Wyniki wyszukiwania - BazTech

1

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

Glowacz A., Glowacz W., Glowacz Z., Kozik J., Gutten M., Korenciak D., Khan Z. F., Irfan M., Carletti E.

Archives of Metallurgy and Materials

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2017

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Vol. 62, iss. 4

2413--2419

EN

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.

2

Recognition of images of finger skin with application of histogram, image filtration and K-NN classifier

Glowacz A., Glowacz Z.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 1

95--101

EN

In this paper, non-invasive method of recognition of finger skin was proposed. A plan of study of images of finger skin was proposed. Researches were carried out for three kinds of images: 60 h after injury, 160 h after injury, 450 h after injury. Proposed technique of recognition used methods of signal processing: extraction of magenta color, calculation of histogram, image filtration, calculation of perimeter, and K-NN classifier. A pattern creation process was conducted using 15 training images of finger skin. In the identification process 60 test images were used. The advantage of the presented method is analysis of the finger skin using a smartphone. The proposed approach will help to diagnose pathologies of human skin.

3

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

Glowacz A.

Archives of Metallurgy and Materials

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2016

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Vol. 61, iss. 1

153--158

EN

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.

4

Fault diagnostics of DC motor using acoustic signals and MSAF-RATIO30-EXPANDED

Glowacz A.

Archives of Electrical Engineering

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2016

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Vol. 65, nr 4

733--744

EN

An early fault diagnostic method of Direct Current motors was presented in this article. The proposed method used acoustic signals of a motor. A method of feature extraction called MSAF-RATIO30-EXPANDED (method of selection of amplitudes of frequencies – ratio 30% of maximum of amplitude – expanded) was presented and implemented. An analysis of proposed method was carried out for early fault states of a real DC motor. Four following states of the DC motor were measured and analyzed: the healthy DC motor, DC motor with 3 shorted rotor coils, DC motor with 6 shorted rotor coils, DC motor with a broken coil. Measured states were caused by natural degradation of the DC motor. The obtained results of analysis were good. The presented early fault diagnostic method can be used for protection of DC motors.

5

Diagnostics of Rotor Damages of Three-Phase Induction Motors Using Acoustic Signals and SMOFS-20-EXPANDED

Glowacz A.

Archives of Acoustics

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2016

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Vol. 41, No. 3

507--515

EN

A fault diagnostics system of three-phase induction motors was implemented. The implemented system was based on acoustic signals of three-phase induction motors. A feature extraction step was performed using SMOFS-20-EXPANDED (shortened method of frequencies selection-20-Expanded). A classification step was performed using 3 classifiers: LDA (Linear Discriminant Analysis), NBC (Naive Bayes Classifier), CT (Classification Tree). An analysis was carried out for incipient states of three-phase induction motors measured under laboratory conditions. The author measured and analysed the following states of motors: healthy motor, motor with one faulty rotor bar, motor with two faulty rotor bars, motor with faulty ring of squirrel-cage. Measured and analysed states were caused by natural degradation of parts of the machine. The efficiency of recognition of the analysed states was good. The proposed method of fault diagnostics can find application in protection of three-phase induction motors.

6

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

Glowacz A., Glowacz W., Glowacz Z.

Eksploatacja i Niezawodność

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2015

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Vol. 17, no. 1

64--69

EN

Recognition of states of electrical systems is very important in industrial plants. Article describes a recognition method of early fault detection of DC generator. The proposed approach is based on an analysis of the patterns. These patterns are the armature currents of selected electrical machine. Information contained in signals of armature current is depending on generator state. Researches were carried out for four states of generator with the use of Fast Fourier Transform (FFT), method of selection of amplitudes of frequencies (MSAF-1) and Linear Discriminant Analysis (LDA). The results of analysis show that the method is efficient and can be used to protect DC generators. This method was verified with the aid of acoustic signals recognition method.

