FPGA-Based Online Detection of Multiple Combined Faults in Induction Motors Through Information Entropy and Fuzzy Inference

Romero-Troncoso, René de Jesús; Saucedo-Gallaga, Ricardo; Cabal-Yépez, Eduardo; García-Pérez, Arturo; Osornio-Ríos, Roque Alfredo; Álvarez-Salas, R.; Miranda-Vidales, Homero; Huber, Nicolas

doi:10.1109/tie.2011.2123858

Cited by 133 publications

(67 citation statements)

References 39 publications

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“…It relates the uncertainty of the signal or event associated with a given probability distribution. The concept of entropy has found broad applications in engineering, including, for instance, fault diagnoses [28][29][30][31]. A survey of recent methods for the fault diagnosis of rotating machinery using entropy techniques was discussed in [32].…”

Section: Background and Methodsologymentioning

confidence: 99%

Broken Rotor Bar Detection in LS-PMSM Based on Startup Current Analysis Using Wavelet Entropy Features

et al. 2017

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High-efficiency motors are being gradually introduced in many industrial applications because of their positive impacts on the environment by reducing energy consumption and CO 2 emission. In this respect, line start permanent magnet synchronous motors (LS-PMSMs) have been introduced recently. Due to their unique configuration, LS-PMSMs allow the obtaining of super premium efficiency levels, accompanied with a high torque and power factor. However, since the use of LS-PMSMs in the industry is in its infancy, no efficient scheme has yet been reported for broken rotor bar (BRB) fault detection in this type of motor. Accordingly, the main aim of this research is to investigate the fault-related feature for BRB faults on LS-PMSMs. In this regard, a simulation model and experimental setup for the investigation of BRB in LS-PMSM are implemented. The detection strategy for BRB in LS-PMSM proposed here is based on the monitoring of the start-up current signal and discrete wavelet transform. The entropy features are used as fault-related features for BRB faults. Finally, the ability of these features is validated for the detection of BRB in LS-PMSM through statistical analysis. In this research, the importance of the starting load is also considered for BRB detection in LS-PMSMs.

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Section: Background and Methodsologymentioning

confidence: 99%

Broken Rotor Bar Detection in LS-PMSM Based on Startup Current Analysis Using Wavelet Entropy Features

et al. 2017

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“…Generator bearing and eccentricity, as well as pinion gear faults, can automatically be detected through stator current analysis using information entropy [20], through an adaptive neuro-fuzzy inference system and particle filtering [21] or computing the energy from the fault-related frequencies [22,23]. The results are validated at laboratory benches for the studies in [20][21][22][23] and using simulated data in [24,25].…”

Section: Fault Diagnosis Techniques For Drive Trains and Induction Gementioning

confidence: 99%

Current Signature and Vibration Analyses to Diagnose an In-Service Wind Turbine Drive Train

Artigao

Koukoura

Carroll³

et al. 2018

Energies

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Abstract:The goal of the present paper is to achieve the diagnosis of an in-service 1.5 MW wind turbine equipped with a doubly-fed induction generator through current signature and vibration analyses. Real data from operating machines have rarely been analysed in the scientific literature through current signature analysis supported by vibrations. The wind turbine under study was originally misdiagnosed by the operator, where a healthy component was replaced and the actual failure continued progressing. The chronological evolution of both the electrical current and vibration spectra is presented to conduct an in-depth tracking of the fault. The diagnosis is achieved through spectral analysis of the stator currents, where fault frequency components related to rotor mechanical unbalance are identified. This is confirmed by the vibration analysis, which provides insightful information on the health of the drive train. These results can be implemented in condition monitoring strategies, which is of great interest to optimise operation and maintenance costs of wind farms.

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“…The hardware platform using field programmable gate arrays (FPGA) seems to be promising, although not matured. A more elaborate research for the incipient detection of multiple faults along with an FPGA based implementation is presented in [67]. It employs the paradigm of information entropy and fuzzy logic.…”

Section: Plant-wide Condition Monitoringmentioning

confidence: 99%

Rotating Electrical Machine Condition Monitoring Automation—A Review

et al. 2017

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Abstract:We review existing machine condition monitoring techniques and industrial automation for plant-wide condition monitoring of rotating electrical machines. Cost and complexity of a condition monitoring system increase with the number of measurements, so extensive condition monitoring is currently mainly restricted to the situations where the consequences of poor availability, yield or quality are so severe that they clearly justify the investment in monitoring. There are challenges to obtaining plant-wide monitoring that includes even small machines and non-critical applications. One of the major inhibiting factors is the ratio of condition monitoring cost to equipment cost, which is crucial to the acceptance of using monitoring to guide maintenance for a large fleet of electrical machinery. Ongoing developments in sensing, communication and computation for industrial automation may greatly extend the set of machines for which extensive monitoring is viable.

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FPGA-Based Online Detection of Multiple Combined Faults in Induction Motors Through Information Entropy and Fuzzy Inference

Cited by 133 publications

References 39 publications

Broken Rotor Bar Detection in LS-PMSM Based on Startup Current Analysis Using Wavelet Entropy Features

Broken Rotor Bar Detection in LS-PMSM Based on Startup Current Analysis Using Wavelet Entropy Features

Current Signature and Vibration Analyses to Diagnose an In-Service Wind Turbine Drive Train

Rotating Electrical Machine Condition Monitoring Automation—A Review

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