PL
 |
EN
Preferencje help
Polish version English version Język
Widoczny [Schowaj] Abstrakt
Liczba wyników

Znaleziono wyników: 8

Liczba wyników na stronie
first rewind previous Strona / 1 next fast forward last
Wyniki wyszukiwania
help Sortuj według:

help Ogranicz wyniki do:
first rewind previous Strona / 1 next fast forward last
1
Content available Application of continuous wavelet transform and convolutional neural networks in fault diagnosis of PMSM stator windings
Pietrzak Przemysław, Wolkiewicz Marcin
Bulletin of the Polish Academy of Sciences. Technical Sciences
|
2024
|
Vol. 72, nr 5
art. no. e150202
EN
Efficiency, reliability, and durability play a key role in modern drive systems in line with the Industry 4.0 paradigm and the sustainability trend. To ensure this, highly efficient motors and appropriate systems must be deployed to monitor their condition and diagnose faults during the operation. For these reasons, in recent years, research has been increasingly focused on developing new methods for fault diagnosis of permanent magnet synchronous motors (PMSMs). This paper proposes a novel hybrid method for the automatic detection and classification of PMSM stator winding faults based on combining the continuous wavelet transform (CWT) analysis of the negative sequence component of the stator phase currents with a convolutional neural network (CNN). CWT scalogram images are used as the inputs of the CNN-based interturn short circuits fault classifier model. Experimental tests were conducted to verify the effectiveness of the proposed approach under various motor operating conditions and at an incipient stage of fault propagation. In addition, the effects of the input image format, CNN structure, and training process parameters on model accuracy and classification effectiveness were investigated. The results of the experimental tests confirmed the high effectiveness of fault detection (99.4%) and classification (97.5%), as well as other important advantages of the developed method.
2
Content available remote Zastosowanie krótkoczasowej transformaty Fouriera oraz sztucznej inteligencji do wykrywania uszkodzeń uzwojeń stojana silnika synchronicznego o magnesach trwałych
Pietrzak Przemysław, Wolkiewicz Marcin
Przegląd Elektrotechniczny
|
2023
|
R. 99, nr 4
22--29
PL
W artykule przedstawiono możliwość wykorzystania krótkoczasowej transformaty Fouriera sygnału prądu fazowego stojana oraz modułu wektora przestrzennego prądów stojana do ekstrakcji symptomów uszkodzeń uzwojeń stojana silnika synchronicznego o magnesach trwałych. Dodatkowo, zaproponowano możliwość automatyzacji procesu wnioskowania o stanie uzwojenia stojana przy zastosowaniu wybranych algorytmów bazujących na sztucznej inteligencji: maszyny wektorów nośnych oraz perceptronu wielowarstwowego. System diagnostyczny rozszerzono o możliwość lokalizacji uszkodzonej fazy. Badania eksperymentalna potwierdzają wysoką skuteczność opracowanego rozwiązania.
EN
This paper presents the possibility of using the short-time Fourier transform of the stator phase current and stator current space vector module in the process of permanent magnet synchronous motor stator winding fault symptoms extraction. Additionally, the automatization of the stator winding condition inference process which the use of selected artificial intelligence based algorithms: Support Vector Machine and MultiLayer Perceptron is proposed. The developed diagnostic system has been extended with the functionality of locating the damaged phase. Experimental studies confirmed the high effectiveness of the developed method.
3
Content available remote Diagnostyka zwarć międzyzwojowych uzwojenia stojana PMSM przy zastosowaniu analizy STFT składowych symetrycznych prądów stojana oraz uczenia maszynowego
Pietrzak Przemysław, Wolkiewicz Marcin
Przegląd Elektrotechniczny
|
2023
|
R. 99, nr 3
1--8
PL
W artykule przedstawiono możliwość zastosowania krótkoczasowej transformaty Fouriera składowych symetrycznych prądów fazowych stojana i wybranych algorytmów uczenia maszynowego: K-najbliższych sąsiadów oraz perceptronu wielowarstwowego, do diagnostyki zwarć międzyzwojowych uzwojenia stojana silnika synchronicznego o magnesach trwałych. Zaproponowana metoda umożliwia uzyskanie wysokiej skuteczności klasyfikacji oraz lokalizacji uszkodzenia we wczesnym stadium, co potwierdziły badania eksperymentalne przeprowadzone w różnych warunkach pracy układu napędowego.
EN
This paper presents the possibility of using the Short-Time Fourier Transform of stator phase currents symmetrical components and selected machine learning algorithms: K-Nearest Neighbors and MultiLayer Perceptron, for the diagnosis of interturn short-circuits in the stator winding of a permanent magnet synchronous motor. The proposed method provides high effectiveness of classification and localization of the fault at an early stage, which is confirmed by experimental tests that were carried out in various operating conditions of the drive system.
4
Content available Unraveling Induction Motor State through Thermal Imaging and Edge Processing : A Step towards Explainable Fault Diagnosis
Piechocki Mateusz, Pajchrowski Tomasz, Kraft Marek, Wolkiewicz Marcin, Ewert Paweł
Eksploatacja i Niezawodność
|
2023
|
Vol. 25, no. 3
art. no. 170114
EN
Equipment condition monitoring is essential to maintain the reliability of the electromechanical systems. Recently topics related to fault diagnosis have attracted significant interest, rapidly evolving this research area. This study presents a non-invasive method for online state classification of a squirrel-cage induction motor. The solution utilizes thermal imaging for non-contact analysis of thermal changes in machinery. Moreover, used convolutional neural networks (CNNs) streamline extracting relevant features from data and malfunction distinction without defining strict rules. A wide range of neural networks was evaluated to explore the possibilities of the proposed approach and their outputs were verified using model interpretability methods. Besides, the top-performing architectures were optimized and deployed on resource-constrained hardware to examine the system's performance in operating conditions. Overall, the completed tests have confirmed that the proposed approach is feasible, provides accurate results, and successfully operates even when deployed on edge devices.
