Diagnostics of separately excited DC motor based on analysis and recognition of signals using FFT and Bayes classifier

Głowacz, W.; Głowacz, Z.

doi:10.1515/aee-2015-0004

Cited by 11 publications

(4 citation statements)

References 7 publications

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“…Scientists developed methods of condition monitoring of electrical motors and various devices [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Many data processing methods (such as FFT, Wavelets, classifiers) were developed in the literature [21][22][23][24][25][26]. Data processing methods are associated with diagnostic methods.…”

Section: Fig 1 Induction Motorsmentioning

confidence: 99%

Recognition of Acoustic Signals of Induction Motors with the Use of MSAF10 and Bayes Classfier

Głowacz¹

2016

Archives of Metallurgy and Materials

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Condition monitoring of deterioration in the metallurgical equipment is essential for faultless operation of the metallurgical processes. These processes use various metallurgical equipment, such as induction motors or industrial furnaces. These devices operate continuously. Correct diagnosis and early detection of incipient faults allow to avoid accidents and help reducing financial loss. This paper deals with monitoring of rotor electrical faults of induction motor. A technique of recognition of acoustic signals of induction motors is presented. Three states of induction motor were analyzed. Studies were carried out for methods of data processing: Method of Selection of Amplitudes of Frequencies (MSAF10) and Bayes classifier. Condition monitoring is helpful to protect induction motors and metallurgical equipment. Further researches will allow to analyze other metallurgical equipment.

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Section: Fig 1 Induction Motorsmentioning

confidence: 99%

Recognition of Acoustic Signals of Induction Motors with the Use of MSAF10 and Bayes Classfier

Głowacz¹

2016

Archives of Metallurgy and Materials

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“…Diagnostics of a separately-excited DC motor were presented [3]. The method of current measurement in the rotor cage bars of a prototype induction motor was also developed [4].…”

Section: Introductionmentioning

confidence: 99%

Recognition of rotor damages in a DC motor using acoustic signals

Głowacz

2017

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. Diagnosis of electrical direct current motors is essential for industrial plants. The emphasis is put on the development of diagnostic methods of solutions for capturing, processing and recognition of diagnostic signals. This paper presents a technique of early fault diagnosis of a DC motor. The proposed approach is based on acoustic signals. A real-world data of the DC motor were used in the analysis. The work provides an original feature extraction method called the shortened method of frequencies selection (SMoFS-15). The obtained results of the presented analysis show that the early fault diagnostic method can be used for monitoring electrical DC motors. The proposed method can also support other fault diagnosis methods based on thermal, current, and vibration signals.

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“…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%

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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Diagnostics of separately excited DC motor based on analysis and recognition of signals using FFT and Bayes classifier

Cited by 11 publications

References 7 publications

Recognition of Acoustic Signals of Induction Motors with the Use of MSAF10 and Bayes Classfier

Recognition of Acoustic Signals of Induction Motors with the Use of MSAF10 and Bayes Classfier

Recognition of rotor damages in a DC motor using acoustic signals

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

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