PL

Rozpoznawanie stanów układów elektrycznych jest bardzo ważne w zakładach przemysłowych. W artykule opisano metodę rozpoznawania stanów przedawaryjnych generatora prądu stałego. Proponowane podejście jest oparte na badaniu wzorców. Wzorce te są prądami twornika wybranej maszyny elektrycznej. Informacja zawarta w sygnałach prądu twornika jest zależna od stanu generatora. Przeprowadzono badania dla czterech stanów generatora z zastosowaniem FFT, metody wyboru amplitud częstotliwości (MSAF-1) i liniowej analizy dyskryminacyjnej (LDA). Wyniki analizy pokazują, że metoda jest skuteczna i metoda może być stosowana do ochrony generatorów prądu stałego. Metoda została zweryfikowana za pomocą metody rozpoznawania sygnałów akustycznych.

7

Recognition of acoustic signals of induction motor using fft, smofs-10 and ISVM

Glowacz A.

Eksploatacja i Niezawodność

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2015

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Vol. 17, no. 4

569--574

EN

A correct diagnosis of electrical circuits is very essential in industrial plants. An article deals with a recognition method of early fault detection of induction motor. The described approach is based on patterns recognition. Acoustic signals of specific induction motor are analyzed patterns. Acoustic signals include information about motor state. The analysis of the patterns was conducted for three states of induction motor using Fast Fourier Transform (FFT), shortened method of frequencies selection (SMoFS-10) and Linear Support Vector Machine (LSVM). The results of calculations suggest that the method is efficient and can be also used for diagnostic purposes.

PL

Prawidłowa diagnostyka obwodów elektrycznych jest bardzo istotna w zakładach przemysłowych. Artykuł zajmuje się metodą rozpoznawania stanów przedawaryjnych silnika indukcyjnego. Opisane podejście jest oparte na rozpoznawaniu wzorców. Sygnały akustyczne określonego silnika indukcyjnego są badanymi wzorcami. Sygnały akustyczne zawierają informację o stanie silnika. Analiza wzorców została przeprowadzona dla trzech stanów silnika indukcyjnego używając FFT, skróconej metody wyboru częstotliwości (SMoFS-10) i liniowej maszyny wektorów wspierających (LSVM). Wyniki obliczeń sugerują, że metoda jest skuteczna i może być również zastosowana dla celów diagnostycznych.

8

Recognition of Acoustic Signals of Loaded Synchronous Motor Using FFT, MSAF-5 and LSVM

Glowacz A.

Archives of Acoustics

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2015

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Vol. 40, No. 2

197--203

EN

This article discusses a system of recognition of acoustic signals of loaded synchronous motor. This software can recognize various types of incipient failures by means of analysis of the acoustic signals. Proposed approach uses the acoustic signals generated by loaded synchronous motor. A plan of study of the acoustic signals of loaded synchronous motor is proposed. Studies include following states: healthy loaded synchronous motor, loaded synchronous motor with shorted stator coil, loaded synchronous motor with shorted stator coil and broken coil, loaded synchronous motor with shorted stator coil and two broken coils. The methods such as FFT, method of selection of amplitudes of frequencies (MSAF-5), Linear Support Vector Machine were used to identify specific state of the motor. The proposed approach can keep high recognition rate and reduce the maintenance cost of synchronous motors.

9

DC Motor Fault Analysis with the Use of Acoustic Signals, Coiflet Wavelet Transform, and K-Nearest Neighbor Classifier

Glowacz A.

Archives of Acoustics

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2015

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Vol. 40, No. 3

321--327

EN

This paper focuses on testing the monitoring system of the Direct Current motor. This system gives the possibility of diagnosing various types of failures by means of analysis of acoustic signals. The applied method is based on a study of acoustic signals generated by the DC motor. A study plan of the DC motor’s acoustic signal was proposed. Studies were conducted for a faultless DC motor and Direct Current motor with 3 shorted rotor coils. Coiflet wavelet transform and K-Nnearest neighbor classifier with Euclidean distance were used to identify the incipient fault. This approach keeps the motor operating in acceptable condition for a long time and is also inexpensive.

10

Diagnostics of direct current machine based on analysis of acoustic signals with the use of symlet wavelet transform and modified classifier based on words

Glowacz A.

Eksploatacja i Niezawodność

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2014

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Vol. 16, no. 4

554--558

EN

In the paper author proposed an original approach for detection and localization of faults occurring in Direct Current machine. A system for diagnosing DC machines was described. The system performed an analysis of the acoustic signals of DC machine. Researches were conducted for two states of Direct Current machines. The studies were conducted for the algorithms of data processing: Symlet wavelet transform and modified classifier based on words. A pattern creation process has been carried out for the 10 sound samples. An identification process has been carried out for the 40 sound samples. The described implementation of the system may be useful for protecting machines. Moreover, this approach will reduce the cost of maintenance and the number of damaged machines.

