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  • IET-SMT.2018.5523

    Rights statement: This paper is a postprint of a paper submitted to and accepted for publication in IET Science Measurement and Technology and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.

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

Research output: Contribution to journalJournal articlepeer-review

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Machine-learning approach for fault detection in brushless synchronous generator using vibration signals. / Rahnama, Mehdi ; Vahedi, Abolfazl; Alikhani, Arta Mohammad ; Montazeri, Allahyar.

In: IET Science, Measurement & Technology, Vol. 13, No. 6, 01.08.2019, p. 852-861.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Rahnama, M, Vahedi, A, Alikhani, AM & Montazeri, A 2019, 'Machine-learning approach for fault detection in brushless synchronous generator using vibration signals', IET Science, Measurement & Technology, vol. 13, no. 6, pp. 852-861. https://doi.org/10.1049/iet-smt.2018.5523

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Author

Rahnama, Mehdi ; Vahedi, Abolfazl ; Alikhani, Arta Mohammad ; Montazeri, Allahyar. / Machine-learning approach for fault detection in brushless synchronous generator using vibration signals. In: IET Science, Measurement & Technology. 2019 ; Vol. 13, No. 6. pp. 852-861.

Bibtex

@article{6db0b73ddc234a47a72181573e4b4a6b,
title = "Machine-learning approach for fault detection in brushless synchronous generator using vibration signals",
abstract = "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. Here, 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.",
author = "Mehdi Rahnama and Abolfazl Vahedi and Alikhani, {Arta Mohammad} and Allahyar Montazeri",
note = "This paper is a postprint of a paper submitted to and accepted for publication in IET Science Measurement and Technology and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.",
year = "2019",
month = aug,
day = "1",
doi = "10.1049/iet-smt.2018.5523",
language = "English",
volume = "13",
pages = "852--861",
journal = " IET Science, Measurement & Technology",
issn = "1751-8822",
publisher = "Institution of Engineering and Technology",
number = "6",

}

RIS

TY - JOUR

T1 - Machine-learning approach for fault detection in brushless synchronous generator using vibration signals

AU - Rahnama, Mehdi

AU - Vahedi, Abolfazl

AU - Alikhani, Arta Mohammad

AU - Montazeri, Allahyar

N1 - This paper is a postprint of a paper submitted to and accepted for publication in IET Science Measurement and Technology and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.

PY - 2019/8/1

Y1 - 2019/8/1

N2 - 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. Here, 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.

AB - 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. Here, 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.

U2 - 10.1049/iet-smt.2018.5523

DO - 10.1049/iet-smt.2018.5523

M3 - Journal article

VL - 13

SP - 852

EP - 861

JO - IET Science, Measurement & Technology

JF - IET Science, Measurement & Technology

SN - 1751-8822

IS - 6

ER -