Fault Diagnosis in Industrial Induction Machines Through Discrete Wavelet Transform

By A. Bouzida, O. Touhami, R. Ibtiouen, Adel Belouchrani, M. Fadel, Abderrezak Rezzoug. IEEE transactions on industrial electronics (1982. Print), 2011

324 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/d8d64f1e5262b77fddf81bb151d56240297011fc

How researchers see this paper

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

In Control and Systems Engineering

1. Shen Yin
2. Huijun Gao
3. Okyay Kaynak
4. Zhiwei Gao
5. Elias G. Strangas
6. Rodney K. Singleton
7. Selin Aviyente
8. Xianwei Li

In Mechanics of Materials

1. Huijun Gao
2. Elias G. Strangas
3. Rodney K. Singleton
4. Selin Aviyente

Citations in the corpus

• Data-Based Techniques Focused on Modern Industry: An Overview (2015, methodology): “Considering the possible external interference and errors of measurement, the processing of data for industrial power systems requires appropriate disturbance classification [83] and fault diagnosis [84] techniques.”
• From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis (2013, methodology): “Typical signal analysis techniques include FFT, spectral estimation, wavelet transform [5], and sequence analysis [71].”
• Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings (2015, background): “Effective diagnostic [2]– [11] and prognostic tools are essential for timely fault detection and remaining useful life (RUL) prediction [12]–[17].”