Time - frequency method and artificial neural network classifier for induction motor drive system defects classification

• Autorzy: Behim Meriem , Merabet Leila , Saad Salah

Identyfikatory

DOI

10.29354/diag/181192

• Języki publikacji: EN

Abstrakty

EN

In this paper, by introducing two statistical parameters, energy and L-kurtosis, a new fault diagnostic system combining Wavelet Packet Decomposition and Multilayer Perceptron Neural Network is designed to improve efficiency and precision of induction motor defects diagnosis. This method is applied to vibratory signals of asynchronous motor running at two different rotational speeds (1500 rpm and 2000 rpm) at a sampling frequency of 8 KHz to detect three main types of defects: bearing faults, load imbalance and misalignment. These speeds are considered as the usual medium running speeds of induction motor. According to the results, the high performance and accuracy of this new faults diagnostic system is proved and confirmed, thus it can be used in the detection of other machines defects.

Słowa kluczowe

EN

energy; L-kurtosis; wavelet packet decomposition; multilayer perceptron neural network; induction motor defects; vibratory signals

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2024
• Tom: Vol. 25, No. 1
• Strony: art. no. 2024110
• Opis fizyczny: Bibliogr. 32 poz., rys., tab.

Twórcy

autor

Behim Meriem

• LSEM, Laboratoire des Systèmes Electromécaniques, Badji Mokhtar University, 23000 Annaba, B.O 12, Algeria

• https://orcid.org/0000-0003-0633-3087

autor

Merabet Leila

• LSEM, Laboratoire des Systèmes Electromécaniques, Badji Mokhtar University, 23000 Annaba, B.O 12, Algeria

• https://orcid.org/0000-0002-1847-0671

autor

Saad Salah

• LSEM, Laboratoire des Systèmes Electromécaniques, Badji Mokhtar University, 23000 Annaba, B.O 12, Algeria

• https://orcid.org/0000-0002-5375-4645

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).