Broken rotor bar fault diagnosis techniques based on motor current signature analysis for induction motor—A review
The most often used motor in commercial drives is the induction motor. While the induction
motor is operating, electrical, thermal, mechanical, magnetic, and environmental stresses …
motor is operating, electrical, thermal, mechanical, magnetic, and environmental stresses …
Understanding and learning discriminant features based on multiattention 1DCNN for wheelset bearing fault diagnosis
Recently, deep-learning-based fault diagnosis methods have been widely studied for rolling
bearings. However, these neural networks are lack of interpretability for fault diagnosis …
bearings. However, these neural networks are lack of interpretability for fault diagnosis …
Broken bar fault detection and diagnosis techniques for induction motors and drives: State of the art
Motors are the higher energy-conversion devices that consume around 40% of the global
electrical generated energy. Induction motors are the most popular motor type due to their …
electrical generated energy. Induction motors are the most popular motor type due to their …
Multibranch and multiscale CNN for fault diagnosis of wheelset bearings under strong noise and variable load condition
The critical issue for fault diagnosis of wheel-set bearings in high-speed trains is to extract
fault features from vibration signals. To handle high complexity, strong coupling, and low …
fault features from vibration signals. To handle high complexity, strong coupling, and low …
Motor fault detection and feature extraction using RNN-based variational autoencoder
Y Huang, CH Chen, CJ Huang - IEEE access, 2019 - ieeexplore.ieee.org
In most of the fault detection methods, the time domain signals collected from the
mechanical equipment usually need to be transformed into frequency domain or other high …
mechanical equipment usually need to be transformed into frequency domain or other high …
A deep learning approach for fault diagnosis of induction motors in manufacturing
Extracting features from original signals is a key procedure for traditional fault diagnosis of
induction motors, as it directly influences the performance of fault recognition. However, high …
induction motors, as it directly influences the performance of fault recognition. However, high …
Feature knowledge based fault detection of induction motors through the analysis of stator current data
T Yang, H Pen, Z Wang… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
The fault detection of electrical or mechanical anomalies in induction motors has been a
challenging problem for researchers over decades to ensure the safety and economic …
challenging problem for researchers over decades to ensure the safety and economic …
An efficient Hilbert–Huang transform-based bearing faults detection in induction machines
This paper focuses on rolling elements bearing fault detection in induction machines based
on stator currents analysis. Specifically, it proposes to process the stator currents using the …
on stator currents analysis. Specifically, it proposes to process the stator currents using the …
A method for detecting half-broken rotor bar in lightly loaded induction motors using current
This paper presents an effective method of motor current signature analysis for detecting half-
as well as full broken single rotor bar fault of a squirrel-cage induction machine under …
as well as full broken single rotor bar fault of a squirrel-cage induction machine under …
Induction motor broken rotor bar fault location detection through envelope analysis of start-up current using Hilbert transform
Robustness, low running cost and reduced maintenance lead Induction Motors (IMs) to
pioneerly penetrate the industrial drive system fields. Broken rotor bars (BRBs) can be …
pioneerly penetrate the industrial drive system fields. Broken rotor bars (BRBs) can be …