5
Content available Stator Winding Fault Detection of Permanent Magnet Synchronous Motors Based on the Short-Time Fourier Transform
Pietrzak Przemysław, Wolkiewicz Marcin
Power Electronics and Drives
|
2022
|
Vol. 7 (42)
112--133
EN
In modern drive systems, the high-efficient permanent magnet synchronous motors (PMSMs) have become one of the most substantial components. Nevertheless, such machines are exposed to various types of faults. Hence, on-line condition monitoring and fault diagnosis of PMSMs have become necessary. One of the most common PMSM faults is the stator winding fault. Due to the destructive character of this failure, it is necessary to use fault diagnostic methods that allow fault detection at its early stage. The article presents the results of experimental studies obtained from fast Fourier transform (FFT) and short-time Fourier transform (STFT) analyses of the stator phase current, stator phase current envelope and stator phase current space vector module. The superiority of the proposed method over the classical approach based on the stator current analysis using FFT is highlighted. The proposed solution is experimentally verified under various motor operating conditions. The application of STFT analysis discussed so far in the literature has been limited to the fault diagnosis of induction motors and the narrow range of the analysed motor operating conditions. Moreover, there are no works in the field of motor diagnostics dealing with STFT analysis for stator windings based on the stator current envelope and the stator current space vector module.
6
Content available Stator winding fault detection of permanent magnet synchronous motors based on the bispectrum analysis
Pietrzak Przemysław, Wolkiewicz Marcin
Bulletin of the Polish Academy of Sciences. Technical Sciences
|
2022
|
Vol. 70, nr 2
art. no. e140556
EN
The popularity of high-efficiency permanent magnet synchronous motors in drive systems has continued to grow in recent years. Therefore, also the detection of their faults is becoming a very important issue. The most common fault of this type of motor is the stator winding fault. Due to the destructive character of this failure, it is necessary to use fault diagnostic methods that facilitate damage detection in its early stages. This paper presents the effectiveness of spectral and bispectrum analysis application for the detection of stator winding faults in permanent magnet synchronous motors. The analyzed diagnostic signals are stator phase current, stator phase current envelope, and stator phase current space vector module. The proposed solution is experimentally verified during various motor operating conditions. The object of the experimental verification was a 2.5 kW permanent magnet synchronous motor, the construction of which was specially prepared to facilitate inter-turn short circuits modelling. The application of bispectrum analysis discussed so far in the literature has been limited to vibration signals and detecting mechanical damages. There are no papers in the field of motor diagnostic dealing with the bispectrum analysis for stator winding fault detection, especially based on stator phase current signal.
7
Content available Application of Spectral and Wavelet Analysis of Stator Current to Detect Angular Misalignment in PMSM Drive Systems
Pietrzak Przemysław, Wolkiewicz Marcin
Power Electronics and Drives
|
2021
|
Vol. 6 (41)
42--60
EN
This paper deals with the selected methods of detecting angular misalignment in drive systems with a permanent magnet synchronous motor (PMSM), which are based on the analysis of the stator phase current signal, as well as their experimental verification and comparison. The proposed and compared methods are spectral analysis and wavelet analysis of the stator current, stator current envelope, stator current space vector module. Furthermore, the influence of power supply frequency and load torque on the performance of the proposed diagnostic methods is also discussed. The experimental tests were carried out for an undamaged motor and for two levels of angular misalignment. The article discusses the question of exactly what damage symptoms can be extracted from each of the methods. In the case of spectral analyses, it is demonstrated which multiplicities of the failure frequency are the most sensitive to misalignment and the least sensitive to changes in motor operating condition, which may be considered novel in the case of drive systems with permanent magnet motors. It is also proven that discrete wavelet transform (DWT) of the envelope and monitoring of the value of the relevant components allows the detection of misalignment with the availability of measuring current only in one phase in various motor operating conditions.
8
Content available Diagnosis of Short-circuits in Induction Motor Stator Winding Using a Modified Park Transformation
Tarchała Grzegorz, Wolkiewicz Marcin, Krzysztofiak Mateusz
Power Electronics and Drives
|
2020
|
Vol. 5 (40)
123--133
EN
This paper deals with a novel diagnostic method for finding the stator winding short-circuit damage of induction motor drives. The proposed method is based on a new, simple idea of applying a modified, triple Park transform instead of using a computationally demanding on-line Fast Fourier Transform (FFT) analysis. The diagnostic method is based on the analysis of current and reference voltage vector components, which are the part of the Direct Field Oriented Control structure. The proposed method is verified experimentally using tests results. Further, the influence of speed, load torque and the parameters of PI regulators on the performance of the proposed diagnostic method are also discussed.
first rewind previous Strona / 1 next fast forward last
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.