Recognition of armature current of DC generator depending on rotor speed using FFT, MSAF-1 and LDA

Głowacz, Adam; Głowacz, W.; Głowacz, Z.

doi:10.17531/ein.2015.1.9

Cited by 32 publications

(19 citation statements)

References 29 publications

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“…Pre-processing of acoustic signal consists of 3 steps: splitting recorded soundtrack into 5-seconds samples, normalization of the amplitude, calculation of FFT [8].…”

Section: Pre-processingmentioning

confidence: 99%

“…Moreover the obtained means are as farther apart as it is possible. More information about LDA classifier is available in the literature [8,29,38,53].…”

Section: Linear Discriminant Analysismentioning

confidence: 99%

“…Some of them are based on recognition of electric, vibro-acoustic, thermal signals [6÷27]. Most of them are based on electric signals, because these signals are not disturbed so much as acoustic signals [1,8,10,56]. The methods based on acoustic signals are not known in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Fault diagnostics of acoustic signals of loaded synchronous motor using SMOFS-25-EXPANDED and selected classifiers

Głowacz

2016

Teh. vjesn.

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Original scientific paper A system of fault diagnostics of loaded synchronous motor was proposed. Proposed system was based on acoustic signals of loaded synchronous motor. A new method of feature extraction SMOFS-25-EXPANDED (shorted method of frequencies selection-25-Expanded) was proposed. Presented method was analysed for 3 classifiers: LDA (Linear Discriminant Analysis), NN (Nearest Neighbour), SOM (Self-organizing Map). Analysis was carried out for real incipient states of loaded synchronous motor. Acoustic signals generated by motor were used in analysis. The following states of motor were analysed: healthy motor, motor with shorted stator coil, motor with shorted stator coil and broken coil, motor with shorted stator coil and two broken coils. These states are caused by natural degradation of rotating synchronous motor. The results of recognition were good. Proposed method of acoustic signal recognition can be used to protect loaded synchronous motors. Keywords: acoustic signal; fault detection; loaded synchronous motor; recognitionDijagnostika greške akustičkih signala opterećenog sinkronog motora primjenom SMOFS-25-EXPANDED i odabranih klasifikatora Izvorni znanstveni članak Predlaže se sustav dijagnostike greške opterećenog sinkronog motora. Predloženi se sustav zasniva na akustičkim signalima opterećenog sinkronog motora. Predlaže se nova metoda određivanja karakteristika SMOFS-25-EXPANDED (shorted method of frequencies selection-25-expaned -skraćena metoda za izbor frekvencija-25-proširena). Predložena se metoda analizirala u odnosu na tri klasifikatora: LDA (Linear Discriminant Analysis -analiza linearnog diskriminatora), NN (Nearest Neighbour -najbliži susjed), SOM (Self-organizing Map -samoorganizirajuća mapa). Analiza je provedena za stvarno početna stanja opterećenog sinkronog motora. U analizi su iskorišteni akustički signali koje je stvarao motor. Analizirala su se sljedeća stanja motora: ispravan motor, motor sa skraćenim namotajem statora, motor sa skraćenim namotajem statora i prekinutim namotajem, motor sa skraćenim namotajem statora i dva prekinuta namotaja. Do takvih je stanja došlo zbog prirodne degradacije rotirajućeg sinkronog motora. Rezultati analize su dobri. Predložena metoda prepoznavanja akustičkog signala može se primijeniti za zaštitu opterećenih sinkronih motora.

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“…Pre-processing of acoustic signal consists of 3 steps: splitting recorded soundtrack into 5-seconds samples, normalization of the amplitude, calculation of FFT [8].…”

Section: Pre-processingmentioning

confidence: 99%

“…Moreover the obtained means are as farther apart as it is possible. More information about LDA classifier is available in the literature [8,29,38,53].…”

Section: Linear Discriminant Analysismentioning

confidence: 99%

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Fault diagnostics of acoustic signals of loaded synchronous motor using SMOFS-25-EXPANDED and selected classifiers

Głowacz

2016

Teh. vjesn.

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“…These kinds of signals do not have many disturbances. The recognition of electrical signals of motors was also described in the literature [30][31][32][33][34][35][36][37][38][39][40]. However more analyses are needed in this topic, to develop more efficient methods of fault diagnosis .…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

Głowacz¹,

Głowacz²,

Głowacz³

et al. 2017

Archives of Metallurgy and Materials

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A degradation of metallurgical equipment is normal process depended on time. Some factors such as: operation process, friction, high temperature can accelerate the degradation process of metallurgical equipment. In this paper the authors analyzed three phase induction motors. These motors are common used in the metallurgy industry, for example in conveyor belt. The diagnostics of such motors is essential. An early detection of faults prevents financial loss and downtimes. The authors proposed a technique of fault diagnosis based on recognition of currents. The authors analyzed 4 states of three phase induction motor: healthy three phase induction motor, three phase induction motor with 1 faulty rotor bar, three phase induction motor with 2 faulty rotor bars, three phase induction motor with faulty ring of squirrel-cage. An analysis was carried out for original method of feature extraction called MSAF-RATIO15 (Method of Selection of Amplitudes of Frequencies -Ratio 15% of maximum of amplitude). A classification of feature vectors was performed by Bayes classifier, Linear Discriminant Analysis (LDA) and Nearest Neighbour classifier. The proposed technique of fault diagnosis can be used for protection of three phase induction motors and other rotating electrical machines. In the near future the authors will analyze other motors and faults. There is also idea to use thermal, acoustic, electrical, vibration signal together.

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“…There is also a wide research field on the development of methods for incipient fault diagnostics. Fault diagnostics in rotating machines is mainly carried out through vibration, electrical, acoustic, and thermal analysis [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Thermal Images of Direct Current Motor with Application of Area Perimeter Vector and Bayes Classifier

Głowacz¹,

Głowacz²,

Głowacz³

2015

Measurement Science Review

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Infrared thermography can measure the temperature of a surface remotely. In this article authors present a diagnostic method of incipient fault detection. The proposed approach is based on pattern recognition. It uses monochrome thermal images of the rotor with the application of an area perimeter vector and a Bayes classifier. The investigations have been carried out for direct current motor without faults and motor with shorted rotor coils. The measurements were performed in the laboratory. The efficiency of recognition using the area perimeter vector and the Bayes classifier was 100 %. The investigations show that the method based on recognition of thermal images can be profitable for engineers. The proposed method can be applied in mining, metallurgy, fuel industry and in factories where electrical motors are used.

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Recognition of armature current of DC generator depending on rotor speed using FFT, MSAF-1 and LDA

Cited by 32 publications

References 29 publications

Fault diagnostics of acoustic signals of loaded synchronous motor using SMOFS-25-EXPANDED and selected classifiers

Fault diagnostics of acoustic signals of loaded synchronous motor using SMOFS-25-EXPANDED and selected classifiers

Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

Recognition of Thermal Images of Direct Current Motor with Application of Area Perimeter Vector and Bayes Classifier

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