to a variety of factors have been conducted. This bibliographic research aims to analyze the conceptual aspects of the first discoveries that served as the basis for the invention of the induction motor, ranging from the development of the Fourier series, the Fourier transform mathematical formula in its different forms and the measurement, treatment and analysis of signals to techniques based on artificial intelligence and soft computing. This research also includes topics of interest such as fault types and their classification according to the engine, software and hardware parts used and modern approaches or maintenance strategies.

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Section: Combined Failuresmentioning

confidence: 99%

State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors

2017

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“…It is thus a powerful tool for condition monitoring and fault diagnosis. In this regard, intensive research efforts have focused on the use of approximation and detail signals for extracting the contribution of fault frequency components [10][11][12][13][14][15][16][17][18].…”

Section: -Time-frequency Domain Analysismentioning

confidence: 99%

On the Detection of Induction-Motor Rotor Fault by the Combined “Time Synchronous Averaging-Discrete Wavelet Transform” Approach

Ngote¹,

Ouassaid²,

Guedira³

et al. 2015

Journal of Electrical Engineering and Technology

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-Induction motors are widely used in industrial processes since they offer a very high degree of reliability. But like any other machine, they are vulnerable to faults, which if left unmonitored, might lead to an unexpected interruption at the industrial plant. Therefore, the condition monitoring of the induction motors have been a challenging topic for many electrical machine researchers. Indeed, the effectiveness of the fault diagnosis and prognosis techniques depends very much on the quality of the fault features selection. However, in induction-motor drives, rotor defects are the most complex in terms of detection since they interact with the supply frequency within a restricted band around this frequency, especially in the no-loaded case. To overcome this drawback, this paper deals with an efficient and new method to diagnose the induction-motor rotor fault based on the digital implementation of the monitoring algorithm based on the association of the Time Synchronous Averaging technique and Discrete Wavelet Transform. Experimental results are presented in order to show the effectiveness of the proposed method. The obtained results are largely satisfactory, indicating a promising industrial application of the combined "Time Synchronous Averaging -Discrete Wavelet Transform" approach.

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“…-1 denotes the matrix inverse. Using (14), it can be shown that D(fs ) H D(fs ) = 1M. It follows that …”

Section: B Amplitude Estimationmentioning

confidence: 99%

Induction machine fault detection enhancement using a stator current high resolution spectrum

Bouchikhi

Choqueuse

Benbouzid

et al. 2012

IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society

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Abstract-Fault detection in squirrel cage induction machines based on stator current spectrum has been widely investi gated. Several high resolution spectral estimation techniques have been developed and used to detect induction machine abnormal operating conditions. In this paper, a modified version of MUSIC algorithm has been developed based on the faults characteristic frequencies. This method has been used to estimate the stator current spectrum. Then, an amplitude estimator has been proposed and a fault indicator has been derived for fault severity measurement. Simulated stator current data issued from a coupled electromagnetic circuits approach has been used to prove the appropriateness of the method for air gap eccentricity and broken rotor bars faults detection.

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The Application of High-Resolution Spectral Analysis for Identifying Multiple Combined Faults in Induction Motors

Cited by 208 publications

References 26 publications

State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors

State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors

On the Detection of Induction-Motor Rotor Fault by the Combined “Time Synchronous Averaging-Discrete Wavelet Transform” Approach

Induction machine fault detection enhancement using a stator current high resolution spectrum

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