Zobrazeno 1 - 10
of 148
pro vyhledávání: '"Chun Yao Lee"'
Autor:
Chun‐Yao Lee, Guang‐Lin Zhuo
Publikováno v:
IET Science, Measurement & Technology, Vol 18, Iss 9, Pp 522-533 (2024)
Abstract Recently, data‐driven cross‐domain fault diagnosis methods for rotating machinery have been successfully developed. However, most existing diagnostic methods assume that the label spaces of the source and target domains are the same. In
Externí odkaz:
https://doaj.org/article/de158986179b41beaf89d3745195d7d6
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
Autor:
Chun‐Yao Lee, Edu Daryl C. Maceren
Publikováno v:
IET Renewable Power Generation, Vol 18, Iss 14, Pp 2496-2511 (2024)
Abstract Intelligent fault diagnosis for wind energy systems requires identifying unique characteristics to differentiate various fault types effectively, even when data discrepancy occurs due to the unpredictable and dynamic nature of its environmen
Externí odkaz:
https://doaj.org/article/4260ae2e9872447fb9f6437b1e644691
Autor:
Chun‐Yao Lee, Edu Daryl C. Maceren
Publikováno v:
IET Electric Power Applications, Vol 18, Iss 3, Pp 297-311 (2024)
Abstract Intelligent motor fault diagnosis in industrial applications requires identifying key characteristics to differentiate various fault types effectively. Solely relying on statistical features cannot guarantee high classification accuracy, whi
Externí odkaz:
https://doaj.org/article/229ac409d8524390b07469c1ae12a939
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
Autor:
Chun-Yao Lee, Guang-Lin Zhuo
Publikováno v:
IEEE Access, Vol 11, Pp 26953-26963 (2023)
To solve the problem of the low signal-to-noise ratio and fault features can only be extracted from a single scale of traditional convolutional neural network (CNN) in vibration-based bearing fault diagnosis, this paper proposes a new multi-scale res
Externí odkaz:
https://doaj.org/article/f3b407289f7d4747a006343f84364dc8
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, Wen-Cheng Lin
Publikováno v:
IEEE Access, Vol 9, Pp 56330-56343 (2021)
This paper proposes a novel fault classification method with application to induction motors, which is based on integrating and combining with receiver operating characteristic (ROC) curve and t-distribution stochastic neighbor embedding (t-SNE). Acc
Externí odkaz:
https://doaj.org/article/9f1379542140404b9b719477f2511166