PL

W pracy autor zaproponował oryginalne podejście do wykrywania, lokalizacji usterek występujących w maszynie prądu stałego. Opisano implementację systemu do diagnostyki maszyn prądu stałego. System przeprowadzał analizę sygnałów akustycznych maszyny prądu stałego. Przeprowadzono badania dla dwóch stanów maszyny prądu stałego. Badania zostały przeprowadzone dla algorytmów przetwarzania danych: Transformacji falkowej Symlet i zmodyfikowanego klasyfikatora opartego na słowach. Proces tworzenia wzorca do rozpoznawania został przeprowadzony dla 10 próbek dźwięku. Proces identyfikacji został przeprowadzony dla 40 próbek dźwięku. Opisana implementacja systemu może być przydatna do ochrony maszyn. Ponadto podejście takie pozwoli zmniejszyć koszty utrzymania i liczbę uszkodzonych maszyn.

11

Recognition of Monochrome Thermal Images of Synchronous Motor with the Application of Binarization and Nearest Mean Classifier

Glowacz A., Glowacz A., Korohoda P.

Archives of Metallurgy and Materials

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2014

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Vol. 59, iss. 1

31--34

EN

This article discusses the recognition method of imminent failure conditions of synchronous motor. The proposed approach is based on a study of thermal images of the motor. Studies were carried out for four conditions of motor with the application of binarization and nearest mean classifier with Manhattan distance. Pattern creation process used 40 monochrome thermal images. Identification process was carried out for 160 monochrome thermal images. The experiments show that the method can be useful for protection of synchronous motor. Moreover, this method can be used to diagnose equipments in steelworks and other industrial plants.

PL

Artykuł omawia metodę rozpoznawania stanów przedawaryjnych silnika synchronicznego. Proponowane podejście jest oparte na badaniu obrazów termowizyjnych silnika. Przeprowadzono badania dla czterech stanów silnika z zastosowaniem binaryzacji i klasyfikatora najbliższej średniej z metryką Manhattan. Proces tworzenia wzorców do rozpoznawania został przeprowadzony dla 40 monochromatycznych obrazów termowizyjnych. W procesie identyfikacji użyto 160 monochromatycznych obrazów termowizyjnych. Eksperymenty pokazują, że metoda może być przydatna do ochrony silników synchronicznych. Ponadto metoda może być stosowana do diagnozowania urządzeń w hutach i innych zakładach przemysłowych.

12

Diagnostics of Synchronous Motor Based on Analysis of Acoustic Signals with the use of Line Spectral Frequencies and K-nearest Neighbor Classifier

Glowacz A.

Archives of Acoustics

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2014

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Vol. 39, No. 2

189--194

EN

In industrial processes electrical motors are serviced after a specific number of hours, even if there is a need for service. This led to the development of early fault diagnostic methods. Paper presents early fault diagnostic method of synchronous motor. This method uses acoustic signals generated by synchronous motor. Plan of study of acoustic signal of synchronous motor was proposed. Two conditions of synchronous motor were analyzed. Studies were carried out for methods of data processing: Line Spectral Frequencies and K-Nearest Neighbor classifier with Minkowski distance. Condition monitoring is useful to protect electric motors and mining equipment. In the future, these studies can be used in other electrical devices.

13

An Approach to Robust Visual Knife Detection

Kmiec M., Glowacz A.

Machine Graphics and Vision

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2011

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Vol. 20, No. 2

215-227

EN

Computerised monitoring of CCTV images is attracting a lot of attention both from potential end-users seeking to increase the effectiveness of their video surveillance systems and as a popular research topic as new methods and algorithms are being developed. In this paper an approach to detecting knives in images is presented. It is based on the use of Histograms of Oriented Gradients (HOG), feature descriptors invariant to geometric and photometric transformations except for rotation. We introduce a dataset containing images of knives in different backgrounds and in varying lighting conditions and evaluate the performance of an HOG-based SVM classifier. We study the question of creating a detector based on knife blade colour and discuss the use of GPU parallel computing as a method of speeding up the detection process.