Sound based induction motor fault diagnosis using Kohonen self-organizing map

Germen, Emin; Başaran, Murat Alper; Fidan, Mehmet

doi:10.1016/j.ymssp.2013.12.002

Cited by 77 publications

(36 citation statements)

References 18 publications

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“…The process of adjusting the weights can be found in [43]. The use of ANN to detect faults in electrical machines has been the subject of considerable recent research [5,[18][19][20][21][22]44,23]. …”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…The experimental results also considered noisy environment situations. One recently employed technique, found in [20], applied the analysis of acoustic data from an induction motor, captured simultaneously by multiple microphones. Correlations and analysis based on signal processing by wavelet transform are used in the current study as the inputs to extract features.…”

Section: Introductionmentioning

confidence: 99%

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A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors

Palácios

Silva

Goedtel

et al. 2015

Electric Power Systems Research

106

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Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors

Palácios

Silva

Goedtel

et al. 2015

Electric Power Systems Research

106

View full text Add to dashboard Cite

“…The vectors { } represent the signal trajectories in the -dimensional space. If we define a threshold , then a two-dimensional matrix can be obtained by comparing the distance between the vectors in { , , ⋯ , } with as showed in Equation (2).…”

Section: Recurrence Plot Methodsmentioning

confidence: 99%

“…As the rapid development of sensing and computing technology, numerous process data can be collected to reflect the variation of different process parameters, in which a large number of various waveform signals are included. Examples of these waveform signals include tonnage signals in the stamping process [1], acoustic data for squirrel cage induction motor fault diagnosis [2] and vibration signals for ball bearing defect diagnostics [3]. These waveform signals contain much process information of the process conditions.…”

Section: Introductionmentioning

confidence: 99%

A New Process Monitoring Method Based on Waveform Signal by Using Recurrence Plot

Zhou

Zhang

2015

Entropy

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Abstract:Process monitoring is an important research problem in numerous areas. This paper proposes a novel process monitoring scheme by integrating the recurrence plot (RP) method and the control chart technique. Recently, the RP method has emerged as an effective tool to analyze waveform signals. However, unlike the existing RP methods that employ recurrence quantification analysis (RQA) to quantify the recurrence plot by a few summary statistics; we propose new concepts of template recurrence plots and continuous-scale recurrence plots to characterize the waveform signals. A new feature extraction method is developed based on continuous-scale recurrence plot. Then, a monitoring statistic based on the top-approach is constructed from the continuous-scale recurrence plot. Finally, a bootstrap control chart is built to detect the signal changes based on the constructed monitoring statistics. The comprehensive simulation studies show that the proposed monitoring scheme outperforms other RQA-based control charts. In addition, a real case study of progressive stamping processes is implemented to further evaluate the performance of the proposed scheme for process monitoring.

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“…From early 2000, feedforward multilayer neural networks trained with the Levenberg-Marquardt algorithm have been applied for the detection of different IM faults: electrical and mechanical types (see [25,26,[33][34][35][36][37][38][39] among many others available in technical literature). The Kohonen maps were also used for IM fault detection [33,[37][38][39], but literature is not as abundant as in the case of feedforward networks. For example, in [37] the Kohonen Network (KN) with a Winner Takes All (WTA) method was applied to the detection of broken bar faults of IM using classical FFT of stator phase current and slip frequencies as network inputs.…”

Section: Introductionmentioning

confidence: 99%

Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors

et al. 2019

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Electrical winding faults, namely stator short-circuits and rotor bar damage, in total constitute around 50% of all faults of induction motors (IMs) applied in variable speed drives (VSD). In particular, the short circuits of stator windings are recognized as one of the most difficult failures to detect because their detection makes sense only at the initial stage of the damage. Well-known symptoms of stator and rotor winding failures can be visible in the stator current spectra; however, the detection and classification of motor windings faults usually require the knowledge of human experts. Nowadays, artificial intelligence methods are also used in fault recognition. This paper presents the results of experimental research on the application of the stator current symptoms of the converter-fed induction motor drive to electrical fault detection and classification using Kohonen neural networks. The experimental tests of a diagnostic setup based on a virtual measurement and data pre-processing system, designed in LabView, are described. It has been shown that the developed neural detectors and classifiers based on self-organizing Kohonen maps, trained with the instantaneous symmetrical components of the stator current spectra (ISCA), enable automatic distinguishing between the stator and rotor winding faults for supplying various voltage frequencies and load torque values.

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Sound based induction motor fault diagnosis using Kohonen self-organizing map

Cited by 77 publications

References 18 publications

A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors

A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors

A New Process Monitoring Method Based on Waveform Signal by Using Recurrence Plot

Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors

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