Experimental investigation on broken rotor bar faults in three phase induction motors using MVSA-FFT method

Bessous, Noureddine; Sbaa, Salim; Toumi, Abida

doi:10.1109/ceit.2018.8751793

Cited by 4 publications

(4 citation statements)

References 25 publications

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“…When performing the condition monitoring, this method normalizes the signal according to the amplitude of the first harmonic after performing the FFT, which reduces the effect of motor load conditions. The FFT algorithm is strongly dependent on small frequency changes in the processing of vibration signals, as in case of larger changes, the FFT brings the error to the actual value of the signal [16,17,20,83,93]. This mathematical apparatus is also the basis for condition monitoring methods from the IFSA (Instantaneous Frequency Signature Analysis) group.…”

Section: Fast Fourier Transformmentioning

confidence: 99%

“…They achieved promising results, but discussed that fault information is overlapping, when the spectra of two motor states are compared. A year later, Bessous et al [17] dealt with the processing of signals obtained by MVSA techniques for fault detection of broken rotor bars in squirrel cage of IM. To process the vibration signals, the FFT was deployed again.…”

Section: Fast Fourier Transformmentioning

confidence: 99%

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Advanced Signal Processing Methods for Condition Monitoring

Jaroš

Byrtus

Dohnal

et al. 2022

Arch Computat Methods Eng

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Condition monitoring of induction motors (IM) among with the predictive maintenance concept are currently among the most promising research topics of manufacturing industry. Production efficiency is an important parameter of every manufacturing plant since it directly influences the final price of products. This research article presents a comprehensive overview of conditional monitoring techniques, along with classification techniques and advanced signal processing techniques. Compared methods are either based on measurement of electrical quantities or nonelectrical quantities that are processed by advanced signal processing techniques. This article briefly compares individual techniques and summarize results achieved by different research teams. Our own testbed is briefly introduced in the discussion section along with plans for future dataset creation. According to the comparison, Wavelet Transform (WT) along with Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA) and Park’s Vector Approach (PVA) provides the most interesting results for real deployment and could be used for future experiments.

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Section: Fast Fourier Transformmentioning

confidence: 99%

Section: Fast Fourier Transformmentioning

confidence: 99%

Advanced Signal Processing Methods for Condition Monitoring

Jaroš

Byrtus

Dohnal

et al. 2022

Arch Computat Methods Eng

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“…The most popular and widespread diagnostic methods used in the detection and classification of interturn short circuits of electrical machines are motor current signature analysis (MCSA) [21][22][23][24], motor vibration signature analysis (MVSA) [25][26][27], the Park vector approach (PVA) [28][29][30], or wavelet transform (WT) [31,32]. These techniques allow for noninvasive analysis of a diagnostic signal without having to turn off the machine, and thus without interrupting the current operating state of the machine.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Efficiency of Failure Recognition in Induction Machines through the Application of Deep Neural Networks

Pietrowski,

Górny

2024

Energies

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The objective of the investigation was to increase the effectiveness of damage detection in the stator of the squirrel-cage induction machine. The analysis aimed to enhance the operational trustworthiness of the squirrel-cage induction machine by employing nonintrusive diagnostic methods based on a current signal and modern artificial intelligence methods. The authors of the study introduced a diagnostic technique for identifying multiphase interturn short circuits of stator winding. These short circuits are one of the most common faults in induction machines. The proposed method focusses on deriving a diagnostic signal from the phase-current waveforms of the machine. The noninvasive nature of the diagnostic technique presented is attributed to the application of the field model of electromagnetic phenomena to determine the diagnostic signal. For this purpose, a field model of a squirrel-cage machine was developed. The waveforms of phase currents obtained from the field model were used as input into an elaborated machine failure neural classifier. A deep neural network was used to develop a neural classifier. The effectiveness of the developed classifier has been experimentally verified, and the obtained results have been presented, concluded, and discussed. The scientific novelty presented in the article is the presentation of research results on the use of a neural classifier to detect damage in all phases of the stator winding at an early stage of its appearance. The features of this type of damage are very difficult to observe in signal waveforms such as a phase current or torque.

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“…Regarding the reported methodologies in the literature for detecting and classifying faults on IMs, there are two general strategies that can be identified: (1) classical methodologies and (2) Data-Driven-based Approaches (DDA). Most of the classical methodologies apply conventional techniques over measured signals in the time, frequency, and time-frequency domains to visualize abnormal behaviors related to the occurrence of faults [13,14]; the most used techniques are the Fast Fourier Transform (FFT), the Discrete Wavelet Transform (DWT), and the Multiple Signal Classification (MUSIC), among others. Although these techniques have demonstrated their effectiveness, some challenges and limitations have not been completely addressed.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Entropy Analysis as a Fault-Related Feature for Detecting Faults in Induction Motors and Their Kinematic Chain

Jaen-Cuellar,

Saucedo-Dorantes,

Elvira-Ortiz

et al. 2024

Electronics

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The induction motors found in industrial and commercial applications are responsible for most of the energy consumption in the world. These machines are widely used because of their advantages like high efficiency, robustness, and practicality; nevertheless, the occurrence of unexpected faults may affect their proper operation leading to unnecessary breakdowns with economic repercussions. For that reason, the development of methodologies that ensure their proper operation is very important, and in this sense, this paper presents an evaluation of signal entropy as an alternative fault-related feature for detecting faults in induction motors and their kinematic chain. The novelty and contribution lie in calculating a set of entropy-related features from vibration and stator current signals measured from an induction motor operating under different fault conditions. The aim of this work is to identify changes and trends in entropy-related features produced by faulty conditions such as broken rotor bars, damage in bearings, misalignment, unbalance, as well as different severities of uniform wear in gearboxes. The estimated entropy-related features are compared to other classical features in order to determine the sensitivity and potentiality of entropy in providing valuable information that could be useful in future work for developing a complete methodology for identifying and classifying faults. The performed analysis is applied to real experimental data acquired from a laboratory test bench and the obtained results depict that entropy-related features can provide significant information related to particular faults in induction motors and their kinematic chain.

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Experimental investigation on broken rotor bar faults in three phase induction motors using MVSA-FFT method

Cited by 4 publications

References 25 publications

Advanced Signal Processing Methods for Condition Monitoring

Advanced Signal Processing Methods for Condition Monitoring

Enhancing the Efficiency of Failure Recognition in Induction Machines through the Application of Deep Neural Networks

Evaluation of Entropy Analysis as a Fault-Related Feature for Detecting Faults in Induction Motors and Their Kinematic Chain

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