A Novel Fault Detection and Identification Framework for Rotating Machinery Using Residual Current Spectrum
A novel framework of model-based fault detection and identification (MFDI) for induction motor (IM)-driven rotating machinery (RM) is proposed in this study. A data-driven subspace identification (SID) algorithm is employed to obtain the IM state-space model from the voltage and current signals in a...
Main Authors: | Widagdo Purbowaskito, Chen-Yang Lan, Kenny Fuh |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-08-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/21/17/5865 |
Similar Items
-
Robust Data-Driven Design for Fault Diagnosis of Industrial Drives
by: Umair Rashid, et al.
Published: (2022-11-01) -
Fault Detection of Induction Motors with Combined Modeling- and Machine-Learning-Based Framework
by: Moritz Benninger, et al.
Published: (2023-04-01) -
Fault detection in an induction motor drive based on the behavior of the power electronics devices
by: Jesús Aguayo Alquicira, et al.
Published: (2013-09-01) -
Fault Identification, Classification, and Location on Transmission Lines Using Combined Machine Learning Methods
by: Nguyen Nhan Bon, et al.
Published: (2022-02-01) -
Impact of Induction Motor Faults on the Basic Parameters' Values
by: Raya A. K. Aswad, et al.
Published: (2020-12-01)