Non-Invasive Detection of Rotor Short-Circuit Fault in Synchronous Machines by Analysis of Stray Magnetic Field and Frame Vibrations

Cuevas, Mauricio; Romary, Raphaël; Lecointe, Jean‐Philippe; Jacq, Thierry

doi:10.1109/tmag.2016.2514406

Cited by 64 publications

(42 citation statements)

References 9 publications

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“…where I′ f is the short circuit current, while I f is the exciting current. For Group (2), the short circuit current is 0.06 A (1.5%) while for Group (3) the short circuit current is 0.1 A (2.5%).…”

Section: Practical Application To Experimental Generatormentioning

confidence: 93%

“…Recently, signal processing field has gained a rapid development and achieved many new outputs. For instance, to overcome the shortcomings of Fourier Transform/Fast Fourier Transform, people proposed the Wavelet Transform (WT) method which has the advantages in both the localization and adaptive window width [2][3][4][5]. However, this method needs to choose the proper mother wavelet and set the decomposition layers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhanced Characteristic Vibration Signal Detection of Generator Based on Time-Wavelet Energy Spectrum and Multipoint Optimal Minimum Entropy Deconvolution Adjusted Method

Wang

Sun

et al. 2020

Mathematical Problems in Engineering

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To overcome the shortage of low SNR (signal to noise ratio) of the multipole generator vibration signal which brings rigid difficulty to the fault diagnosis, a new method which combines the Time-Wavelet Energy Spectrum (TWES) with the Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) algorithm is proposed. This method uses TWES to extract and enhance the characteristic signal, while employing MOMEDA to optimize the spectrum structure and filter the noise. The application of this method to the simulating signal as well as the test stator vibration signal in a 6-pole generator before and after rotor interturn short circuit fault validates the effectiveness of the method. Moreover, the comparison among the proposed method and some other general methods such as the Empirical Mode Decomposition (EMD) and the maximum correlative kurtosis deconvolution (MCKD) suggests that the proposed method is superior to these methods.

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Section: Practical Application To Experimental Generatormentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Enhanced Characteristic Vibration Signal Detection of Generator Based on Time-Wavelet Energy Spectrum and Multipoint Optimal Minimum Entropy Deconvolution Adjusted Method

Wang

Sun

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…In another pairing, MCSA and vibration were utilised by [9–12] to detect bowed rotor fault, inter‐turn short‐circuit fault, dynamic eccentricity, and bearing faults. Also, the use of flux and vibration was explored for the detection of short‐circuit and phase unbalance faults [13–15]. The identified limitations of such research works are the use of extra sensors due to the extraction of two signatures, and the sensors are all susceptible to EMI as they are conventional sensors.…”

Section: Some Limitations Of Existing Fault Detection Techniquesmentioning

confidence: 99%

Bi‐Signature optical spectroscopy for online fault detection in electrical machines

Alalibo

Cao

Liu

2019

J. eng.

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“…The fault detection in which there is no need to direct access to the faulted part has been investigated in some literature and some processes are proposed. In the articles, the fault detection is carried out using acoustic signals [2][3][4][5], vibration signals [6][7][8][9][10][11], some other types of signals [12][13][14][15], and the combination of some signals [16][17][18][19][20]. The KNN method is utilized to classify the short-circuit fault in the main winding and auxiliary coil of the single-phase induction motor using acoustic signals [2].…”

Section: Introductionmentioning

confidence: 99%

“…Mechanical and electrical faults detection of wind turbines has been investigated using the generator output power and rotational speed as well as continuous wavelet transform [18]. Simultaneous analysis of the stray flux and frame vibration of the synchronous machine have been used for a nondestructive and cheap method to detect short-circuit fault on the rotor winding of the synchronous machine [19]. Based on analysis of the modal voltage and the modal current of the stator, an approach is proposed in [20] for detection of induction motors stator turn-faults.…”

Section: Introductionmentioning

confidence: 99%

Machine‐learning approach for fault detection in brushless synchronous generator using vibration signals

Rahnama

Vahedi

Mohammad‐Alikhani

et al. 2019

IET Science, Measurement & Technology

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In order to maintain continuous production and to avoid the maintenance cost increment in power plants, it is important to monitor the condition of equipment, especially the generator. Regarding the impossibility of direct access to rotating diodes in brushless synchronous generators, the condition monitoring of these elements is very important. In this paper, a novel fault detection method is proposed for the diode rectifier of brushless synchronous generator. At the first stage of this method, the vibration signals are recorded and feature extraction is performed by calculating the relative energy of discrete wavelet transform components. Multiclass support vector machine (MSVM) is used for classification, and the best mother wavelet and number of decomposition level are chosen based on classification performance. To enhance the performance of the classification, a modified sequential forward subset selection approach is included by which the best statistical features are selected. In this approach, besides selecting the best subset of statistical features, the classification parameter is tuned according to the selected subset to achieve the best performance. The result of the proposed method is eventually compared with those results of classification performance using conventional subset selection. Experimental results show that the proposed method can detect rectifier faults effectively.

show abstract

Non-Invasive Detection of Rotor Short-Circuit Fault in Synchronous Machines by Analysis of Stray Magnetic Field and Frame Vibrations

Cited by 64 publications

References 9 publications

Enhanced Characteristic Vibration Signal Detection of Generator Based on Time-Wavelet Energy Spectrum and Multipoint Optimal Minimum Entropy Deconvolution Adjusted Method

Enhanced Characteristic Vibration Signal Detection of Generator Based on Time-Wavelet Energy Spectrum and Multipoint Optimal Minimum Entropy Deconvolution Adjusted Method

Bi‐Signature optical spectroscopy for online fault detection in electrical machines

Machine‐learning approach for fault detection in brushless synchronous generator using vibration signals

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