Time - frequency method and artificial neural network classifier for induction motor drive system defects classification
1
LSEM, Laboratoire des Systèmes Electromécaniques, Badji Mokhtar University, 23000 Annaba, B.O 12, Algeria.
These authors had equal contribution to this work
Submission date: 2023-06-07
Final revision date: 2023-10-23
Acceptance date: 2024-01-15
Online publication date: 2024-01-28
Publication date: 2024-01-28
Corresponding author
Saad Salah
LSEM, Laboratoire des Systèmes Electromécaniques, Badji Mokhtar University, 23000 Annaba, B.O 12, Algeria.
LSEM, Laboratoire des Systèmes Electromécaniques, Badji Mokhtar University, 23000 Annaba, B.O 12, Algeria.
Diagnostyka 2024;25(1):2024110
KEYWORDS
TOPICS
ABSTRACT
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.
ACKNOWLEDGEMENTS
The authors would like to thank the Algerian General Direction of Research (DGRSDT) for providing the facilities and the financial funding of this project.
FUNDING
The Algerian General Direction of Scientific Research and Technological Development (DGRSDT).
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