Zobrazeno 1 - 10
of 362
pro vyhledávání: '"Truong‐An Le"'
Publikováno v:
IET Electric Power Applications, Vol 18, Iss 10, Pp 1107-1121 (2024)
Abstract The authors present a model for diagnosing motor faults based on machine learning, demonstrating advantages over other algorithms in terms of both improved fitness values and reduced running time. The structure of the model involves three pr
Externí odkaz:
https://doaj.org/article/4988f3f11e544c5d8e8e030698fe03a2
Publikováno v:
Mathematics, Vol 12, Iss 11, p 1718 (2024)
Motor fault diagnosis is an important task in the operational monitoring of electrical machines in manufacturing. This study proposes an effective bearing fault diagnosis model for electrical machinery based on machine learning techniques. The propos
Externí odkaz:
https://doaj.org/article/095cb55a8acf4948a23082878170157d
Publikováno v:
IEEE Access, Vol 11, Pp 51282-51295 (2023)
The main objective of this study is to propose a motor fault diagnosis model based on machine learning. Compared with the traditional motor fault diagnosis model, the proposed model can reduce the computation time. This model can be divided into thre
Externí odkaz:
https://doaj.org/article/67a9024e81924a69abb0350d98af86b2
Publikováno v:
IEEE Access, Vol 10, Pp 56691-56705 (2022)
An effective bearing fault diagnosis model based on machine learning is proposed in this study. The model can separate into three stages: feature extraction, feature selection, and classification. In the stage of feature extraction, multiresolution a
Externí odkaz:
https://doaj.org/article/30d09eda981c4f8d977fb403acf750b6
Publikováno v:
IEEE Access, Vol 10, Pp 69939-69949 (2022)
Early fault diagnosis is essential for the proper operation of rotating machines. This article proposes a fitness function in differential evolution (DE) that considers accuracy rate and false negative rate for optimization in brushless DC (BLDC) mot
Externí odkaz:
https://doaj.org/article/c2cb806b76434703a8d97d306c3207a4
Autor:
Chun-Yao Lee, Truong-An Le
Publikováno v:
IEEE Access, Vol 9, Pp 78241-78252 (2021)
The task of accurately bearing fault diagnosis of the rotary machinery from the measured signal remains a major problem that attracts a lot of attention. This paper proposed a new approach to build an efficient bearing fault diagnostic model for rota
Externí odkaz:
https://doaj.org/article/0fa6c3343dc144b9a56d4f0a5368cb72
Autor:
Chun-Yao Lee, Truong-An Le
Publikováno v:
IEEE Access, Vol 9, Pp 102671-102686 (2021)
This study proposes an effective bearing fault diagnosis model based on an optimized approach for feature selection. The measured signal of the electric motor is processed by envelope analysis and Hilbert-Huang transform techniques to extract the pot
Externí odkaz:
https://doaj.org/article/a64cc70e89c34a59b8d9ba15e944d086
Autor:
Chun-Yao Lee, Truong-An Le
Publikováno v:
IEEE Access, Vol 8, Pp 198343-198356 (2020)
This article presents an effective bearing fault diagnosis model based on multiple extraction and selection techniques. In multiple feature extraction, the discrete wavelet transform, envelope analysis, and fast Fourier transform are considered. Whil
Externí odkaz:
https://doaj.org/article/dcbf4447123d40f793919cbf9e8d9707
Publikováno v:
Mathematics, Vol 11, Iss 6, p 1442 (2023)
This paper describes a development that offers new opportunities for detecting faulty bearings. Prioritization is based on the technique for order of preference by similarity to the ideal solution (TOPSIS) for the most discriminative features in the
Externí odkaz:
https://doaj.org/article/3c15912c14714c0abd9caa6dba28fd97
Publikováno v:
Mathematics, Vol 10, Iss 13, p 2250 (2022)
This paper proposes a fault-detection system for faulty induction motors (bearing faults, interturn shorts, and broken rotor bars) based on multiresolution analysis (MRA), correlation and fitness values-based feature selection (CFFS), and artificial
Externí odkaz:
https://doaj.org/article/603c9c83d1ac49c8a821a57edffbceb5