Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference

By V. Tran, Bo-Suk Yang, Myung-Suck Oh, A. Tan. Expert systems with applications, 2009

460 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/889f03816bcce6dd53b7ed7920cc0c01fa086cc7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xuning Feng
2. Dongsheng Ren
3. Xiangming He
4. Minggao Ouyang
5. Li Wang
6. Siqi Zheng
7. Yu Wang
8. Xiaosong Hu

Citations in the corpus

• Time Sequence Map for Interpreting the Thermal Runaway Mechanism of Lithium-Ion Batteries With LiNixCoyMnzO2 Cathode (2018, background): “Lithium-ion battery, given its high energy density and extended life time, is a promising choice for the EESS (Feng et al., 2012; Ji et al., 2015; Kong et al., 2018).”
• DCNN-Based Multi-Signal Induction Motor Fault Diagnosis (2020, unknown): “Successful applications including Support Vector Machine (SVM) [17], Bayesian networks (BN), arti-ficial neural networks (ANNs) [18], fuzzy inference [19], and deep learning (DL), have been seen in various fault diagnosis scenario.”